Causal inference, probability theory, and graphical insights.

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Baker, Stuart G

2013-11-10

Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.

Causal learning and inference as a rational process: the new synthesis.

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Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Cortical hierarchies perform Bayesian causal inference in multisensory perception.

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Tim Rohe

2015-02-01

Full Text Available To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI, and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation. At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion. Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.

Statistical causal inferences and their applications in public health research

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Wu, Pan; Chen, Ding-Geng

2016-01-01

This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.

Causal inference in public health.

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Glass, Thomas A; Goodman, Steven N; Hernán, Miguel A; Samet, Jonathan M

2013-01-01

Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action's consequences rather than the less precise notion of a risk factor's causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world.

Inferring causality from noisy time series data

DEFF Research Database (Denmark)

Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian

2016-01-01

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...

Non-Gaussian Methods for Causal Structure Learning.

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Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

Causal inference of asynchronous audiovisual speech

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John F Magnotti

2013-11-01

Full Text Available During speech perception, humans integrate auditory information from the voice with visual information from the face. This multisensory integration increases perceptual precision, but only if the two cues come from the same talker; this requirement has been largely ignored by current models of speech perception. We describe a generative model of multisensory speech perception that includes this critical step of determining the likelihood that the voice and face information have a common cause. A key feature of the model is that it is based on a principled analysis of how an observer should solve this causal inference problem using the asynchrony between two cues and the reliability of the cues. This allows the model to make predictions abut the behavior of subjects performing a synchrony judgment task, predictive power that does not exist in other approaches, such as post hoc fitting of Gaussian curves to behavioral data. We tested the model predictions against the performance of 37 subjects performing a synchrony judgment task viewing audiovisual speech under a variety of manipulations, including varying asynchronies, intelligibility, and visual cue reliability. The causal inference model outperformed the Gaussian model across two experiments, providing a better fit to the behavioral data with fewer parameters. Because the causal inference model is derived from a principled understanding of the task, model parameters are directly interpretable in terms of stimulus and subject properties.

Campbell's and Rubin's Perspectives on Causal Inference

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West, Stephen G.; Thoemmes, Felix

2010-01-01

Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…

Detecting dynamic causal inference in nonlinear two-phase fracture flow

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Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

What Do Patients Think about the Cause of Their Mental Disorder? A Qualitative and Quantitative Analysis of Causal Beliefs of Mental Disorder in Inpatients in Psychosomatic Rehabilitation.

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Julia Luise Magaard

Full Text Available Patients' causal beliefs about their mental disorders are important for treatment because they affect illness-related behaviours. However, there are few studies exploring patients' causal beliefs about their mental disorder.(a To qualitatively explore patients' causal beliefs of their mental disorder, (b to explore frequencies of patients stating causal beliefs, and (c to investigate differences of causal beliefs according to patients' primary diagnoses.Inpatients in psychosomatic rehabilitation were asked an open-ended question about their three most important causal beliefs about their mental illness. Answers were obtained from 678 patients, with primary diagnoses of depression (N = 341, adjustment disorder (N = 75, reaction to severe stress (N = 57 and anxiety disorders (N = 40. Two researchers developed a category system inductively and categorised the reported causal beliefs. Qualitative analysis has been supplemented by logistic regression analyses.The causal beliefs were organized into twelve content-related categories. Causal beliefs referring to "problems at work" (47% and "problems in social environment" (46% were most frequently mentioned by patients with mental disorders. 35% of patients indicate causal beliefs related to "self/internal states". Patients with depression and patients with anxiety disorders stated similar causal beliefs, whereas patients with reactions to severe stress and adjustment disorders stated different causal beliefs in comparison to patients with depression.There was no opportunity for further exploration, because we analysed written documents.These results add a detailed insight to mentally ill patients' causal beliefs to illness perception literature. Additionally, evidence about differences in frequencies of causal beliefs between different illness groups complement previous findings. For future research it is important to clarify the relation between patients' causal beliefs and the chosen treatment.

What Do Patients Think about the Cause of Their Mental Disorder? A Qualitative and Quantitative Analysis of Causal Beliefs of Mental Disorder in Inpatients in Psychosomatic Rehabilitation.

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Magaard, Julia Luise; Schulz, Holger; Brütt, Anna Levke

2017-01-01

Patients' causal beliefs about their mental disorders are important for treatment because they affect illness-related behaviours. However, there are few studies exploring patients' causal beliefs about their mental disorder. (a) To qualitatively explore patients' causal beliefs of their mental disorder, (b) to explore frequencies of patients stating causal beliefs, and (c) to investigate differences of causal beliefs according to patients' primary diagnoses. Inpatients in psychosomatic rehabilitation were asked an open-ended question about their three most important causal beliefs about their mental illness. Answers were obtained from 678 patients, with primary diagnoses of depression (N = 341), adjustment disorder (N = 75), reaction to severe stress (N = 57) and anxiety disorders (N = 40). Two researchers developed a category system inductively and categorised the reported causal beliefs. Qualitative analysis has been supplemented by logistic regression analyses. The causal beliefs were organized into twelve content-related categories. Causal beliefs referring to "problems at work" (47%) and "problems in social environment" (46%) were most frequently mentioned by patients with mental disorders. 35% of patients indicate causal beliefs related to "self/internal states". Patients with depression and patients with anxiety disorders stated similar causal beliefs, whereas patients with reactions to severe stress and adjustment disorders stated different causal beliefs in comparison to patients with depression. There was no opportunity for further exploration, because we analysed written documents. These results add a detailed insight to mentally ill patients' causal beliefs to illness perception literature. Additionally, evidence about differences in frequencies of causal beliefs between different illness groups complement previous findings. For future research it is important to clarify the relation between patients' causal beliefs and the chosen treatment.

Drawing causal inferences using propensity scores: a practical guide for community psychologists.

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Lanza, Stephanie T; Moore, Julia E; Butera, Nicole M

2013-12-01

Confounding present in observational data impede community psychologists' ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods-weighting, matching, and subclassification-is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.

Non-Bayesian Inference: Causal Structure Trumps Correlation

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Bes, Benedicte; Sloman, Steven; Lucas, Christopher G.; Raufaste, Eric

2012-01-01

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more…

A Causal Inference Model Explains Perception of the McGurk Effect and Other Incongruent Audiovisual Speech.

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John F Magnotti

2017-02-01

Full Text Available Audiovisual speech integration combines information from auditory speech (talker's voice and visual speech (talker's mouth movements to improve perceptual accuracy. However, if the auditory and visual speech emanate from different talkers, integration decreases accuracy. Therefore, a key step in audiovisual speech perception is deciding whether auditory and visual speech have the same source, a process known as causal inference. A well-known illusion, the McGurk Effect, consists of incongruent audiovisual syllables, such as auditory "ba" + visual "ga" (AbaVga, that are integrated to produce a fused percept ("da". This illusion raises two fundamental questions: first, given the incongruence between the auditory and visual syllables in the McGurk stimulus, why are they integrated; and second, why does the McGurk effect not occur for other, very similar syllables (e.g., AgaVba. We describe a simplified model of causal inference in multisensory speech perception (CIMS that predicts the perception of arbitrary combinations of auditory and visual speech. We applied this model to behavioral data collected from 60 subjects perceiving both McGurk and non-McGurk incongruent speech stimuli. The CIMS model successfully predicted both the audiovisual integration observed for McGurk stimuli and the lack of integration observed for non-McGurk stimuli. An identical model without causal inference failed to accurately predict perception for either form of incongruent speech. The CIMS model uses causal inference to provide a computational framework for studying how the brain performs one of its most important tasks, integrating auditory and visual speech cues to allow us to communicate with others.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

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Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

CauseMap: fast inference of causality from complex time series.

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Maher, M Cyrus; Hernandez, Ryan D

2015-01-01

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a

CauseMap: fast inference of causality from complex time series

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M. Cyrus Maher

2015-03-01

Full Text Available Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data.Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM, a method for establishing causality from long time series data (≳25 observations. Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement

Adolescent Victimization and Early-Adult Psychopathology: Approaching Causal Inference Using a Longitudinal Twin Study to Rule Out Noncausal Explanations

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Schaefer, Jonathan D.; Moffitt, Terrie E.; Arseneault, Louise; Danese, Andrea; Fisher, Helen L.; Houts, Renate; Sheridan, Margaret A.; Wertz, Jasmin; Caspi, Avshalom

2017-01-01

Adolescence is the peak age for both victimization and mental disorder onset. Previous research has reported associations between victimization exposure and many psychiatric conditions. However, causality remains controversial. Within the Environmental Risk Longitudinal Twin Study, we tested whether seven types of adolescent victimization increased risk of multiple psychiatric conditions and approached causal inference by systematically ruling out noncausal explanations. Longitudinal within-individual analyses showed that victimization was followed by increased mental health problems over a childhood baseline of emotional/behavioral problems. Discordant-twin analyses showed that victimization increased risk of mental health problems independent of family background and genetic risk. Both childhood and adolescent victimization made unique contributions to risk. Victimization predicted heightened generalized liability (the “p factor”) to multiple psychiatric spectra, including internalizing, externalizing, and thought disorders. Results recommend violence reduction and identification and treatment of adolescent victims to reduce psychiatric burden. PMID:29805917

Causal Beliefs and Effects upon Mental Illness Identification Among Chinese Immigrant Relatives of Individuals with Psychosis.

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Yang, Lawrence H; Wonpat-Borja, Ahtoy J

2012-08-01

Identifying factors that facilitate treatment for psychotic disorders among Chinese-immigrants is crucial due to delayed treatment use. Identifying causal beliefs held by relatives that might predict identification of 'mental illness' as opposed to other 'indigenous labels' may promote more effective mental health service use. We examine what effects beliefs of 'physical causes' and other non-biomedical causal beliefs ('general social causes', and 'indigenous Chinese beliefs' or culture-specific epistemologies of illness) might have on mental illness identification. Forty-nine relatives of Chinese-immigrant consumers with psychosis were sampled. Higher endorsement of 'physical causes' was associated with mental illness labeling. However among the non-biomedical causal beliefs, 'general social causes' demonstrated no relationship with mental illness identification, while endorsement of 'indigenous Chinese beliefs' showed a negative relationship. Effective treatment- and community-based psychoeducation, in addition to emphasizing biomedical models, might integrate indigenous Chinese epistemologies of illness to facilitate rapid identification of psychotic disorders and promote treatment use.

Inferring the conservative causal core of gene regulatory networks

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Emmert-Streib Frank

2010-09-01

Full Text Available Abstract Background Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. Results In this paper, we introduce a novel gene regulatory network inference (GRNI algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. Conclusions For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Inferring the conservative causal core of gene regulatory networks.

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Altay, Gökmen; Emmert-Streib, Frank

2010-09-28

Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review.

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Haber, Noah; Smith, Emily R; Moscoe, Ellen; Andrews, Kathryn; Audy, Robin; Bell, Winnie; Brennan, Alana T; Breskin, Alexander; Kane, Jeremy C; Karra, Mahesh; McClure, Elizabeth S; Suarez, Elizabeth A

2018-01-01

The pathway from evidence generation to consumption contains many steps which can lead to overstatement or misinformation. The proliferation of internet-based health news may encourage selection of media and academic research articles that overstate strength of causal inference. We investigated the state of causal inference in health research as it appears at the end of the pathway, at the point of social media consumption. We screened the NewsWhip Insights database for the most shared media articles on Facebook and Twitter reporting about peer-reviewed academic studies associating an exposure with a health outcome in 2015, extracting the 50 most-shared academic articles and media articles covering them. We designed and utilized a review tool to systematically assess and summarize studies' strength of causal inference, including generalizability, potential confounders, and methods used. These were then compared with the strength of causal language used to describe results in both academic and media articles. Two randomly assigned independent reviewers and one arbitrating reviewer from a pool of 21 reviewers assessed each article. We accepted the most shared 64 media articles pertaining to 50 academic articles for review, representing 68% of Facebook and 45% of Twitter shares in 2015. Thirty-four percent of academic studies and 48% of media articles used language that reviewers considered too strong for their strength of causal inference. Seventy percent of academic studies were considered low or very low strength of inference, with only 6% considered high or very high strength of causal inference. The most severe issues with academic studies' causal inference were reported to be omitted confounding variables and generalizability. Fifty-eight percent of media articles were found to have inaccurately reported the question, results, intervention, or population of the academic study. We find a large disparity between the strength of language as presented to the

Causal inference with missing exposure information: Methods and applications to an obstetric study.

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Zhang, Zhiwei; Liu, Wei; Zhang, Bo; Tang, Li; Zhang, Jun

2016-10-01

Causal inference in observational studies is frequently challenged by the occurrence of missing data, in addition to confounding. Motivated by the Consortium on Safe Labor, a large observational study of obstetric labor practice and birth outcomes, this article focuses on the problem of missing exposure information in a causal analysis of observational data. This problem can be approached from different angles (i.e. missing covariates and causal inference), and useful methods can be obtained by drawing upon the available techniques and insights in both areas. In this article, we describe and compare a collection of methods based on different modeling assumptions, under standard assumptions for missing data (i.e. missing-at-random and positivity) and for causal inference with complete data (i.e. no unmeasured confounding and another positivity assumption). These methods involve three models: one for treatment assignment, one for the dependence of outcome on treatment and covariates, and one for the missing data mechanism. In general, consistent estimation of causal quantities requires correct specification of at least two of the three models, although there may be some flexibility as to which two models need to be correct. Such flexibility is afforded by doubly robust estimators adapted from the missing covariates literature and the literature on causal inference with complete data, and by a newly developed triply robust estimator that is consistent if any two of the three models are correct. The methods are applied to the Consortium on Safe Labor data and compared in a simulation study mimicking the Consortium on Safe Labor. © The Author(s) 2013.

Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments.

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Teramoto, Reiji; Saito, Chiaki; Funahashi, Shin-ichi

2014-06-30

Knockdown or overexpression of genes is widely used to identify genes that play important roles in many aspects of cellular functions and phenotypes. Because next-generation sequencing generates high-throughput data that allow us to detect genes, it is important to identify genes that drive functional and phenotypic changes of cells. However, conventional methods rely heavily on the assumption of normality and they often give incorrect results when the assumption is not true. To relax the Gaussian assumption in causal inference, we introduce the non-paranormal method to test conditional independence in the PC-algorithm. Then, we present the non-paranormal intervention-calculus when the directed acyclic graph (DAG) is absent (NPN-IDA), which incorporates the cumulative nature of effects through a cascaded pathway via causal inference for ranking causal genes against a phenotype with the non-paranormal method for estimating DAGs. We demonstrate that causal inference with the non-paranormal method significantly improves the performance in estimating DAGs on synthetic data in comparison with the original PC-algorithm. Moreover, we show that NPN-IDA outperforms the conventional methods in exploring regulators of the flowering time in Arabidopsis thaliana and regulators that control the browning of white adipocytes in mice. Our results show that performance improvement in estimating DAGs contributes to an accurate estimation of causal effects. Although the simplest alternative procedure was used, our proposed method enables us to design efficient intervention experiments and can be applied to a wide range of research purposes, including drug discovery, because of its generality.

Autistic Traits Affect P300 Response to Unexpected Events, regardless of Mental State Inferences

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Mitsuhiko Ishikawa

2017-01-01

Full Text Available Limited use of contextual information has been suggested as a way of understanding cognition in people with autism spectrum disorder (ASD. However, it has also been argued that individuals with ASD may have difficulties inferring others’ mental states. Here, we examined how individuals with different levels of autistic traits respond to contextual deviations by measuring event-related potentials that reflect context usage. The Autism Spectrum Quotient (AQ was used to quantify autistic-like traits in 28 university students, and 19 participants were defined as Low or High AQ groups. To additionally examine inferences about mental state, two belief conditions (with or without false belief were included. Participants read short stories in which the final sentence included either an expected or an unexpected word and rated the word’s degree of deviation from expectation. P300 waveform analysis revealed that unexpected words were associated with larger P300 waveforms for the Low AQ group, but smaller P300 responses in the High AQ group. Additionally, AQ social skill subscores were positively correlated with evaluation times in the Unexpected condition, whether a character’s belief was false or not. This suggests that autistic traits can affect responses to unexpected events, possibly because of decreased availability of context information.

Causal Beliefs and Effects upon Mental Illness Identification Among Chinese Immigrant Relatives of Individuals with Psychosis

OpenAIRE

Yang, Lawrence H.; Wonpat-Borja, Ahtoy J.

2011-01-01

Identifying factors that facilitate treatment for psychotic disorders among Chinese-immigrants is crucial due to delayed treatment use. Identifying causal beliefs held by relatives that might predict identification of ‘mental illness’ as opposed to other ‘indigenous labels’ may promote more effective mental health service use. We examine what effects beliefs of ‘physical causes’ and other non-biomedical causal beliefs (‘general social causes’, and ‘indigenous Chinese beliefs’ or culture-speci...

Causal Inference in the Perception of Verticality.

Science.gov (United States)

de Winkel, Ksander N; Katliar, Mikhail; Diers, Daniel; Bülthoff, Heinrich H

2018-04-03

The perceptual upright is thought to be constructed by the central nervous system (CNS) as a vector sum; by combining estimates on the upright provided by the visual system and the body's inertial sensors with prior knowledge that upright is usually above the head. Recent findings furthermore show that the weighting of the respective sensory signals is proportional to their reliability, consistent with a Bayesian interpretation of a vector sum (Forced Fusion, FF). However, violations of FF have also been reported, suggesting that the CNS may rely on a single sensory system (Cue Capture, CC), or choose to process sensory signals based on inferred signal causality (Causal Inference, CI). We developed a novel alternative-reality system to manipulate visual and physical tilt independently. We tasked participants (n = 36) to indicate the perceived upright for various (in-)congruent combinations of visual-inertial stimuli, and compared models based on their agreement with the data. The results favor the CI model over FF, although this effect became unambiguous only for large discrepancies (±60°). We conclude that the notion of a vector sum does not provide a comprehensive explanation of the perception of the upright, and that CI offers a better alternative.

Non-Causal Computation

Directory of Open Access Journals (Sweden)

Ämin Baumeler

2017-07-01

Full Text Available Computation models such as circuits describe sequences of computation steps that are carried out one after the other. In other words, algorithm design is traditionally subject to the restriction imposed by a fixed causal order. We address a novel computing paradigm beyond quantum computing, replacing this assumption by mere logical consistency: We study non-causal circuits, where a fixed time structure within a gate is locally assumed whilst the global causal structure between the gates is dropped. We present examples of logically consistent non-causal circuits outperforming all causal ones; they imply that suppressing loops entirely is more restrictive than just avoiding the contradictions they can give rise to. That fact is already known for correlations as well as for communication, and we here extend it to computation.

An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach

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Ibrahim Delibalta

2017-01-01

Full Text Available We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios.

Entanglement, non-Markovianity, and causal non-separability

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Milz, Simon; Pollock, Felix A.; Le, Thao P.; Chiribella, Giulio; Modi, Kavan

2018-03-01

Quantum mechanics, in principle, allows for processes with indefinite causal order. However, most of these causal anomalies have not yet been detected experimentally. We show that every such process can be simulated experimentally by means of non-Markovian dynamics with a measurement on additional degrees of freedom. In detail, we provide an explicit construction to implement arbitrary a causal processes. Furthermore, we give necessary and sufficient conditions for open system dynamics with measurement to yield processes that respect causality locally, and find that tripartite entanglement and nonlocal unitary transformations are crucial requirements for the simulation of causally indefinite processes. These results show a direct connection between three counter-intuitive concepts: entanglement, non-Markovianity, and causal non-separability.

Meaningful mediation analysis : Plausible causal inference and informative communication

NARCIS (Netherlands)

Pieters, Rik

2017-01-01

Statistical mediation analysis has become the technique of choice in consumer research to make causal inferences about the influence of a treatment on an outcome via one or more mediators. This tutorial aims to strengthen two weak links that impede statistical mediation analysis from reaching its

Causal Reasoning with Mental Models

Science.gov (United States)

2014-08-08

The initial rubric is equivalent to an exclusive disjunction between the two causal assertions. It 488 yields the following two mental models: 489...are 575 important, whereas the functions of artifacts are important (Ahn, 1998). A genetic code is 576 accordingly more critical to being a goat than

Research designs and making causal inferences from health care studies.

Science.gov (United States)

Flannelly, Kevin J; Jankowski, Katherine R B

2014-01-01

This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.

Learning What Works in Sensory Disabilities: Establishing Causal Inference

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Cooney, John B.; Young, John, III; Luckner, John L.; Ferrell, Kay Alicyn

2015-01-01

This article is intended to assist teachers and researchers in designing studies that examine the efficacy of a particular intervention or strategy with students with sensory disabilities. Ten research designs that can establish causal inference (the ability to attribute any effects to the intervention) with and without randomization are discussed.

Do biogenetic causal beliefs reduce mental illness stigma in people with mental illness and in mental health professionals? A systematic review.

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Larkings, Josephine S; Brown, Patricia M

2018-06-01

Viewing mental illness as an 'illness like any other' and promoting biogenetic causes have been explored as a stigma-reduction strategy. The relationship between causal beliefs and mental illness stigma has been researched extensively in the general public, but has gained less attention in more clinically-relevant populations (i.e. people with mental illness and mental health professionals). A systematic review examining whether endorsing biogenetic causes decreases mental illness stigma in people with mental illness and mental health professionals was undertaken using the preferred reporting items for systematic reviews and meta-analyses guidelines. Multiple databases were searched, and studies that explored the relationship between biogenetic causal beliefs and mental illness stigma in people with mental illness or mental health professionals were considered. Studies were included if they focussed on depression, schizophrenia, or mental illness in general, were in English, and had adult participants. The search identified 11 journal articles reporting on 15 studies, which were included in this review. Of these, only two provided evidence that endorsing biogenetic causes was associated with less mental illness stigma in people with mental illness or mental health professionals. The majority of studies in the present review (n = 10) found that biogenetic causal beliefs were associated with increased stigma or negative attitudes towards mental illness. The present review highlights the lack of research exploring the impacts of endorsing biogenetic causes in people with mental illness and mental health professionals. Clinical implications associated with these results are discussed, and suggestions are made for further research that examines the relationship between causal beliefs and treatment variables. © 2017 Australian College of Mental Health Nurses Inc.

A Bayesian nonparametric approach to causal inference on quantiles.

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Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

2018-02-25

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

Inference of Boundaries in Causal Sets

OpenAIRE

Cunningham, William

2017-01-01

We investigate the extrinsic geometry of causal sets in $(1+1)$-dimensional Minkowski spacetime. The properties of boundaries in an embedding space can be used not only to measure observables, but also to supplement the discrete action in the partition function via discretized Gibbons-Hawking-York boundary terms. We define several ways to represent a causal set using overlapping subsets, which then allows us to distinguish between null and non-null bounding hypersurfaces in an embedding space...

Explanation in causal inference methods for mediation and interaction

CERN Document Server

VanderWeele, Tyler

2015-01-01

A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.

Mental Development of Children with Non-epileptic Paroxysmal States in Medical History

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Turovskaya N.G.,

2015-10-01

Full Text Available The author studied mental functions disorders in children with a history of paroxysmal states of various etiologies and compared mental development disorder patterns in patients with epileptic and non-epileptic paroxysms. Study sample were 107 children, aged 6 to 10 years. The study used experimental psychological and neuropsychological techniques. According to the empirical study results, non-epileptic paroxysms unlike epileptic much less combined with a number of mental functions disorders and intelligence in general. However, non-epileptic paroxysmal states as well as epileptic seizure associated with increasing activity exhaustion and abnormal function of the motor analyzer (dynamic and kinesthetic dyspraxia. Visual memory disorders and modal-nonspecific memory disorders have more pronounced importance in the mental ontogenesis structure in children with convulsive paroxysms compared to children with cerebral pathology without paroxysms history

Kant on causal laws and powers.

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Henschen, Tobias

2014-12-01

The aim of the paper is threefold. Its first aim is to defend Eric Watkins's claim that for Kant, a cause is not an event but a causal power: a power that is borne by a substance, and that, when active, brings about its effect, i.e. a change of the states of another substance, by generating a continuous flow of intermediate states of that substance. The second aim of the paper is to argue against Watkins that the Kantian concept of causal power is not the pre-critical concept of real ground but the category of causality, and that Kant holds with Hume that causal laws cannot be inferred non-inductively (that he accordingly has no intention to show in the Second analogy or elsewhere that events fall under causal laws). The third aim of the paper is to compare the Kantian position on causality with central tenets of contemporary powers ontology: it argues that unlike the variants endorsed by contemporary powers theorists, the Kantian variants of these tenets are resistant to objections that neo-Humeans raise to these tenets.

Bayesian nonparametric generative models for causal inference with missing at random covariates.

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Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J

2018-03-26

We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients. © 2018, The International Biometric Society.

State and non-state mental health service collaboration in a South African district: a mixed methods study.

Science.gov (United States)

Janse van Rensburg, André; Petersen, Inge; Wouters, Edwin; Engelbrecht, Michelle; Kigozi, Gladys; Fourie, Pieter; van Rensburg, Dingie; Bracke, Piet

2018-05-01

The Life Esidimeni tragedy in South Africa showed that, despite significant global gains in recognizing the salience of integrated public mental health care during the past decade, crucial gaps remain. State and non-state mental health service collaboration is a recognized strategy to increase access to care and optimal use of community resources, but little evidence exist about how it unfolds in low- to middle-income countries. South Africa's Mental Health Policy Framework and Strategic Plan 2013-20 (MHPF) underlines the importance of collaborative public mental health care, though it is unclear how and to what extent this happens. The aim of the study was to explore the extent and nature of state and non-state mental health service collaboration in the Mangaung Metropolitan District, Free State, South Africa. The research involved an equal status, sequential mixed methods design, comprised of social network analysis (SNA) and semi-structured interviews. SNA-structured interviews were conducted with collaborating state and non-state mental health service providers. Semi-structured interviews were conducted with collaborating partners and key stake holders. Descriptive network analyses of the SNA data were performed with Gephi, and thematic analysis of the semi-structured interview data were performed in NVivo. SNA results suggested a fragmented, hospital centric network, with low average density and clustering, and high authority and influence of a specialist psychiatric hospital. Several different types of collaborative interactions emerged, of which housing and treatment adherence a key point of collaboration. Proportional interactions between state and non-state services were low. Qualitative data expanded on these findings, highlighting the range of available mental health services, and pointed to power dynamics as an important consideration in the mental health service network. The fostering of a well-integrated system of care as proposed in the MHPF requires

Inference of boundaries in causal sets

Science.gov (United States)

Cunningham, William J.

2018-05-01

We investigate the extrinsic geometry of causal sets in (1+1) -dimensional Minkowski spacetime. The properties of boundaries in an embedding space can be used not only to measure observables, but also to supplement the discrete action in the partition function via discretized Gibbons–Hawking–York boundary terms. We define several ways to represent a causal set using overlapping subsets, which then allows us to distinguish between null and non-null bounding hypersurfaces in an embedding space. We discuss algorithms to differentiate between different types of regions, consider when these distinctions are possible, and then apply the algorithms to several spacetime regions. Numerical results indicate the volumes of timelike boundaries can be measured to within 0.5% accuracy for flat boundaries and within 10% accuracy for highly curved boundaries for medium-sized causal sets with N  =  214 spacetime elements.

A note on mental content in the Causal Theory

African Journals Online (AJOL)

A note on mental content in the Causal Theory. JP Smit. Department of Philosophy, Stellenbosch University, Private Bag X1, 7600 Matieland, South Africa. E-mail: jps@sun.ac.za. Kripke's causal theory requires that downstream users of a name must have the intention to use the name in the same way that upstream users ...

Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions

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Felix Agakov

2011-12-01

Full Text Available We describe a unified computational framework for learning causal dependencies between genotypes, biomarkers, and phenotypic outcomes from large-scale data. In contrast to previous studies, our framework allows for noisy measurements, hidden confounders, missing data, and pleiotropic effects of genotypes on outcomes. The method exploits the use of genotypes as “instrumental variables” to infer causal associations between phenotypic biomarkers and outcomes, without requiring the assumption that genotypic effects are mediated only through the observed biomarkers. The framework builds on sparse linear methods developed in statistics and machine learning and modified here for inferring structures of richer networks with latent variables. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs detected in genetic linkage studies. To demonstrate our method, we examined effects of gene transcript levels in the liver on plasma HDL cholesterol levels in a sample of 260 mice from a heterogeneous stock.

Reward Behavior by Male and Female Leaders: A Causal Inference Analysis.

Science.gov (United States)

Szilagyi, Andrew D.

1980-01-01

Investigated causal inferences between leader reward behavior and subordinate goal attainment, absenteeism, and work satisfaction. Results revealed that no significant differences were attributed to sex and that the leader reward behavior and subordinate attitudes and behavior were independent of the effects of sex of supervisor or subordinate.…

Causal strength induction from time series data.

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Soo, Kevin W; Rottman, Benjamin M

2018-04-01

One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away.

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Rottman, Benjamin M; Hastie, Reid

2016-06-01

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X1→Y→X2, common cause structures X1←Y→X2, and common effect structures X1→Y←X2, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative "explaining away" pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations. Copyright © 2016 Elsevier Inc. All rights reserved.

Causality and skies: is non-refocussing necessary?

International Nuclear Information System (INIS)

Bautista, A; Ibort, A; Lafuente, J

2015-01-01

The causal structure of a strongly causal, null pseudo-convex, space-time M is completely characterized in terms of a partial order on its space of skies defined by means of a class of non-negative Legendrian isotopies called sky isotopies. It is also shown that such partial order is determined by the class of future causal celestial curves, that is, curves in the space of light rays which are tangent to skies and such that they determine non-negative sky isotopies. It will also be proved that the space of skies Σ equipped with Low’s (or reconstructive) topology is homeomorphic and diffeomorphic to M under the only additional assumption that M separates skies, that is, that different events determine different skies. The sky-separating property of M is sharp and the previous result provides an answer to the question about the class of space-times whose causal structure, topological and differentiable structure can be reconstructed from their spaces of light rays and skies. These results can be understood as a Malament–Hawking-like theorem stated in terms of the partial order defined on the space of skies. (paper)

Energy consumption and economic growth: Parametric and non-parametric causality testing for the case of Greece

International Nuclear Information System (INIS)

Dergiades, Theologos; Martinopoulos, Georgios; Tsoulfidis, Lefteris

2013-01-01

The objective of this paper is to contribute towards the understanding of the linear and non-linear causal linkages between energy consumption and economic activity, making use of annual time series data of Greece for the period 1960–2008. Two are the salient features of our study: first, the total energy consumption has been adjusted for qualitative differences among its constituent components through the thermodynamics of energy conversion. In doing so, we rule out the possibility of a misleading inference with respect to causality due to aggregation bias. Second, the investigation of the causal linkage between economic growth and the adjusted for quality total energy consumption is conducted within a non-linear context. Our empirical results reveal significant unidirectional both linear and non-linear causal linkages running from total useful energy to economic growth. These findings may provide valuable information for the contemplation of more effective energy policies with respect to both the consumption of energy and environmental protection. - Highlights: ► The energy consumption and economic growth nexus is investigated for Greece. ► A quality-adjusted energy series is used in our analysis. ► The causality testing procedure is conducted within a non-linear context. ► A causality running from energy consumption to economic growth is verified

The influence of cognitive ability and instructional set on causal conditional inference.

Science.gov (United States)

Evans, Jonathan St B T; Handley, Simon J; Neilens, Helen; Over, David

2010-05-01

We report a large study in which participants are invited to draw inferences from causal conditional sentences with varying degrees of believability. General intelligence was measured, and participants were split into groups of high and low ability. Under strict deductive-reasoning instructions, it was observed that higher ability participants were significantly less influenced by prior belief than were those of lower ability. This effect disappeared, however, when pragmatic reasoning instructions were employed in a separate group. These findings are in accord with dual-process theories of reasoning. We also took detailed measures of beliefs in the conditional sentences used for the reasoning tasks. Statistical modelling showed that it is not belief in the conditional statement per se that is the causal factor, but rather correlates of it. Two different models of belief-based reasoning were found to fit the data according to the kind of instructions and the type of inference under consideration.

Usefulness of near-infrared spectroscopy to detect brain dysfunction in children with autism spectrum disorder when inferring the mental state of others.

Science.gov (United States)

Iwanaga, Ryoichiro; Tanaka, Goro; Nakane, Hideyuki; Honda, Sumihisa; Imamura, Akira; Ozawa, Hiroki

2013-05-01

The purpose of this study was to examine the usefulness of near-infrared spectroscopy (NIRS) for identifying abnormalities in prefrontal brain activity in children with autism spectrum disorders (ASD) as they inferred the mental states of others. The subjects were 16 children with ASD aged between 8 and 14 years and 16 age-matched healthy control children. Oxygenated hemoglobin concentration was measured in the subject's prefrontal brain region on NIRS during tasks expressing a person's mental state (MS task) and expressing an object's characteristics (OC task). There was a significant main effect of group (ASD vs control), with the control group having more activity than the ASD group. But there was no significant main effect of task (MS task vs OC task) or hemisphere (right vs left). Significant interactions of task and group were found, with the control group showing more activity than the ASD group during the MS task relative to the OC task. NIRS showed that there was lower activity in the prefrontal brain area when children with ASD performed MS tasks. Therefore, clinicians might be able to use NIRS and these tasks for conveniently detecting brain dysfunction in children with ASD related to inferring mental states, in the clinical setting. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

Causal Diagrams for Empirical Research

OpenAIRE

Pearl, Judea

1994-01-01

The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifiying causal effects from non-experimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in ter...

State-Space Inference and Learning with Gaussian Processes

OpenAIRE

Turner, R; Deisenroth, MP; Rasmussen, CE

2010-01-01

18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...

Individuals with currently untreated mental illness: causal beliefs and readiness to seek help.

Science.gov (United States)

Stolzenburg, S; Freitag, S; Evans-Lacko, S; Speerforck, S; Schmidt, S; Schomerus, G

2018-01-16

Many people with mental illness do not seek professional help. Beliefs about the causes of their current health problem seem relevant for initiating treatment. Our aim was to find out to what extent the perceived causes of current untreated mental health problems determine whether a person considers herself/himself as having a mental illness, perceives need for professional help and plans to seek help in the near future. In a cross-sectional study, we examined 207 untreated persons with a depressive syndrome, all fulfilling criteria for a current mental illness as confirmed with a structured diagnostic interview (Mini International Neuropsychiatric Interview). The sample was recruited in the community using adverts, flyers and social media. We elicited causal explanations for the present problem, depression literacy, self-identification as having a mental illness, perceived need for professional help, help-seeking intentions, severity of depressive symptoms (Patient Health Questionnaire - Depression), and whether respondents had previously sought mental healthcare. Most participants fulfilled diagnostic criteria for a mood disorder (n = 181, 87.4%) and/or neurotic, stress-related and somatoform disorders (n = 120, 58.0%) according to the ICD-10. N = 94 (45.4%) participants had never received mental health treatment previously. Exploratory factor analysis of a list of 25 different causal explanations resulted in five factors: biomedical causes, person-related causes, childhood trauma, current stress and unhealthy behaviour. Attributing the present problem to biomedical causes, person-related causes, childhood trauma and stress were all associated with stronger self-identification as having a mental illness. In persons who had never received mental health treatment previously, attribution to biomedical causes was related to greater perceived need and stronger help-seeking intentions. In those with treatment experience, lower attribution to person-related causes and

Causal and causally separable processes

Science.gov (United States)

Oreshkov, Ognyan; Giarmatzi, Christina

2016-09-01

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

Causal and causally separable processes

International Nuclear Information System (INIS)

Oreshkov, Ognyan; Giarmatzi, Christina

2016-01-01

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B , either A is in the causal past of B , B is in the causal past of A , or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B , an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A ’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

Causal attribution of mental illness in South-Eastern Nigeria.

Science.gov (United States)

Ikwuka, Ugo; Galbraith, Niall; Nyatanga, Lovemore

2014-05-01

Understanding of mental illness in sub-Saharan Africa has remained under-researched in spite of the high and increasing neuropsychiatric burden of disease in the region. This study investigated the causal beliefs that the Igbo people of south-eastern Nigeria hold about schizophrenia, with a view to establishing the extent to which the population makes psychosocial, biological and supernatural attributions. Multi-stage sampling was used to select participants (N = 200) to which questionnaires were administered. Mean comparison of the three causal models revealed a significant endorsement of supernatural causation. Logistic regressions revealed significant contributions of old age and female gender to supernatural attribution; old age, high education and Catholic religious denomination to psychosocial attributions; and high education to biological attributions. It is hoped that the findings would enlighten, augment literature and enhance mental health care service delivery.

Causal imprinting in causal structure learning.

Science.gov (United States)

Taylor, Eric G; Ahn, Woo-Kyoung

2012-11-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures "causal imprinting." Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. Copyright © 2012 Elsevier Inc. All rights reserved.

Using fiction to assess mental state understanding: a new task for assessing theory of mind in adults.

Directory of Open Access Journals (Sweden)

David Dodell-Feder

Full Text Available Social functioning depends on the ability to attribute and reason about the mental states of others--an ability known as theory of mind (ToM. Research in this field is limited by the use of tasks in which ceiling effects are ubiquitous, rendering them insensitive to individual differences in ToM ability and instances of subtle ToM impairment. Here, we present data from a new ToM task--the Short Story Task (SST--intended to improve upon many aspects of existing ToM measures. More specifically, the SST was designed to: (a assess the full range of individual differences in ToM ability without suffering from ceiling effects; (b incorporate a range of mental states of differing complexity, including epistemic states, affective states, and intentions to be inferred from a first- and second-order level; (c use ToM stimuli representative of real-world social interactions; (d require participants to utilize social context when making mental state inferences; (e exhibit adequate psychometric properties; and (f be quick and easy to administer and score. In the task, participants read a short story and were asked questions that assessed explicit mental state reasoning, spontaneous mental state inference, and comprehension of the non-mental aspects of the story. Responses were scored according to a rubric that assigned greater points for accurate mental state attributions that included multiple characters' mental states. Results demonstrate that the SST is sensitive to variation in ToM ability, can be accurately scored by multiple raters, and exhibits concurrent validity with other social cognitive tasks. The results support the effectiveness of this new measure of ToM in the study of social cognition. The findings are also consistent with studies demonstrating significant relationships among narrative transportation, ToM, and the reading of fiction. Together, the data indicate that reading fiction may be an avenue for improving ToM ability.

Using Fiction to Assess Mental State Understanding: A New Task for Assessing Theory of Mind in Adults

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Dodell-Feder, David; Lincoln, Sarah Hope; Coulson, Joseph P.; Hooker, Christine I.

2013-01-01

Social functioning depends on the ability to attribute and reason about the mental states of others – an ability known as theory of mind (ToM). Research in this field is limited by the use of tasks in which ceiling effects are ubiquitous, rendering them insensitive to individual differences in ToM ability and instances of subtle ToM impairment. Here, we present data from a new ToM task – the Short Story Task (SST) - intended to improve upon many aspects of existing ToM measures. More specifically, the SST was designed to: (a) assess the full range of individual differences in ToM ability without suffering from ceiling effects; (b) incorporate a range of mental states of differing complexity, including epistemic states, affective states, and intentions to be inferred from a first- and second-order level; (c) use ToM stimuli representative of real-world social interactions; (d) require participants to utilize social context when making mental state inferences; (e) exhibit adequate psychometric properties; and (f) be quick and easy to administer and score. In the task, participants read a short story and were asked questions that assessed explicit mental state reasoning, spontaneous mental state inference, and comprehension of the non-mental aspects of the story. Responses were scored according to a rubric that assigned greater points for accurate mental state attributions that included multiple characters’ mental states. Results demonstrate that the SST is sensitive to variation in ToM ability, can be accurately scored by multiple raters, and exhibits concurrent validity with other social cognitive tasks. The results support the effectiveness of this new measure of ToM in the study of social cognition. The findings are also consistent with studies demonstrating significant relationships among narrative transportation, ToM, and the reading of fiction. Together, the data indicate that reading fiction may be an avenue for improving ToM ability. PMID:24244736

Property transmission: an explanatory account of the role of similarity information in causal inference.

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White, Peter A

2009-09-01

Many kinds of common and easily observed causal relations exhibit property transmission, which is a tendency for the causal object to impose its own properties on the effect object. It is proposed that property transmission becomes a general and readily available hypothesis used to make interpretations and judgments about causal questions under conditions of uncertainty, in which property transmission functions as a heuristic. The property transmission hypothesis explains why and when similarity information is used in causal inference. It can account for magical contagion beliefs, some cases of illusory correlation, the correspondence bias, overestimation of cross-situational consistency in behavior, nonregressive tendencies in prediction, the belief that acts of will are causes of behavior, and a range of other phenomena. People learn that property transmission is often moderated by other factors, but under conditions of uncertainty in which the operation of relevant other factors is unknown, it tends to exhibit a pervasive influence on thinking about causality. (c) 2009 APA, all rights reserved.

Principal stratification in causal inference.

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Frangakis, Constantine E; Rubin, Donald B

2002-03-01

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

Causal explanations of distress and general practitioners' assessments of common mental disorder among punjabi and English attendees.

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Bhui, Kamaldeep; Bhugra, Dinesh; Goldberg, David

2002-01-01

The literature on the primary care assessment of mental distress among Indian subcontinent origin patients suggests frequent presentations to general practitioner, but rarely for recognisable psychiatric disorders. This study investigates whether cultural variations in patients' causal explanatory models account for cultural variations in the assessment of non-psychotic mental disorders in primary care. In a two-phase survey, 272 Punjabi and 269 English subjects were screened. The second phase was completed by 209 and 180 subjects, respectively. Causal explanatory models were elicited as explanations of two vignette scenarios. One of these emphasised a somatic presentation and the other anxiety symptoms. Psychiatric disorder was assessed by GPs on a Likert scale and by a psychiatrist on the Clinical Interview Schedule. Punjabis more commonly expressed medical/somatic and religious beliefs. General practitioners were more likely to assess any subject giving psychological explanations to vignette A and English subjects giving religious explanations to vignette B as having a significant psychiatric disorder. Where medical/somatic explanations of distress were most prevalent in response to the somatic vignette, psychological, religious and work explanations were less prevalent among Punjabis but not among English subjects. Causal explanations did not fully explain cultural differences in assessments. General practitioners' assessments and causal explanations are related and influenced by culture, but causal explanations do not fully explain cultural differences in assessments.

Mental State Inferences Abilities Contribution to Verbal Irony Comprehension in Older Adults with Mild Cognitive Impairment

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G. Gaudreau

2015-01-01

Full Text Available Objective. The present study examined mentalizing capacities as well as the relative implication of mentalizing in the comprehension of ironic and sincere assertions among 30 older adults with mild cognitive impairment (MCI and 30 healthy control (HC subjects. Method. Subjects were administered a task evaluating mentalizing by means of short stories. A verbal irony comprehension task, in which participants had to identify ironic or sincere statements within short stories, was also administered; the design of the task allowed uniform implication of mentalizing across the conditions. Results. Findings indicated that participants with MCI have second-order mentalizing difficulties compared to HC subjects. Moreover, MCI participants were impaired compared to the HC group in identifying ironic or sincere stories, both requiring mental inference capacities. Conclusion. This study suggests that, in individuals with MCI, difficulties in the comprehension of ironic and sincere assertions are closely related to second-order mentalizing deficits. These findings support previous data suggesting a strong relationship between irony comprehension and mentalizing.

Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly

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Moura LMVR

2016-12-01

Full Text Available Lidia MVR Moura,1,2 M Brandon Westover,1,2 David Kwasnik,1 Andrew J Cole,1,2 John Hsu3–5 1Massachusetts General Hospital, Department of Neurology, Epilepsy Service, Boston, MA, USA; 2Harvard Medical School, Boston, MA, USA; 3Massachusetts General Hospital, Mongan Institute, Boston, MA, USA; 4Harvard Medical School, Department of Medicine, Boston, MA, USA; 5Harvard Medical School, Department of Health Care Policy, Boston, MA, USA Abstract: The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer’s disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions. Keywords: epilepsy, epidemiology, neurostatistics, causal inference

On the notion of causality in medicine: addressing Austin Bradford Hill and John L. Mackie

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Luís Fernando S. C. de Araújo

2014-03-01

Full Text Available Almost 50 years ago appeared the seminal article by Austin Bradford Hill where he presented parameters for inferring causes from statistical associations, which became known as Hill’s causal criteria. This was a milestone for the renewal of the idea of cause in medicine. Our article revisits his contribution in light of the ideas from the Australian philosopher John L. Mackie, whose important works on causality reached an audience distinct from Hill’s. We suggest that both the British epidemiologist and the Australian philosopher share the purpose of articulating probabilistic determinism and multi-causality, the first with a predominantly probabilistic model and the second with an analytical approach. This article explores the possible consequences of addressing these authors jointly in regard to causal inferences in medicine, especially in respect to mental disorders.

Causal Inference and Model Selection in Complex Settings

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Zhao, Shandong

Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. In this article, we firstly review three main methods that generalize propensity scores in this direction, namely, inverse propensity weighting (IPW), the propensity function (P-FUNCTION), and the generalized propensity score (GPS), along with recent extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. We propose three new methods that provide robust causal estimation based on the P-FUNCTION and GPS. While our proposed P-FUNCTION-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation. In a related line of research, we consider adjustment for posttreatment covariates in causal inference. Even in a randomized experiment, observations might have different compliance performance under treatment and control assignment. This posttreatment covariate cannot be adjusted using standard statistical methods. We review the principal stratification framework which allows for modeling this effect as part of its Bayesian hierarchical models. We generalize the current model to add the possibility of adjusting for pretreatment covariates. We also propose a new estimator of the average treatment effect over the entire population. In a third line of research, we discuss the spectral line detection problem in high energy astrophysics. We carefully review how this problem can be statistically formulated as a precise hypothesis test with point null hypothesis, why a usual likelihood ratio test does not apply for problem of this nature, and a doable fix to correctly

Space and time in perceptual causality

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Benjamin Straube

2010-04-01

Full Text Available Inferring causality is a fundamental feature of human cognition that allows us to theorize about and predict future states of the world. Michotte suggested that humans automatically perceive causality based on certain perceptual features of events. However, individual differences in judgments of perceptual causality cast doubt on Michotte’s view. To gain insights in the neural basis of individual difference in the perception of causality, our participants judged causal relationships in animations of a blue ball colliding with a red ball (a launching event while fMRI-data were acquired. Spatial continuity and temporal contiguity were varied parametrically in these stimuli. We did not find consistent brain activation differences between trials judged as caused and those judged as non-caused, making it unlikely that humans have universal instantiation of perceptual causality in the brain. However, participants were slower to respond to and showed greater neural activity for violations of causality, suggesting that humans are biased to expect causal relationships when moving objects appear to interact. Our participants demonstrated considerable individual differences in their sensitivity to spatial and temporal characteristics in perceiving causality. These qualitative differences in sensitivity to time or space in perceiving causality were instantiated in individual differences in activation of the left basal ganglia or right parietal lobe, respectively. Thus, the perception that the movement of one object causes the movement of another is triggered by elemental spatial and temporal sensitivities, which themselves are instantiated in specific distinct neural networks.

Altered causal connectivity of resting state brain networks in amnesic MCI.

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Peipeng Liang

Full Text Available Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI have concentrated on functional connectivity (FC based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data--default mode network (DMN, hippocampal cortical memory network (HCMN, dorsal attention network (DAN and fronto-parietal control network (FPCN. Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE, and California verbal learning test (CVLT scores. When the whole-brain signal averaged over the entire brain was used as a nuisance co-variate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.

Illusory inferences from a disjunction of conditionals: a new mental models account.

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Barrouillet, P; Lecas, J F

2000-08-14

(Johnson-Laird, P.N., & Savary, F. (1999, Illusory inferences: a novel class of erroneous deductions. Cognition, 71, 191-229.) have recently presented a mental models account, based on the so-called principle of truth, for the occurrence of inferences that are compelling but invalid. This article presents an alternative account of the illusory inferences resulting from a disjunction of conditionals. In accordance with our modified theory of mental models of the conditional, we show that the way individuals represent conditionals leads them to misinterpret the locus of the disjunction and prevents them from drawing conclusions from a false conditional, thus accounting for the compelling character of the illusory inference.

Confounding effects of phase delays on causality estimation.

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Vasily A Vakorin

Full Text Available Linear and non-linear techniques for inferring causal relations between the brain signals representing the underlying neuronal systems have become a powerful tool to extract the connectivity patterns in the brain. Typically these tools employ the idea of Granger causality, which is ultimately based on the temporal precedence between the signals. At the same time, phase synchronization between coupled neural ensembles is considered a mechanism implemented in the brain to integrate relevant neuronal ensembles to perform a cognitive or perceptual task. Phase synchronization can be studied by analyzing the effects of phase-locking between the brain signals. However, we should expect that there is no one-to-one mapping between the observed phase lag and the time precedence as specified by physically interacting systems. Specifically, phase lag observed between two signals may interfere with inferring causal relations. This could be of critical importance for the coupled non-linear oscillating systems, with possible time delays in coupling, when classical linear cross-spectrum strategies for solving phase ambiguity are not efficient. To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality, as established in the literature. Specifically, we compared the performance of the spectral and information-theoretic Granger pipelines as well as standard Granger causality in their relations to the observed phase differences for frequencies at which the signals become synchronized to each other. We found that an information-theoretic approach, which takes into account different time lags between the past of one signal and the future of another signal, was the most robust to phase effects.

Causal inference between bioavailability of heavy metals and environmental factors in a large-scale region

International Nuclear Information System (INIS)

Liu, Yuqiong; Du, Qingyun; Wang, Qi; Yu, Huanyun; Liu, Jianfeng; Tian, Yu; Chang, Chunying; Lei, Jing

2017-01-01

The causation between bioavailability of heavy metals and environmental factors are generally obtained from field experiments at local scales at present, and lack sufficient evidence from large scales. However, inferring causation between bioavailability of heavy metals and environmental factors across large-scale regions is challenging. Because the conventional correlation-based approaches used for causation assessments across large-scale regions, at the expense of actual causation, can result in spurious insights. In this study, a general approach framework, Intervention calculus when the directed acyclic graph (DAG) is absent (IDA) combined with the backdoor criterion (BC), was introduced to identify causation between the bioavailability of heavy metals and the potential environmental factors across large-scale regions. We take the Pearl River Delta (PRD) in China as a case study. The causal structures and effects were identified based on the concentrations of heavy metals (Zn, As, Cu, Hg, Pb, Cr, Ni and Cd) in soil (0–20 cm depth) and vegetable (lettuce) and 40 environmental factors (soil properties, extractable heavy metals and weathering indices) in 94 samples across the PRD. Results show that the bioavailability of heavy metals (Cd, Zn, Cr, Ni and As) was causally influenced by soil properties and soil weathering factors, whereas no causal factor impacted the bioavailability of Cu, Hg and Pb. No latent factor was found between the bioavailability of heavy metals and environmental factors. The causation between the bioavailability of heavy metals and environmental factors at field experiments is consistent with that on a large scale. The IDA combined with the BC provides a powerful tool to identify causation between the bioavailability of heavy metals and environmental factors across large-scale regions. Causal inference in a large system with the dynamic changes has great implications for system-based risk management. - Causation between the

Introductive remarks on causal inference

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Silvana A. Romio

2013-05-01

Full Text Available One of the more challenging issues in epidemiological research is being able to provide an unbiased estimate of the causal exposure-disease effect, to assess the possible etiological mechanisms and the implication for public health. A major source of bias is confounding, which can spuriously create or mask the causal relationship. In the last ten years, methodological research has been developed to better de_ne the concept of causation in epidemiology and some important achievements have resulted in new statistical models. In this review, we aim to show how a technique the well known by statisticians, i.e. standardization, can be seen as a method to estimate causal e_ects, equivalent under certain conditions to the inverse probability treatment weight procedure.

Non-Causal Time-Domain Filters for Single-Channel Noise Reduction

DEFF Research Database (Denmark)

Jensen, Jesper Rindom; Benesty, Jacob; Christensen, Mads Græsbøll

2012-01-01

suppression and signal distortion by allowing the filters to be non-causal. Non-causal time-domain filters require knowledge of the future, and are therefore not directly implementable. If the observed signal is processed in blocks, however, the non-causal filters are implementable. In this paper, we propose...

Causal Attributions of Success and Failure and Mood States in Football Players

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Szczepaniak Joanna

2016-12-01

Full Text Available Introduction. The aim of the study was to determine the causal attributions of success and failure in a football match in a group of football players, as well as to investigate the association of the players’ attributions with their level of achievement and the relationships between their causal attributions and affective states. Material and methods. The study involved 75 football players, including 44 players from the first league and 31 players from the third league. The research was carried out using the Profile of Mood States (POMS by D.M. McNair, M. Lorr, and L.F. Droppleman and a specially designed questionnaire concerning the causal attributions of success and failure. Results. It was found that the football players who participated in the study tended to attribute success to internal causes and failure to external causes. More frequent use of external attributions most likely had an adverse impact on the mood state of the players. Conclusion. Information concerning the attributions that a given player makes can be useful for coaches, as it can help them develop the athlete’s mental abilities more effectively. Beliefs related to attributions can be modified. It is worth considering the benefits of encouraging internal attributions in the case of success and external attributions in situations of failure.

CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS.

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Shpitser, Ilya; Tchetgen, Eric Tchetgen

2016-12-01

Identifying causal parameters from observational data is fraught with subtleties due to the issues of selection bias and confounding. In addition, more complex questions of interest, such as effects of treatment on the treated and mediated effects may not always be identified even in data where treatment assignment is known and under investigator control, or may be identified under one causal model but not another. Increasingly complex effects of interest, coupled with a diversity of causal models in use resulted in a fragmented view of identification. This fragmentation makes it unnecessarily difficult to determine if a given parameter is identified (and in what model), and what assumptions must hold for this to be the case. This, in turn, complicates the development of estimation theory and sensitivity analysis procedures. In this paper, we give a unifying view of a large class of causal effects of interest, including novel effects not previously considered, in terms of a hierarchy of interventions, and show that identification theory for this large class reduces to an identification theory of random variables under interventions from this hierarchy. Moreover, we show that one type of intervention in the hierarchy is naturally associated with queries identified under the Finest Fully Randomized Causally Interpretable Structure Tree Graph (FFRCISTG) model of Robins (via the extended g-formula), and another is naturally associated with queries identified under the Non-Parametric Structural Equation Model with Independent Errors (NPSEM-IE) of Pearl, via a more general functional we call the edge g-formula. Our results motivate the study of estimation theory for the edge g-formula, since we show it arises both in mediation analysis, and in settings where treatment assignment has unobserved causes, such as models associated with Pearl's front-door criterion.

How people explain their own and others’ behavior: A theory of lay causal explanations

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Gisela eBöhm

2015-02-01

Full Text Available A theoretical model is proposed that speci¬fies lay causal theo¬ries of behavior; and supporting experimental evidence is presented. The model’s basic assumption is that diffe¬rent types of behavior trigger different hypotheses concerning the types of causes that may have brought about the behavior. Se¬ven categories are distinguished that are assumed to serve as both behavior types and explanation types: goals, disposi¬tions, tem¬po¬rary states such as emotions, intentional actions, outcomes, events, and sti¬mulus attributes. The mo¬del specifies inference rules that lay people use when explai¬ning beha¬vior (actions are caused by goals; goals are caused by higher order goals or temporary states; temporary states are caused by dispositions, stimulus attributes, or events; outcomes are caused by actions, temporary states, dispositions, stimulus attributes, or events; events are caused by dispositions or preceding events. Two experiments are reported. Experi¬ment 1 showed that free-response explanations followed the assumed inference rules. Expe¬ri¬ment 2 demonstrated that ex¬plana¬tions which match the inference rules are generated faster and more frequently than non-matching explanations. Together, the findings support models that incorporate knowledge-based aspects into the process of causal explanation. The results are discussed with respect to their implications for different stages of this process, such as the activation of causal hypotheses and their subsequent selection, as well as with respect to social influences on this process.

Multichannel Signal Enhancement using Non-Causal, Time-Domain Filters

DEFF Research Database (Denmark)

Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Benesty, Jacob

2013-01-01

In the vast amount of time-domain filtering methods for speech enhancement, the filters are designed to be causal. Recently, however, it was shown that the noise reduction and signal distortion capabilities of such single-channel filters can be improved by allowing the filters to be non-causal. W......In the vast amount of time-domain filtering methods for speech enhancement, the filters are designed to be causal. Recently, however, it was shown that the noise reduction and signal distortion capabilities of such single-channel filters can be improved by allowing the filters to be non......-causal, multichannel filters for enhancement based on an orthogonal decomposition is proposed. The evaluation shows that there is a potential gain in noise reduction and signal distortion by introducing non-causality. Moreover, experiments on real-life speech show that we can improve the perceptual quality....

Causal attributions of vineyard executives – A mental model study of vineyard management☆

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Martin FG Schaffernicht

2017-12-01

Full Text Available This article contributes a reference of causal attributions made by vineyard executives in Chile, where increasing costs and stagnating prices challenge the vineyards’ profits. The investigation was motivated by the question how executives interpret the industry's mid term future and how they reflect on steering their companies. Based on in-depth interviews, causal maps were elaborated to represent the executives’ mental models. These are represented as sequences of attributions, connecting variables by causal links. It was found that some mental models guide policies bound to increase the prices, whereas other models suggest taking the prices as givens and control costs. The collection of causal attributions of the vineyard executives (CAVE has been made publicly available. As a result, CAVE can be used by other management scholars to elicit other executives’ mental models and increase the data base available. Since such research will be cumulative, a minimum size for meaningful statistical analysis can be reached, opening up an avenue for improving the design of business policies. CAVE can also serve executives and consultants in constructing causal argumentations and business policies. Future research and development of supporting software are called for. Keywords: Mental models, Strategy, Business model

Neural Correlates of Causal Power Judgments

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Denise Dellarosa Cummins

2014-12-01

Full Text Available Causal inference is a fundamental component of cognition and perception. Probabilistic theories of causal judgment (most notably causal Bayes networks derive causal judgments using metrics that integrate contingency information. But human estimates typically diverge from these normative predictions. This is because human causal power judgments are typically strongly influenced by beliefs concerning underlying causal mechanisms, and because of the way knowledge is retrieved from human memory during the judgment process. Neuroimaging studies indicate that the brain distinguishes causal events from mere covariation, and between perceived and inferred causality. Areas involved in error prediction are also activated, implying automatic activation of possible exception cases during causal decision-making.

A review of protective factors and causal mechanisms that enhance the mental health of Indigenous Circumpolar youth

OpenAIRE

Petrasek MacDonald, Joanna; Ford, James D.; Cunsolo Willox, Ashlee; Ross, Nancy A.

2013-01-01

Objectives To review the protective factors and causal mechanisms which promote and enhance Indigenous youth mental health in the Circumpolar North. Study design A systematic literature review of peer-reviewed English-language research was conducted to systematically examine the protective factors and causal mechanisms which promote and enhance Indigenous youth mental health in the Circumpolar North. Methods This review followed the Preferred Reporting Items for Systematic Reviews and Meta-An...

Causal inference between bioavailability of heavy metals and environmental factors in a large-scale region.

Science.gov (United States)

Liu, Yuqiong; Du, Qingyun; Wang, Qi; Yu, Huanyun; Liu, Jianfeng; Tian, Yu; Chang, Chunying; Lei, Jing

2017-07-01

The causation between bioavailability of heavy metals and environmental factors are generally obtained from field experiments at local scales at present, and lack sufficient evidence from large scales. However, inferring causation between bioavailability of heavy metals and environmental factors across large-scale regions is challenging. Because the conventional correlation-based approaches used for causation assessments across large-scale regions, at the expense of actual causation, can result in spurious insights. In this study, a general approach framework, Intervention calculus when the directed acyclic graph (DAG) is absent (IDA) combined with the backdoor criterion (BC), was introduced to identify causation between the bioavailability of heavy metals and the potential environmental factors across large-scale regions. We take the Pearl River Delta (PRD) in China as a case study. The causal structures and effects were identified based on the concentrations of heavy metals (Zn, As, Cu, Hg, Pb, Cr, Ni and Cd) in soil (0-20 cm depth) and vegetable (lettuce) and 40 environmental factors (soil properties, extractable heavy metals and weathering indices) in 94 samples across the PRD. Results show that the bioavailability of heavy metals (Cd, Zn, Cr, Ni and As) was causally influenced by soil properties and soil weathering factors, whereas no causal factor impacted the bioavailability of Cu, Hg and Pb. No latent factor was found between the bioavailability of heavy metals and environmental factors. The causation between the bioavailability of heavy metals and environmental factors at field experiments is consistent with that on a large scale. The IDA combined with the BC provides a powerful tool to identify causation between the bioavailability of heavy metals and environmental factors across large-scale regions. Causal inference in a large system with the dynamic changes has great implications for system-based risk management. Copyright © 2017 Elsevier Ltd. All

The Russo-Williamson Theses in the social sciences: causal inference drawing on two types of evidence.

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Claveau, François

2012-12-01

This article examines two theses formulated by Russo and Williamson (2007) in their study of causal inference in the health sciences. The two theses are assessed against evidence from a specific case in the social sciences, i.e., research on the institutional determinants of the aggregate unemployment rate. The first Russo-Williamson Thesis is that a causal claim can only be established when it is jointly supported by difference-making and mechanistic evidence. This thesis is shown not to hold. While researchers in my case study draw extensively on both types of evidence, one causal claim out of the three analyzed is established even though it is exclusively supported by mechanistic evidence. The second Russo-Williamson Thesis is that standard accounts of causality fail to handle the dualist epistemology highlighted in the first Thesis. I argue that a counterfactual-manipulationist account of causality--which is endorsed by many philosophers as well as many social scientists--can perfectly make sense of the typical strategy in my case study to draw on both difference-making and mechanistic evidence; it is just an instance of the common strategy of increasing evidential variety. Copyright © 2012 Elsevier Ltd. All rights reserved.

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

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2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

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Barnett, Lionel; Seth, Anil K

2017-01-01

Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity

A theory of causal learning in children: Causal maps and Bayes nets

OpenAIRE

Gopnik, A; Glymour, C; Sobel, D M; Schulz, L E; Kushnir, T; Danks, D

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computatio...

Post-traumatic stress disorder and cardiometabolic disease: improving causal inference to inform practice.

Science.gov (United States)

Koenen, K C; Sumner, J A; Gilsanz, P; Glymour, M M; Ratanatharathorn, A; Rimm, E B; Roberts, A L; Winning, A; Kubzansky, L D

2017-01-01

Post-traumatic stress disorder (PTSD) has been declared 'a life sentence' based on evidence that the disorder leads to a host of physical health problems. Some of the strongest empirical research - in terms of methodology and findings - has shown that PTSD predicts higher risk of cardiometabolic diseases, specifically cardiovascular disease (CVD) and type 2 diabetes (T2D). Despite mounting evidence, PTSD is not currently acknowledged as a risk factor by cardiovascular or endocrinological medicine. This view is unlikely to change absent compelling evidence that PTSD causally contributes to cardiometabolic disease. This review suggests that with developments in methods for epidemiological research and the rapidly expanding knowledge of the behavioral and biological effects of PTSD the field is poised to provide more definitive answers to questions of causality. First, we discuss methods to improve causal inference using the observational data most often used in studies of PTSD and health, with particular reference to issues of temporality and confounding. Second, we consider recent work linking PTSD with specific behaviors and biological processes, and evaluate whether these may plausibly serve as mechanisms by which PTSD leads to cardiometabolic disease. Third, we evaluate how looking more comprehensively into the PTSD phenotype provides insight into whether specific aspects of PTSD phenomenology are particularly relevant to cardiometabolic disease. Finally, we discuss new areas of research that are feasible and could enhance understanding of the PTSD-cardiometabolic relationship, such as testing whether treatment of PTSD can halt or even reverse the cardiometabolic risk factors causally related to CVD and T2D.

Exploring Work-Related Causal Attributions of Common Mental Disorders.

Science.gov (United States)

Olsen, Ingrid Blø; Øverland, Simon; Reme, Silje Endresen; Løvvik, Camilla

2015-09-01

Common mental disorders (CMDs) are major causes of sickness absence and disability. Prevention requires knowledge of how individuals perceive causal mechanisms, and in this study we sought to examine work-related factors as causal attribution of CMDs. A trial sample of n = 1,193, recruited because they struggled with work participation due to CMDs, answered an open-ended questionnaire item about what they believed were the most important causes of their CMDs. The population included participants at risk of sickness absence, and participants with reduced work participation due to sickness absence, disability or unemployment. We used thematic content analysis and categorized responses from 487 participants who reported work-related factors as causal attributions of their CMDs. Gender differences in work-related causal attributions were also examined. The participants attributed their CMDs to the following work-related factors; work stress, leadership, reduced work participation, job dissatisfaction, work conflict, social work environment, job insecurity and change, workplace bullying, and physical strain. Women tended to attribute CMDs to social factors at work. Findings from this study suggest several work-related risk factors for CMDs. Both factors at the workplace, and reduced work participation, were perceived by study participants as contributing causes of CMDs. Thus, there is a need to promote work participation whilst at the same time targeting aversive workplace factors. Further, our findings indicate that work-related factors may affect women and men differently. This illustrates that the association between work participation and CMDs is complex, and needs to be explored further.

Classical planning and causal implicatures

DEFF Research Database (Denmark)

Blackburn, Patrick Rowan; Benotti, Luciana

In this paper we motivate and describe a dialogue manager (called Frolog) which uses classical planning to infer causal implicatures. A causal implicature is a type of Gricean relation implicature, a highly context dependent form of inference. As we shall see, causal implicatures are important...... to generate clarification requests"; as a result we can model task-oriented dialogue as an interactive process locally structured by negotiation of the underlying task. We give several examples of Frolog-human dialog, discuss the limitations imposed by the classical planning paradigm, and indicate...

Inductive reasoning about causally transmitted properties.

Science.gov (United States)

Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D; Tenenbaum, Joshua B

2008-11-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.

Infertile Individuals’ Marital Relationship Status, Happiness, and Mental Health: A Causal Model

Science.gov (United States)

Ahmadi Forooshany, Seyed Habiballah; Yazdkhasti, Fariba; Safari Hajataghaie, Saiede; Nasr Esfahani, Mohammad Hossein

2014-01-01

Background This study examined the causal model of relation between marital relation- ship status, happiness, and mental health in infertile individuals. Materials and Methods In this descriptive study, 155 subjects (men: 52 and women: 78), who had been visited in one of the infertility Centers, voluntarily participated in a self-evaluation. Golombok Rust Inventory of Marital Status, Oxford Happiness Ques- tionnaire, and General Health Questionnaire were used as instruments of the study. Data was analyzed by SPSS17 and Amos 5 software using descriptive statistics, independent sample t test, and path analysis. Results Disregarding the gender factor, marital relationship status was directly related to happiness (phappiness was directly related to mental health, (phappiness and mental health was significant (phappiness had a mediator role in relation between marital relationship status and mental health in infertile individu- als disregarding the gender factor. Also, considering the gender factor, only in infertile women, marital relationship status can directly and indirectly affect happiness and mental health. PMID:25379161

Infertile individuals' marital relationship status, happiness, and mental health: a causal model.

Science.gov (United States)

Ahmadi Forooshany, Seyed Habiballah; Yazdkhasti, Fariba; Safari Hajataghaie, Saiede; Nasr Esfahani, Mohammad Hossein

2014-10-01

This study examined the causal model of relation between marital relation- ship status, happiness, and mental health in infertile individuals. In this descriptive study, 155 subjects (men: 52 and women: 78), who had been visited in one of the infertility Centers, voluntarily participated in a self-evaluation. Golombok Rust Inventory of Marital Status, Oxford Happiness Ques- tionnaire, and General Health Questionnaire were used as instruments of the study. Data was analyzed by SPSS17 and Amos 5 software using descriptive statistics, independent sample t test, and path analysis. Disregarding the gender factor, marital relationship status was directly related to happiness (phappiness was directly related to mental health, (phappiness and mental health was significant (phappiness had a mediator role in relation between marital relationship status and mental health in infertile individu- als disregarding the gender factor. Also, considering the gender factor, only in infertile women, marital relationship status can directly and indirectly affect happiness and mental health.

Epidemiological causality.

Science.gov (United States)

Morabia, Alfredo

2005-01-01

Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining whether what has been found grossly contradicts what we think we already know: how strong is the association? Is there a dose-response relationship? Does the cause precede the effect? Is the effect biologically plausible? Etc. This approach to causal inference can be traced back to the English philosophers David Hume and John Stuart Mill. On the other hand, the mode of establishing causality, devised by Jakob Henle and Robert Koch, which has been fruitful in bacteriology, requires that in every instance the effect invariably follows the cause (e.g., inoculation of Koch bacillus and tuberculosis). This is incompatible with epidemiological causality which has to deal with probabilistic effects (e.g., smoking and lung cancer), and is therefore invariant only for the population.

Challenges to inferring causality from viral information dispersion in dynamic social networks

Science.gov (United States)

Ternovski, John

2014-06-01

Understanding the mechanism behind large-scale information dispersion through complex networks has important implications for a variety of industries ranging from cyber-security to public health. With the unprecedented availability of public data from online social networks (OSNs) and the low cost nature of most OSN outreach, randomized controlled experiments, the "gold standard" of causal inference methodologies, have been used with increasing regularity to study viral information dispersion. And while these studies have dramatically furthered our understanding of how information disseminates through social networks by isolating causal mechanisms, there are still major methodological concerns that need to be addressed in future research. This paper delineates why modern OSNs are markedly different from traditional sociological social networks and why these differences present unique challenges to experimentalists and data scientists. The dynamic nature of OSNs is particularly troublesome for researchers implementing experimental designs, so this paper identifies major sources of bias arising from network mutability and suggests strategies to circumvent and adjust for these biases. This paper also discusses the practical considerations of data quality and collection, which may adversely impact the efficiency of the estimator. The major experimental methodologies used in the current literature on virality are assessed at length, and their strengths and limits identified. Other, as-yetunsolved threats to the efficiency and unbiasedness of causal estimators--such as missing data--are also discussed. This paper integrates methodologies and learnings from a variety of fields under an experimental and data science framework in order to systematically consolidate and identify current methodological limitations of randomized controlled experiments conducted in OSNs.

How multiple causes combine: independence constraints on causal inference.

Science.gov (United States)

Liljeholm, Mimi

2015-01-01

According to the causal power view, two core constraints-that causes occur independently (i.e., no confounding) and influence their effects independently-serve as boundary conditions for causal induction. This study investigated how violations of these constraints modulate uncertainty about the existence and strength of a causal relationship. Participants were presented with pairs of candidate causes that were either confounded or not, and that either interacted or exerted their influences independently. Consistent with the causal power view, uncertainty about the existence and strength of causal relationships was greater when causes were confounded or interacted than when unconfounded and acting independently. An elemental Bayesian causal model captured differences in uncertainty due to confounding but not those due to an interaction. Implications of distinct sources of uncertainty for the selection of contingency information and causal generalization are discussed.

A theory of causal learning in children: causal maps and Bayes nets.

Science.gov (United States)

Gopnik, Alison; Glymour, Clark; Sobel, David M; Schulz, Laura E; Kushnir, Tamar; Danks, David

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Non-parametric causality detection: An application to social media and financial data

Science.gov (United States)

Tsapeli, Fani; Musolesi, Mirco; Tino, Peter

2017-10-01

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about a particular parametric form of the model expressing statistical relationships among the variables of the study and can effectively control a large number of observed factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.

Large-scale Granger causality analysis on resting-state functional MRI

Science.gov (United States)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

How do practising clinicians and students apply newly learned causal information about mental disorders?

Science.gov (United States)

de Kwaadsteniet, Leontien; Kim, Nancy S; Yopchick, Jennelle E

2013-02-01

New causal theories explaining the aetiology of psychiatric disorders continuously appear in the literature. How might such new information directly impact clinical practice, to the degree that clinicians are aware of it and accept it? We investigated whether expert clinical psychologists and students use new causal information about psychiatric disorders according to rationalist norms in their diagnostic reasoning. Specifically, philosophical and Bayesian analyses suggest that it is rational to draw stronger inferences about the presence of a disorder when a client's presenting symptoms are from disparate locations in a causal theory of the disorder than when they are from proximal locations. In a controlled experiment, we presented experienced clinical psychologists and students with recently published causal theories for different disorders; specifically, these theories proposed how the symptoms of each disorder stem from a root cause. Participants viewed hypothetical clients with presenting proximal or diverse symptoms, and indicated either the likelihood that the client has the disorder, or what additional information they would seek out to help inform a diagnostic decision. Clinicians and students alike showed a strong preference for diverse evidence, over proximal evidence, in making diagnostic judgments and in seeking additional information. They did not show this preference in the control condition, in which they gave their own opinions prior to learning the causal information. These findings suggest that experienced clinical psychologists and students are likely to use newly learned causal knowledge in a normative, rational way in diagnostic reasoning. © 2011 Blackwell Publishing Ltd.

Electrophysiological difference between mental state decoding and mental state reasoning.

Science.gov (United States)

Cao, Bihua; Li, Yiyuan; Li, Fuhong; Li, Hong

2012-06-29

Previous studies have explored the neural mechanism of Theory of Mind (ToM), but the neural correlates of its two components, mental state decoding and mental state reasoning, remain unclear. In the present study, participants were presented with various photographs, showing an actor looking at 1 of 2 objects, either with a happy or an unhappy expression. They were asked to either decode the emotion of the actor (mental state decoding task), predict which object would be chosen by the actor (mental state reasoning task), or judge at which object the actor was gazing (physical task), while scalp potentials were recorded. Results showed that (1) the reasoning task elicited an earlier N2 peak than the decoding task did over the prefrontal scalp sites; and (2) during the late positive component (240-440 ms), the reasoning task elicited a more positive deflection than the other two tasks did at the prefrontal scalp sites. In addition, neither the decoding task nor the reasoning task has no left/right hemisphere difference. These findings imply that mental state reasoning differs from mental state decoding early (210 ms) after stimulus onset, and that the prefrontal lobe is the neural basis of mental state reasoning. Copyright © 2012 Elsevier B.V. All rights reserved.

Inference of directed climate networks: role of instability of causality estimation methods

Science.gov (United States)

Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

2013-04-01

Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge

Repair of Partly Misspecified Causal Diagrams.

Science.gov (United States)

Oates, Chris J; Kasza, Jessica; Simpson, Julie A; Forbes, Andrew B

2017-07-01

Errors in causal diagrams elicited from experts can lead to the omission of important confounding variables from adjustment sets and render causal inferences invalid. In this report, a novel method is presented that repairs a misspecified causal diagram through the addition of edges. These edges are determined using a data-driven approach designed to provide improved statistical efficiency relative to de novo structure learning methods. Our main assumption is that the expert is "directionally informed," meaning that "false" edges provided by the expert would not create cycles if added to the "true" causal diagram. The overall procedure is cast as a preprocessing technique that is agnostic to subsequent causal inferences. Results based on simulated data and data derived from an observational cohort illustrate the potential for data-assisted elicitation in epidemiologic applications. See video abstract at, http://links.lww.com/EDE/B208.

Associations between causal attributions and personal stigmatizing attitudes in untreated persons with current mental health problems.

Science.gov (United States)

Stolzenburg, Susanne; Freitag, Simone; Schmidt, Silke; Schomerus, Georg

2018-02-01

Past research has shown that among the general public, certain causal explanations like biomedical causes are associated with stronger desire for social distance from persons with mental illness. Aim of this study was to find out how different causal attributions of persons with untreated mental health problems regarding their own complaints are associated with stigmatizing attitudes, anticipated self-stigma when seeking help and perceived stigma-stress. Altogether, 207 untreated persons with a current depressive syndrome were interviewed. Biomedical causes, but also belief in childhood trauma or unhealthy behavior as a cause of the problem, were associated with stronger personal stigma and with more stigma-stress. Similarities and differences to findings among the general population and implications for future research are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Brief mindfulness meditation improves mental state attribution and empathizing.

Science.gov (United States)

Tan, Lucy B G; Lo, Barbara C Y; Macrae, C Neil

2014-01-01

The ability to infer and understand the mental states of others (i.e., Theory of Mind) is a cornerstone of human interaction. While considerable efforts have focused on explicating when, why and for whom this fundamental psychological ability can go awry, considerably less is known about factors that may enhance theory of mind. Accordingly, the current study explored the possibility that mindfulness-based meditation may improve people's mindreading skills. Following a 5-minute mindfulness induction, participants with no prior meditation experience completed tests that assessed mindreading and empathic understanding. The results revealed that brief mindfulness meditation enhanced both mental state attribution and empathic concern, compared to participants in the control group. These findings suggest that mindfulness may be a powerful technique for facilitating core aspects of social-cognitive functioning.

Could Plasmodium vivax malaria trigger malnutrition? Revisiting the Bradford Hill criteria to assess a causal relationship between two neglected problems

Directory of Open Access Journals (Sweden)

Wuelton Marcelo Monteiro

2016-06-01

Full Text Available Abstract: The benign characteristics formerly attributed to Plasmodium vivax infections have recently changed owing to the increasing number of reports of severe vivax malaria resulting in a broad spectrum of clinical complications, probably including undernutrition. Causal inference is a complex process, and arriving at a tentative inference of the causal or non-causal nature of an association is a subjective process limited by the existing evidence. Applying classical epidemiology principles, such as the Bradford Hill criteria, may help foster an understanding of causality and lead to appropriate interventions being proposed that may improve quality of life and decrease morbidity in neglected populations. Here, we examined these criteria in the context of the available data suggesting that vivax malaria may substantially contribute to childhood malnutrition. We found the data supported a role for P. vivax in the etiology of undernutrition in endemic areas. Thus, the application of modern causal inference tools, in future studies, may be useful in determining causation.

Infertile Individuals’ Marital Relationship Status, Happiness, and Mental Health: A Causal Model

Directory of Open Access Journals (Sweden)

Seyed Habiballah Ahmadi Forooshany

2014-11-01

Full Text Available Background: This study examined the causal model of relation between marital relationship status, happiness, and mental health in infertile individuals. Materials and Methods: In this descriptive study, 155 subjects (men: 52 and women: 78, who had been visited in one of the infertility Centers, voluntarily participated in a self-evaluation. Golombok Rust Inventory of Marital Status, Oxford Happiness Questionnaire, and General Health Questionnaire were used as instruments of the study. Data was analyzed by SPSS17 and Amos 5 software using descriptive statistics, independent sample t test, and path analysis. Results: Disregarding the gender factor, marital relationship status was directly related to happiness (p<0.05 and happiness was directly related to mental health, (p<0.05. Also, indirect relation between marital relationship status and mental health was significant (p<0.05. These results were confirmed in women participants but in men participants only the direct relation between happiness and mental health was significant (p<0.05. Conclusion: Based on goodness of model fit in fitness indexes, happiness had a mediator role in relation between marital relationship status and mental health in infertile individuals disregarding the gender factor. Also, considering the gender factor, only in infertile women, marital relationship status can directly and indirectly affect happiness and mental health.

Sensorimotor Network Crucial for Inferring Amusement from Smiles.

Science.gov (United States)

Paracampo, Riccardo; Tidoni, Emmanuele; Borgomaneri, Sara; di Pellegrino, Giuseppe; Avenanti, Alessio

2017-11-01

Understanding whether another's smile reflects authentic amusement is a key challenge in social life, yet, the neural bases of this ability have been largely unexplored. Here, we combined transcranial magnetic stimulation (TMS) with a novel empathic accuracy (EA) task to test whether sensorimotor and mentalizing networks are critical for understanding another's amusement. Participants were presented with dynamic displays of smiles and explicitly requested to infer whether the smiling individual was feeling authentic amusement or not. TMS over sensorimotor regions representing the face (i.e., in the inferior frontal gyrus (IFG) and ventral primary somatosensory cortex (SI)), disrupted the ability to infer amusement authenticity from observed smiles. The same stimulation did not affect performance on a nonsocial task requiring participants to track the smiling expression but not to infer amusement. Neither TMS over prefrontal and temporo-parietal areas supporting mentalizing, nor peripheral control stimulations, affected performance on either task. Thus, motor and somatosensory circuits for controlling and sensing facial movements are causally essential for inferring amusement from another's smile. These findings highlight the functional relevance of IFG and SI to amusement understanding and suggest that EA abilities may be grounded in sensorimotor networks for moving and feeling the body. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The causal structure of utility conditionals.

Science.gov (United States)

Bonnefon, Jean-François; Sloman, Steven A

2013-01-01

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ''if p then q'' statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. Copyright © 2012 Cognitive Science Society, Inc.

Causal mediation analysis with multiple causally non-ordered mediators.

Science.gov (United States)

Taguri, Masataka; Featherstone, John; Cheng, Jing

2018-01-01

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

A quantum probability model of causal reasoning

Directory of Open Access Journals (Sweden)

Jennifer S Trueblood

2012-05-01

Full Text Available People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause with diagnostic judgments (i.e., the conditional probability of a cause given an effect. The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.

Domain-specific perceptual causality in children depends on the spatio-temporal configuration, not motion onset

Directory of Open Access Journals (Sweden)

Anne eSchlottmann

2013-07-01

Full Text Available Humans, even babies, perceive causality when one shape moves briefly and linearly after another. Motion timing is crucial in this and causal impressions disappear with short delays between motions. However, the role of temporal information is more complex: It is both a cue to causality and a factor that constrains processing. It affects ability to distinguish causality from non-causality, and social from mechanical causality. Here we study both issues with 3- to 7-year-olds and adults who saw two computer-animated squares and chose if a picture of mechanical, social or non-causality fit each event best. Prior work fit with the standard view that early in development, the distinction between the social and physical domains depends mainly on whether or not the agents make contact, and that this reflects concern with domain-specific motion onset, in particular, whether the motion is self-initiated or not. The present experiments challenge both parts of this position. In Experiments 1 and 2, we showed that not just spatial, but also animacy and temporal information affect how children distinguish between physical and social causality. In Experiments 3 and 4 we showed that children do not seem to use spatio-temporal information in perceptual causality to make inferences about self- or other-initiated motion onset. Overall, spatial contact may be developmentally primary in domain-specific perceptual causality in that it is processed easily and is dominant over competing cues, but it is not the only cue used early on and it is not used to infer motion onset. Instead, domain-specific causal impressions may be automatic reactions to specific perceptual configurations, with a complex role for temporal information.

Factors Influencing the Sahelian Paradox at the Local Watershed Scale: Causal Inference Insights

Science.gov (United States)

Van Gordon, M.; Groenke, A.; Larsen, L.

2017-12-01

While the existence of paradoxical rainfall-runoff and rainfall-groundwater correlations are well established in the West African Sahel, the hydrologic mechanisms involved are poorly understood. In pursuit of mechanistic explanations, we perform a causal inference analysis on hydrologic variables in three watersheds in Benin and Niger. Using an ensemble of techniques, we compute the strength of relationships between observational soil moisture, runoff, precipitation, and temperature data at seasonal and event timescales. Performing analysis over a range of time lags allows dominant time scales to emerge from the relationships between variables. By determining the time scales of hydrologic connectivity over vertical and lateral space, we show differences in the importance of overland and subsurface flow over the course of the rainy season and between watersheds. While previous work on the paradoxical hydrologic behavior in the Sahel focuses on surface processes and infiltration, our results point toward the importance of subsurface flow to rainfall-runoff relationships in these watersheds. The hypotheses generated from our ensemble approach suggest that subsequent explorations of mechanistic hydrologic processes in the region include subsurface flow. Further, this work highlights how an ensemble approach to causal analysis can reveal nuanced relationships between variables even in poorly understood hydrologic systems.

Errors in causal inference: an organizational schema for systematic error and random error.

Science.gov (United States)

Suzuki, Etsuji; Tsuda, Toshihide; Mitsuhashi, Toshiharu; Mansournia, Mohammad Ali; Yamamoto, Eiji

2016-11-01

To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference. We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability. Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods. Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. Copyright © 2016 Elsevier Inc. All rights reserved.

Mini mental state examination

DEFF Research Database (Denmark)

Kørner, Ejnar Alex; Lauritzen, Lise; Wang, August

2008-01-01

INTRODUCTION: The Mini Mental State Examination (MMSE) is widely used in Denmark, but often in non-validated versions. In 2000 a cross-sectional workgroup decided on a new common version of the MMSE with a corresponding manual, which is validated for the first time in the present study. MATERIALS...... the severity of dementia disorders. Udgivelsesdato: 2008-Feb-25...

The effect of military deployment on mental health

DEFF Research Database (Denmark)

Lyk-Jensen, Stéphanie; Weatherall, Cecilie Dohlmann; W. Jepsen, Peter

for the non-deployed eligible men, and our results hold to various robustness checks. Our administrative records provide objective measures of mental health service use in the form of psychiatric diagnoses and purchase of mental health-related medication. The very rich data makes it possible to control......In this paper we estimate the causal effect of military deployment on soldiers’ mental health. To handle the selection bias problem, we use longitudinal data for deployed and non-deployed eligible men in a difference-in-differences setting. Using pair-wise matching, we impute deployment dates...... for important variables like intelligence tests and family background. We find significant adverse effects of military deployment on soldiers’ mental health service use. Highlights: - Causal effect of military deployment on soldiers’ use of mental health service - Using a difference-in-differences approach...

Granger Causality and Unit Roots

DEFF Research Database (Denmark)

Rodríguez-Caballero, Carlos Vladimir; Ventosa-Santaulària, Daniel

2014-01-01

The asymptotic behavior of the Granger-causality test under stochastic nonstationarity is studied. Our results confirm that the inference drawn from the test is not reliable when the series are integrated to the first order. In the presence of deterministic components, the test statistic diverges......, eventually rejecting the null hypothesis, even when the series are independent of each other. Moreover, controlling for these deterministic elements (in the auxiliary regressions of the test) does not preclude the possibility of drawing erroneous inferences. Granger-causality tests should not be used under...

Business as usual--at the state mental hospital.

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Fowlkes, M R

1975-02-01

Despite official policy and professional emphasis to the contrary, the custodial mental hospital continues to exist as a major form of state-provided mental health care. In this paper, one such institution, "New England State Hospital", is described, and the various features of hospital organization that sustain a system of custodial care are discussed. Although the custodial hospital offers little to its patients, its persistent survival can be explained by the number of non-patient vested interests that are well served by the state hospital, precisely in its existing custodial form. The case study of New England State Hospital suggests that reform of state mental institutions depends less on a programmatic formulation of desired changes than on an understanding of the structured resistance to such changes.

Thinking Fast and Slow about Causality: Response to Palinkas

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Marsh, Jeanne C.

2014-01-01

Larry Palinkas advances the developing science of social work by providing an explanation of how social science research methods, both qualitative and quantitative, can improve our capacity to draw casual inferences. Understanding causal relations and making causal inferences--with the promise of being able to predict and control outcomes--is…

Illness causal beliefs in Turkish immigrants

Directory of Open Access Journals (Sweden)

Klimidis Steven

2007-07-01

Full Text Available Abstract Background People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Methods Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Results Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Conclusion Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes

Illness causal beliefs in Turkish immigrants.

Science.gov (United States)

Minas, Harry; Klimidis, Steven; Tuncer, Can

2007-07-24

People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different

Inferring causal genomic alterations in breast cancer using gene expression data

Science.gov (United States)

2011-01-01

Background One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies. Results We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments. Conclusions To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data. PMID:21806811

R Package multiPIM: A Causal Inference Approach to Variable Importance Analysis

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Stephan J Ritter

2014-04-01

Full Text Available We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including super learner, a meta-learner which combines several different algorithms into one. We describe a simulation in which the double robust TMLE is compared to the graphical computation estimator. We also provide example analyses using two data sets which are included with the package.

Perceptual learning shapes multisensory causal inference via two distinct mechanisms.

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McGovern, David P; Roudaia, Eugenie; Newell, Fiona N; Roach, Neil W

2016-04-19

To accurately represent the environment, our brains must integrate sensory signals from a common source while segregating those from independent sources. A reasonable strategy for performing this task is to restrict integration to cues that coincide in space and time. However, because multisensory signals are subject to differential transmission and processing delays, the brain must retain a degree of tolerance for temporal discrepancies. Recent research suggests that the width of this 'temporal binding window' can be reduced through perceptual learning, however, little is known about the mechanisms underlying these experience-dependent effects. Here, in separate experiments, we measure the temporal and spatial binding windows of human participants before and after training on an audiovisual temporal discrimination task. We show that training leads to two distinct effects on multisensory integration in the form of (i) a specific narrowing of the temporal binding window that does not transfer to spatial binding and (ii) a general reduction in the magnitude of crossmodal interactions across all spatiotemporal disparities. These effects arise naturally from a Bayesian model of causal inference in which learning improves the precision of audiovisual timing estimation, whilst concomitantly decreasing the prior expectation that stimuli emanate from a common source.

A quantum causal discovery algorithm

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Giarmatzi, Christina; Costa, Fabio

2018-03-01

Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.

Mini-mental state examination as a predictor of mortality among older people referred to secondary mental healthcare.

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Su, Yu-Ping; Chang, Chin-Kuo; Hayes, Richard D; Perera, Gayan; Broadbent, Matthew; To, David; Hotopf, Matthew; Stewart, Robert

2014-01-01

Lower levels of cognitive function have been found to be associated with higher mortality in older people, particularly in dementia, but the association in people with other mental disorders is still inconclusive. Data were analysed from a large mental health case register serving a geographic catchment of 1.23 million residents, and associations were investigated between cognitive function measured by the Mini-Mental State Examination (MMSE) and survival in patients aged 65 years old and over. Cox regressions were carried out, adjusting for age, gender, psychiatric diagnosis, ethnicity, marital status, and area-level socioeconomic index. A total of 6,704 subjects were involved, including 3,368 of them having a dementia diagnosis and 3,336 of them with depression or other diagnoses. Descriptive outcomes by Kaplan-Meier curves showed significant differences between those with normal and impaired cognitive function (MMSE scoremental health services regardless of a dementia diagnosis. Causal pathways between this exposure and outcome (for example, suboptimal healthcare) need further investigation.

Causal analysis of self-sustaining processes in the log-layer of wall-bounded turbulence

Science.gov (United States)

Lozano-Duran, Adrian; Bae, Hyunji Jane

2017-11-01

Despite the large amount of information provided by direct numerical simulations of turbulent flows, the underlying dynamics remain elusive even in the most simple and canonical configurations. Most standard methods used to investigate turbulence do not provide a clear causal inference between events, which is necessary to determine this dynamics, particularly in self-sustaning processes. In the present work, we examine the causal interactions between streaks and rolls in the logarithmic layer of minimal turbulent channel flow. Causality between structures is assessed in a non-intrusive manner by transfer entropy, i.e., how much the uncertainty of one structure is reduced by knowing the past states of the others. Streaks are represented by the first Fourier modes of the streamwise velocity, while rolls are defined by the wall-normal and spanwise velocities. The results show that the process is mainly unidirectional rather than cyclic, and that the log-layer motions are sustained by extracting energy from the mean shear, which controls the dynamics and time-scales. The well-known lift-up effect is shown to be not a key ingredient in the causal network between shear, streaks and rolls. Funded by ERC Coturb Madrid Summer Program.

Causal analysis of self-sustaining processes in the logarithmic layer of wall-bounded turbulence

Science.gov (United States)

Bae, H. J.; Encinar, M. P.; Lozano-Durán, A.

2018-04-01

Despite the large amount of information provided by direct numerical simulations of turbulent flows, their underlying dynamics remain elusive even in the most simple and canonical configurations. Most common approaches to investigate the turbulence phenomena do not provide a clear causal inference between events, which is essential to determine the dynamics of self-sustaining processes. In the present work, we examine the causal interactions between streaks, rolls and mean shear in the logarithmic layer of a minimal turbulent channel flow. Causality between structures is assessed in a non-intrusive manner by transfer entropy, i.e., how much the uncertainty of one structure is reduced by knowing the past states of the others. We choose to represent streaks by the first Fourier modes of the streamwise velocity, while rolls are defined by the wall-normal and spanwise velocity modes. The results show that the process is mainly unidirectional rather than cyclic, and that the log-layer motions are sustained by extracting energy from the mean shear which controls the dynamics and time-scales. The well-known lift-up effect is also identified, but shown to be of secondary importance in the causal network between shear, streaks and rolls.

Personality traits and mental health states of methamphetamine-dependent and methamphetamine non-using MSM.

Science.gov (United States)

Solomon, Todd M; Kiang, Mathew V; Halkitis, Perry N; Moeller, Robert W; Pappas, Molly K

2010-02-01

This analysis considers the relation between personality traits, mental health states and methamphetamine (MA) use in 60 men who have sex with men (MSM). Thirty MA-dependent and 30 MA non-using MSM were assessed on the Neo Five Factor Inventory, the Brief Symptom Inventory, the Perceived Stress Scale, and the Posttraumatic Stress Disorder Checklist-Civilian Version tests. Our results indicate differences between groups on a variety of measures of personality traits and mental states. Specifically, MA-dependent participants were found to be more Neurotic, less Open, less Agreeable, and less Conscientious. Further, MA-dependent participants were found to have higher levels of Paranoid Ideation and higher levels of Interpersonal Sensitivity. Given the high prevalence of MA use in the MSM community and the association between MA use and sexual risk taking, our findings provided a clearer understanding of how individual personality traits may be a factor in the continued use of this drug among MSM. Further research should seek to incorporate individual personality traits into the development of efficacious MA-specific treatment interventions.

Children's False Memory for Emotional Events: A Developmental Perspective on Emotion s Impact on Backwards Causal-Inference Errors.

OpenAIRE

Vennerød, Frida Felicia

2014-01-01

The present study examines how emotion affects false memory formation using the Backwards Causal-Inference Paradigm, with a developmental perspective. One-hundred-and-thirty-two children participated in the study, with 56 children aged 6-8 years, 43 children aged 9-10 years and 33 children aged 11-12 years. The children were presented with one of six different PowerPoints, which all displayed the same scripts in photographs, but differed in emotional (positive vs. negative vs. neutral) outcom...

Beyond Scientism and Skepticism: An Integrative Approach to Global Mental Health

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Dan J Stein

2015-11-01

Full Text Available The global burden of disorders has shifted from infectious disease to non-communicable diseases, including neuropsychiatric disorders. Whereas infectious disease can sometimes be combated by targeting single causal mechanisms, such as prevention of contact-spread illness by hand-washing, in the case of mental disorders multiple causal mechanisms are relevant. The emergent field of global mental health has emphasized the magnitude of the treatment gap, particularly in the low and middle income world, and has paid particular attention to upstream causal factors, for example, poverty, inequality, and gender discrimination in the pathogenesis of mental disorders. However, this field has also been criticised for relying on erroneous Western paradigms of mental illness, which may not be relevant or appropriate to the low- and middle-income context. Here, it is important to steer a path between scienticism and skepticism. Scientism regards mental disorders as essential categories, and takes a covering law approach to causality; skepticism regards mental disorders as merely social constructions, and emphasizes the role of political power in causal relations. We propose an integrative model that emphasizes the contribution of a broad range of causal mechanisms operating at biological and societal levels to mental disorders, and the consequent importance of broad-spectrum and multi-pronged approaches to intervention.

The observability principle and beyond. Reply to comments on "Seeing mental states: An experimental strategy for measuring the observability of other minds" by Cristina Becchio et al.

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Becchio, Cristina; Koul, Atesh; Ansuini, Caterina; Bertone, Cesare; Cavallo, Andrea

2018-03-01

Is it possible to directly perceive others' mental states? Mediating the debate between Direct Perception and Inferentialism proponents would require knowing "what counts as an inference and how to tell the difference between inferential and non-inferential processing" [1]. However, few theorists have even attempted to answer the question of what counts as inference. The consequence, as noted by Spaulding [1], is that "given that neither Inferentialists nor DSP [Direct Social Perception, Ed.] proponents specify what they mean by inference, it is hard to tell what exactly each side is affirming and denying. Thus, the debate between Inferentialism and DSP is at an impasse". Similar considerations apply to distinguishing between what is 'observable' versus 'unobservable' [2].

Quantum-Like Representation of Non-Bayesian Inference

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Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

[Professional stressors and common mental health disorders: Causal links?

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Nicolas, C; Chawky, N; Jourdan-Ionescu, C; Drouin, M-S; Page, C; Houlfort, N; Beauchamp, G; Séguin, M

2017-03-22

According to the World Health Organization, depression has become the leading cause of disability in the world, contributing significantly to the burden of health issues especially in the industrialized countries. This is a major public health problem, with potential impact on work climates, productivity at work and the continued existence of the organizations. Some recent studies have examined potential links between professional factors and common mental health disorders, but none have demonstrated a direct causal link. In the present study, we explored possible links between work-related stressors and common mental health disorders, with the objective of determining priority mental health prevention axes. The study used a life trajectory method. We compared professional stressors and difficulties present in other spheres of life in the last five years between two groups: a group of 29 participants with common mental health disorders during the last five years (depression, anxiety disorders, eating disorders, substance use disorders, pathological gambling), and a group of 29 participants who have not experienced a mental health disorder in the last five years. Data were collected from semi-structured interviews with the participants using a life course analysis method. Each participant was interviewed during two or three meetings of two to three hour duration. Questions regarding difficulties in different spheres of life and mental health were asked. More precisely, data were collected with regards to the presence or absence of mental health disorders in the last five years and the nature of mental health disorders and difficulties. Moreover, we collected data pertaining to the most important positive and negative events in different spheres of life that were present in the last five years, including family life, romantic relationships, social life, academic difficulties, losses and separations, episodes of personal difficulties, financial difficulties as well as

Hume, Mill, Hill, and the sui generis epidemiologic approach to causal inference.

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Morabia, Alfredo

2013-11-15

The epidemiologic approach to causal inference (i.e., Hill's viewpoints) consists of evaluating potential causes from the following 2, noncumulative angles: 1) established results from comparative, observational, or experimental epidemiologic studies; and 2) reviews of nonepidemiologic evidence. It does not involve statements of statistical significance. The philosophical roots of Hill's viewpoints are unknown. Superficially, they seem to descend from the ideas of Hume and Mill. Hill's viewpoints, however, use a different kind of evidence and have different purposes than do Hume's rules or Mill's system of logic. In a nutshell, Hume ignores comparative evidence central to Hill's viewpoints. Mill's logic disqualifies as invalid nonexperimental evidence, which forms the bulk of epidemiologic findings reviewed from Hill's viewpoints. The approaches by Hume and Mill cannot corroborate successful implementations of Hill's viewpoints. Besides Hume and Mill, the epidemiologic literature is clueless about a plausible, pre-1965 philosophical origin of Hill's viewpoints. Thus, Hill's viewpoints may be philosophically novel, sui generis, still waiting to be validated and justified.

The causal nexus between oil prices and equity market in the U.S.: A regime switching model

International Nuclear Information System (INIS)

Balcilar, Mehmet; Ozdemir, Zeynel Abidin

2013-01-01

The aim of this paper is to analyse the causal link between monthly oil futures price changes and a sub-grouping of S and P 500 stock index changes. The causal linkage between oil and stock markets is modelled using a vector autoregressive model with time-varying parameters so as to reflect changes in Granger causality over time. A Markov switching vector autoregressive (MS-VAR) model, in which causal link between the series is stochastic and governed by an unobservable Markov chain, is used for inferring time-varying causality. Although we do not find any lead–lag type Granger causality, the results based on the MS-VAR model clearly show that oil futures price has strong regime prediction power for a sub-grouping of S and P 500 stock index during various sub-periods in the sample, while there is a weak evidence for the regime prediction power of a sub-grouping of S and P 500 stock indexes. The regime-prediction non-causality tests on the MS-VAR model show that both variables are useful for making inference about the regime process and that the evidence on regime-prediction causality is primarily found in the equation describing a sub-grouping of S and P 500 stock market returns. The evidence from the conditional non-causality tests shows that past information on the other series fails to improve the one step ahead prediction for both oil futures and stock returns. - Highlights: • We analyse the causal links between oil futures price and a sub-grouping of S and P 500 index. • The causal links are modelled using a regime switching model. • We do not find any lead–lag type Granger causality between the series. • The results show that oil futures price has regime prediction power for a sub-grouping of S and P 500 stock index

Beyond Markov: Accounting for independence violations in causal reasoning.

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Rehder, Bob

2018-06-01

Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y 1 ←X→Y 2 ) was extended so that the effects themselves had effects (Z 1 ←Y 1 ←X→Y 2 →Z 2 ). A traditional common effect network (Y 1 →X←Y 2 ) was extended so that the causes themselves had causes (Z 1 →Y 1 →X←Y 2 ←Z 2 ). Subjects' inferences were most consistent with the beta-Q model in which consistent states of the world-those in which variables are either mostly all present or mostly all absent-are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects' inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people's causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented. Copyright © 2018 Elsevier Inc. All rights reserved.

Inference of beliefs and emotions in patients with Alzheimer's disease.

Science.gov (United States)

Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn

2006-01-01

The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).

Causally nonseparable processes admitting a causal model

International Nuclear Information System (INIS)

Feix, Adrien; Araújo, Mateus; Brukner, Caslav

2016-01-01

A recent framework of quantum theory with no global causal order predicts the existence of ‘causally nonseparable’ processes. Some of these processes produce correlations incompatible with any causal order (they violate so-called ‘causal inequalities’ analogous to Bell inequalities ) while others do not (they admit a ‘causal model’ analogous to a local model ). Here we show for the first time that bipartite causally nonseparable processes with a causal model exist, and give evidence that they have no clear physical interpretation. We also provide an algorithm to generate processes of this kind and show that they have nonzero measure in the set of all processes. We demonstrate the existence of processes which stop violating causal inequalities but are still causally nonseparable when mixed with a certain amount of ‘white noise’. This is reminiscent of the behavior of Werner states in the context of entanglement and nonlocality. Finally, we provide numerical evidence for the existence of causally nonseparable processes which have a causal model even when extended with an entangled state shared among the parties. (paper)

Mental Health and Stressful Life Events in Coronary Heart Disease Patients and Non- Patients

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Samaneh Nateghian

2008-07-01

Full Text Available "nObjective: "n The main goal of this study is to compare stressful life events and mental health in coronary heart disease (CHD patients and non-patients. "nMethod: In this research, 120 participants (n=68 male, n= 52 female were divided into two groups of patients (n=60 and non-patients (n=60. They were selected from Vali Asr Hospital of Meshginshahr (Iran and completed the  General Health Questionnaire (GHQ and Stressful Life Events Inventory . "nResults: Data was analyzed using independent t-test. The results showed significant differences between CHD patients and non-patients in mental health and stressful life events. CHD patients showed high rates of physical symptoms and anxiety of GHQ . "nConclusion: CHD patients reported more stressful life events. Therefore, it can be inferred that psychological factors are important in coronary heart disease.

Bayesian networks improve causal environmental ...

Science.gov (United States)

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Path-space variational inference for non-equilibrium coarse-grained systems

International Nuclear Information System (INIS)

Harmandaris, Vagelis; Kalligiannaki, Evangelia; Katsoulakis, Markos; Plecháč, Petr

2016-01-01

In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities. We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.

Path-space variational inference for non-equilibrium coarse-grained systems

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Harmandaris, Vagelis, E-mail: harman@uoc.gr [Department of Mathematics and Applied Mathematics, University of Crete (Greece); Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas (FORTH), IACM/FORTH, GR-71110 Heraklion (Greece); Kalligiannaki, Evangelia, E-mail: ekalligian@tem.uoc.gr [Department of Mathematics and Applied Mathematics, University of Crete (Greece); Katsoulakis, Markos, E-mail: markos@math.umass.edu [Department of Mathematics and Statistics, University of Massachusetts at Amherst (United States); Plecháč, Petr, E-mail: plechac@math.udel.edu [Department of Mathematical Sciences, University of Delaware, Newark, Delaware (United States)

2016-06-01

In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities. We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.

An empirical analysis of mental state talk and affect regulation in two single-cases of psychodynamic child therapy.

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Halfon, Sibel; Bekar, Ozlem; Gürleyen, Büşra

2017-06-01

Literature has shown the importance of mentalizing techniques in symptom remission and emotional understanding; however, no study to date has looked at the dynamic relations between mental state talk and affect regulation in the psychotherapy process. From a psychodynamic perspective, the emergence of the child's capacity to regulate affect through the therapist's reflection on the child's mental states is a core aspect of treatment. In an empirical investigation of 2 single cases with separation anxiety disorder, who were treated in long-term psychodynamic play therapy informed with mentalization principles, the effect of therapists' and children's use of mental state talk on children's subsequent capacity to regulate affect in play was assessed. One case was a positive outcome case, whereas the other did not show symptomatic improvement at the end of treatment. Children's and therapists' utterances in the sessions were coded using the Coding System for Mental State Talk in Narratives, and children's play was coded by Children's Play Therapy Instrument, which generated an index of children's "affect regulation." Time-series Granger Causality tests showed that even though both therapists' use of mental state talk significantly predicted children's subsequent affect regulation, the association between child's mental state talk and affect regulation was only supported for the child who showed clinically significant symptom reduction. This study provided preliminary support that mental state talk in psychodynamic psychotherapy facilitates emotion regulation in play. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A framework for Bayesian nonparametric inference for causal effects of mediation.

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Kim, Chanmin; Daniels, Michael J; Marcus, Bess H; Roy, Jason A

2017-06-01

We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part 1 is a flexible model (using BNP) for the observed data distribution. Part 2 is a set of uncheckable assumptions with sensitivity parameters that in conjunction with Part 1 allows identification and estimation of the causal parameters and allows for uncertainty about these assumptions via priors on the sensitivity parameters. For Part 1, we specify a Dirichlet process mixture of multivariate normals as a prior on the joint distribution of the outcome, mediator, and covariates. This approach allows us to obtain a (simple) closed form of each marginal distribution. For Part 2, we consider two sets of assumptions: (a) the standard sequential ignorability (Imai et al., 2010) and (b) weakened set of the conditional independence type assumptions introduced in Daniels et al. (2012) and propose sensitivity analyses for both. We use this approach to assess mediation in a physical activity promotion trial. © 2016, The International Biometric Society.

Supporting inquiry learning by promoting normative understanding of multivariable causality

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Keselman, Alla

2003-11-01

Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

Relating the thermodynamic arrow of time to the causal arrow

International Nuclear Information System (INIS)

Allahverdyan, Armen E; Janzing, Dominik

2008-01-01

Consider a Hamiltonian system that consists of a slow subsystem S and a fast subsystem F. The autonomous dynamics of S is driven by an effective Hamiltonian, but its thermodynamics is unexpected. We show that a well-defined thermodynamic arrow of time (second law) emerges for S whenever there is a well-defined causal arrow from S to F and the back-action is negligible. This is because the back-action of F on S is described by a non-globally Hamiltonian Born–Oppenheimer term that violates the Liouville theorem, and makes the second law inapplicable to S. If S and F are mixing, under the causal arrow condition they are described by microcanonical distributions P(S) and P(S|F). Their structure supports a causal inference principle proposed recently in machine learning

A general, multivariate definition of causal effects in epidemiology.

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Flanders, W Dana; Klein, Mitchel

2015-07-01

Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

Evaluating the impact of implementation factors on family-based prevention programming: methods for strengthening causal inference.

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Crowley, D Max; Coffman, Donna L; Feinberg, Mark E; Greenberg, Mark T; Spoth, Richard L

2014-04-01

Despite growing recognition of the important role implementation plays in successful prevention efforts, relatively little work has sought to demonstrate a causal relationship between implementation factors and participant outcomes. In turn, failure to explore the implementation-to-outcome link limits our understanding of the mechanisms essential to successful programming. This gap is partially due to the inability of current methodological procedures within prevention science to account for the multitude of confounders responsible for variation in implementation factors (i.e., selection bias). The current paper illustrates how propensity and marginal structural models can be used to improve causal inferences involving implementation factors not easily randomized (e.g., participant attendance). We first present analytic steps for simultaneously evaluating the impact of multiple implementation factors on prevention program outcome. Then, we demonstrate this approach for evaluating the impact of enrollment and attendance in a family program, over and above the impact of a school-based program, within PROSPER, a large-scale real-world prevention trial. Findings illustrate the capacity of this approach to successfully account for confounders that influence enrollment and attendance, thereby more accurately representing true causal relations. For instance, after accounting for selection bias, we observed a 5% reduction in the prevalence of 11th grade underage drinking for those who chose to receive a family program and school program compared to those who received only the school program. Further, we detected a 7% reduction in underage drinking for those with high attendance in the family program.

Generalizability of causal inference in observational studies under retrospective convenience sampling.

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Hu, Zonghui; Qin, Jing

2018-05-20

Many observational studies adopt what we call retrospective convenience sampling (RCS). With the sample size in each arm prespecified, RCS randomly selects subjects from the treatment-inclined subpopulation into the treatment arm and those from the control-inclined into the control arm. Samples in each arm are representative of the respective subpopulation, but the proportion of the 2 subpopulations is usually not preserved in the sample data. We show in this work that, under RCS, existing causal effect estimators actually estimate the treatment effect over the sample population instead of the underlying study population. We investigate how to correct existing methods for consistent estimation of the treatment effect over the underlying population. Although RCS is adopted in medical studies for ethical and cost-effective purposes, it also has a big advantage for statistical inference: When the tendency to receive treatment is low in a study population, treatment effect estimators under RCS, with proper correction, are more efficient than their parallels under random sampling. These properties are investigated both theoretically and through numerical demonstration. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Dynamical Symmetries and Causality in Non-Equilibrium Phase Transitions

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Malte Henkel

2015-11-01

Full Text Available Dynamical symmetries are of considerable importance in elucidating the complex behaviour of strongly interacting systems with many degrees of freedom. Paradigmatic examples are cooperative phenomena as they arise in phase transitions, where conformal invariance has led to enormous progress in equilibrium phase transitions, especially in two dimensions. Non-equilibrium phase transitions can arise in much larger portions of the parameter space than equilibrium phase transitions. The state of the art of recent attempts to generalise conformal invariance to a new generic symmetry, taking into account the different scaling behaviour of space and time, will be reviewed. Particular attention will be given to the causality properties as they follow for co-variant n-point functions. These are important for the physical identification of n-point functions as responses or correlators.

Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

International Nuclear Information System (INIS)

Neelamkavil, Raphael

2014-01-01

A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

Energy Technology Data Exchange (ETDEWEB)

Neelamkavil, Raphael

2014-07-01

A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

Causal mapping of emotion networks in the human brain: Framework and initial findings.

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Dubois, Julien; Oya, Hiroyuki; Tyszka, J Michael; Howard, Matthew; Eberhardt, Frederick; Adolphs, Ralph

2017-11-13

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI. Copyright © 2017 Elsevier Ltd. All rights reserved.

Theory of mind and preschoolers' understanding of implicit causality in verbs: A comparison between Serbian and Hungarian children

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Agota Major

2010-06-01

Full Text Available This study aims to investigate the effect of theory of mind, age and mother tongue on the implicit causality effect in preschoolers from two different language backgrounds. Serbian and Hungarian native speakers aged 3–7 years participated in the study. After taking part in a Theory of Mind task, children were presented verbs in simple „Subject verb Object” sentences describing interactions between two participants, with the interactions being based on emotional, mental or visual experiences. Children were asked “Why does S verb O?” and their responses were categorized as containing an inference about the sentence-S or the sentence-O. The results show that Theory of Mind is a significant factor in the emergence of implicit causality, with age of participants and mother tongue being also contributing to explaining patterns of implicit causality.

Rational Inference of Beliefs and Desires From Emotional Expressions.

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Wu, Yang; Baker, Chris L; Tenenbaum, Joshua B; Schulz, Laura E

2018-04-01

We investigated people's ability to infer others' mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents' desires throughout. When the agent observed, but did not cause the outcome, participants' ability to recover the agent's beliefs depended on the evidence they got (i.e., her reaction only to the actual outcome or to both the expected and actual outcomes; Experiments 1 and 2). When the agent caused the event, participants' judgments also depended on the probability of the action (Experiments 3 and 4); when actions were improbable given the mental states, people failed to recover the agent's beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout (rs ≥ .95), consistent with the proposal that people rationally integrate information about others' actions and emotional reactions to infer their unobservable mental states. Copyright © 2017 Cognitive Science Society, Inc.

Time-varying causality between energy consumption, CO2 emissions, and economic growth: evidence from US states.

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Tzeremes, Panayiotis

2018-02-01

This study is the first attempt to investigate the relationship between CO 2 emissions, energy consumption, and economic growth at a state level, for the 50 US states, through a time-varying causality approach using annual data over the periods 1960-2010. The time-varying causality test facilitates the better understanding of the causal relationship between the covariates owing to the fact that it might identify causalities when the time-constant hypothesis is rejected. Our findings indicate the existence of a time-varying causality at the state level. Specifically, the results probe eight bidirectional time-varying causalities between energy consumption and CO 2 emission, six cases of two-way time-varying causalities between economic growth and energy consumption, and five bidirectional time-varying causalities between economic growth and CO 2 emission. Moreover, we examine the traditional environmental Kuznets curve hypothesis for the states. Notably, our results do not endorse the validity of the EKC, albeit the majority of states support an inverted N-shaped relationship. Lastly, we can identify multiple policy implications based on the empirical results.

An alternative empirical likelihood method in missing response problems and causal inference.

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Ren, Kaili; Drummond, Christopher A; Brewster, Pamela S; Haller, Steven T; Tian, Jiang; Cooper, Christopher J; Zhang, Biao

2016-11-30

Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified. In this paper, we introduce an empirical likelihood-based estimator as an alternative to Qin and Zhang (2007). Our proposed estimator is also doubly robust and locally efficient. Simulation results show that the proposed estimator has better performance when the propensity score is correctly modeled. Moreover, the proposed method can be applied in the estimation of average treatment effect in observational causal inferences. Finally, we apply our method to an observational study of smoking, using data from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions clinical trial. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

[Dangerous states and mental health disorders: perceptions and reality].

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Tassone-Monchicourt, C; Daumerie, N; Caria, A; Benradia, I; Roelandt, J-L

2010-01-01

Image of Madness was always strongly linked with the notion of "dangerousness", provoking fear and social exclusion, despite the evolution of psychiatric practices and organisation, and the emphasis on user's rights respect. Mediatization and politicization of this issue through news item combining crime and mental illness, reinforce and spread out this perception. This paper presents a review of the litterature on social perceptions associating "dangerousness", "Insanity" and "mental illness", available data about the link between "dangerous states" and "psychiatric disorders", as well as the notion of "dangerousness" and the assessment of "dangerous state" of people suffering or not from psychiatric disorders. MAPPING OF SOCIAL REPRESENTATIONS: The French Survey "Mental Health in General Population: Images and Realities (MHGP)" was carried out between 1999 and 2003, on a representative sample of 36.000 individuals over 18 years old. It aims at describing the social representations of the population about "insanity/insane" and "mental illness/mentally ill". The results show that about 75% of the people interviewed link "insanity" or "mental illness" with "criminal or violent acts". Young people and those with a high level of education more frequently categorize violent and dangerous behaviours in the field of Mental illness rather than in that of madness. CORRELATION BETWEEN DANGEROUS STATE AND PSYCHIATRIC DISORDERS: in the scientific literature, all experts reject the hypothesis of a direct link between violence and mental disorder. Besides, 2 tendencies appear in their conclusions: on one hand, some studies establish a significative link between violence and severe mental illness, compared with the general population. On the other hand, results show that 87 to 97% of des aggressors are not mentally ills. Therefore, the absence of scientific consensus feeds the confusion and reinforce the link of causality between psychiatric disorders and violence. OFFICIAL

Determining Directional Dependency in Causal Associations

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Pornprasertmanit, Sunthud; Little, Todd D.

2012-01-01

Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of…

Programs as Causal Models: Speculations on Mental Programs and Mental Representation

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Chater, Nick; Oaksford, Mike

2013-01-01

Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of…

Finding the Cause: Verbal Framing Helps Children Extract Causal Evidence Embedded in a Complex Scene

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Butler, Lucas P.; Markman, Ellen M.

2012-01-01

In making causal inferences, children must both identify a causal problem and selectively attend to meaningful evidence. Four experiments demonstrate that verbally framing an event ("Which animals make Lion laugh?") helps 4-year-olds extract evidence from a complex scene to make accurate causal inferences. Whereas framing was unnecessary when…

Causal ubiquity in quantum physics a superluminal and local-causal physical ontology

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Neelamkavil, Raphael

2014-01-01

A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly non-causal processes, something exists processually in extension-motion, between the causal and the non-causal. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That

Did She Jump Because She Was the Big Sister or Because the Trampoline Was Safe? Causal Inference and the Development of Social Attribution

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Seiver, Elizabeth; Gopnik, Alison; Goodman, Noah D.

2013-01-01

Children rely on both evidence and prior knowledge to make physical causal inferences; this study explores whether they make attributions about others' behavior in the same manner. A total of one hundred and fifty-nine 4- and 6-year-olds saw 2 dolls interacting with 2 activities, and explained the dolls' actions. In the person condition, each doll…

A review of protective factors and causal mechanisms that enhance the mental health of Indigenous Circumpolar youth

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Joanna Petrasek MacDonald

2013-12-01

Full Text Available Objectives . To review the protective factors and causal mechanisms which promote and enhance Indigenous youth mental health in the Circumpolar North. Study design . A systematic literature review of peer-reviewed English-language research was conducted to systematically examine the protective factors and causal mechanisms which promote and enhance Indigenous youth mental health in the Circumpolar North. Methods . This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines, with elements of a realist review. From 160 records identified in the initial search of 3 databases, 15 met the inclusion criteria and were retained for full review. Data were extracted using a codebook to organize and synthesize relevant information from the articles. Results . More than 40 protective factors at the individual, family, and community levels were identified as enhancing Indigenous youth mental health. These included practicing and holding traditional knowledge and skills, the desire to be useful and to contribute meaningfully to one's community, having positive role models, and believing in one's self. Broadly, protective factors at the family and community levels were identified as positively creating and impacting one's social environment, which interacts with factors at the individual level to enhance resilience. An emphasis on the roles of cultural and land-based activities, history, and language, as well as on the importance of social and family supports, also emerged throughout the literature. Conclusions . Healthy communities and families foster and support youth who are resilient to mental health challenges and able to adapt and cope with multiple stressors, be they social, economic, or environmental. Creating opportunities and environments where youth can successfully navigate challenges and enhance their resilience can in turn contribute to fostering healthy Circumpolar communities. Looking at the

Causal relations among events and states in dynamic geographical phenomena

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Huang, Zhaoqiang; Feng, Xuezhi; Xuan, Wenling; Chen, Xiuwan

2007-06-01

There is only a static state of the real world to be recorded in conventional geographical information systems. However, there is not only static information but also dynamic information in geographical phenomena. So that how to record the dynamic information and reveal the relations among dynamic information is an important issue in a spatio-temporal information system. From an ontological perspective, we can initially divide the spatio-temporal entities in the world into continuants and occurrents. Continuant entities endure through some extended (although possibly very short) interval of time (e.g., houses, roads, cities, and real-estate). Occurrent entities happen and are then gone (e.g., a house repair job, road construction project, urban expansion, real-estate transition). From an information system perspective, continuants and occurrents that have a unique identity in the system are referred to as objects and events, respectively. And the change is represented implicitly by static snapshots in current spatial temporal information systems. In the previous models, the objects can be considered as the fundamental components of the system, and the change is modeled by considering time-varying attributes of these objects. In the spatio-temporal database, the temporal information that is either interval or instant is involved and the underlying data structures and indexes for temporal are considerable investigated. However, there is the absence of explicit ways of considering events, which affect the attributes of objects or the state. So the research issue of this paper focuses on how to model events in conceptual models of dynamic geographical phenomena and how to represent the causal relations among events and the objects or states. Firstly, the paper reviews the conceptual modeling in a temporal GIS by researchers. Secondly, this paper discusses the spatio-temporal entities: objects and events. Thirdly, this paper investigates the causal relations amongst

Resting-state brain networks revealed by granger causal connectivity in frogs.

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Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

2016-10-15

Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Neural mechanisms underlying valence inferences to sound: The role of the right angular gyrus.

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Bravo, Fernando; Cross, Ian; Hawkins, Sarah; Gonzalez, Nadia; Docampo, Jorge; Bruno, Claudio; Stamatakis, Emmanuel Andreas

2017-07-28

We frequently infer others' intentions based on non-verbal auditory cues. Although the brain underpinnings of social cognition have been extensively studied, no empirical work has yet examined the impact of musical structure manipulation on the neural processing of emotional valence during mental state inferences. We used a novel sound-based theory-of-mind paradigm in which participants categorized stimuli of different sensory dissonance level in terms of positive/negative valence. Whilst consistent with previous studies which propose facilitated encoding of consonances, our results demonstrated that distinct levels of consonance/dissonance elicited differential influences on the right angular gyrus, an area implicated in mental state attribution and attention reorienting processes. Functional and effective connectivity analyses further showed that consonances modulated a specific inhibitory interaction from associative memory to mental state attribution substrates. Following evidence suggesting that individuals with autism may process social affective cues differently, we assessed the relationship between participants' task performance and self-reported autistic traits in clinically typical adults. Higher scores on the social cognition scales of the AQ were associated with deficits in recognising positive valence in consonant sound cues. These findings are discussed with respect to Bayesian perspectives on autistic perception, which highlight a functional failure to optimize precision in relation to prior beliefs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessment of network inference methods: how to cope with an underdetermined problem.

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Caroline Siegenthaler

Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.

Mentalizing regions represent distributed, continuous, and abstract dimensions of others' beliefs.

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Koster-Hale, Jorie; Richardson, Hilary; Velez, Natalia; Asaba, Mika; Young, Liane; Saxe, Rebecca

2017-11-01

The human capacity to reason about others' minds includes making causal inferences about intentions, beliefs, values, and goals. Previous fMRI research has suggested that a network of brain regions, including bilateral temporo-parietal junction (TPJ), superior temporal sulcus (STS), and medial prefrontal-cortex (MPFC), are reliably recruited for mental state reasoning. Here, in two fMRI experiments, we investigate the representational content of these regions. Building on existing computational and neural evidence, we hypothesized that social brain regions contain at least two functionally and spatially distinct components: one that represents information related to others' motivations and values, and another that represents information about others' beliefs and knowledge. Using multi-voxel pattern analysis, we find evidence that motivational versus epistemic features are independently represented by theory of mind (ToM) regions: RTPJ contains information about the justification of the belief, bilateral TPJ represents the modality of the source of knowledge, and VMPFC represents the valence of the resulting emotion. These representations are found only in regions implicated in social cognition and predict behavioral responses at the level of single items. We argue that cortical regions implicated in mental state inference contain complementary, but distinct, representations of epistemic and motivational features of others' beliefs, and that, mirroring the processes observed in sensory systems, social stimuli are represented in distinct and distributed formats across the human brain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Study of Noise Canceling Performance of Feedforward Fuzzy-Based ANC System under Non-Causal Condition

DEFF Research Database (Denmark)

Mojallali, Hamed; Izadi-Zamanabadi, Roozbeh; Amini, Rouzbeh

of noise canceling performance of feed-forward fuzzy-based ANC systems for ducts under non-causal condition is presented. For this purpose, we use fuzzy filtered-x algorithm as an adaptive filter and the results are compared with classical filteredx algorithm which is employed under the same conditions......Feed-forward active noise control (ANC) systems act as adaptive systems to control and cancel undesired signals and noises. If the delay in the noise canceling subsystems increases more than the delays in the primary path, non-causal condition will occur in these systems. In this paper, study....... Analysis shows that ANC systems using fuzzy algorithm has better efficiency for noise cancellation in non-causal condition....

Causal uncertainty, claimed and behavioural self-handicapping.

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Thompson, Ted; Hepburn, Jonathan

2003-06-01

Causal uncertainty beliefs involve doubts about the causes of events, and arise as a consequence of non-contingent evaluative feedback: feedback that leaves the individual uncertain about the causes of his or her achievement outcomes. Individuals high in causal uncertainty are frequently unable to confidently attribute their achievement outcomes, experience anxiety in achievement situations and as a consequence are likely to engage in self-handicapping behaviour. Accordingly, we sought to establish links between trait causal uncertainty, claimed and behavioural self-handicapping. Participants were N=72 undergraduate students divided equally between high and low causally uncertain groups. We used a 2 (causal uncertainty status: high, low) x 3 (performance feedback condition: success, non-contingent success, non-contingent failure) between-subjects factorial design to examine the effects of causal uncertainty on achievement behaviour. Following performance feedback, participants completed 20 single-solution anagrams and 12 remote associate tasks serving as performance measures, and 16 unicursal tasks to assess practice effort. Participants also completed measures of claimed handicaps, state anxiety and attributions. Relative to low causally uncertain participants, high causally uncertain participants claimed more handicaps prior to performance on the anagrams and remote associates, reported higher anxiety, attributed their failure to internal, stable factors, and reduced practice effort on the unicursal tasks, evident in fewer unicursal tasks solved. These findings confirm links between trait causal uncertainty and claimed and behavioural self-handicapping, highlighting the need for educators to facilitate means by which students can achieve surety in the manner in which they attribute the causes of their achievement outcomes.

Causal Coherence in the Oral Narratives of Spanish-Speaking Children.

Science.gov (United States)

Gutierrez-Clellen, Vera F.; Iglesias, Aquiles

1992-01-01

Forty-six Spanish-speaking children ages four, six, or eight years viewed a short silent film and told what happened in the film. The stories of older children included more narrative actions, more mental state/goal causes, more three-clause causal sequences, and a lower proportion of unrelated statements than those of younger children.…

Temporal and Statistical Information in Causal Structure Learning

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McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David

2015-01-01

Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…

Energy use, emissions, economic growth and trade: A Granger non-causality evidence for Malaysia

OpenAIRE

Ismail, Mohd Adib; Mawar, Murni Yunus

2012-01-01

This paper investigates the relationship among energy, emissions and economic growth in Malaysia with the presence of trade activities. We employ Johansen’s (1995) approach to investigate the relationship. Using annual data from 1971 to 2007, the empirical results shows that there are long-run causalities among energy, emission and economic growth, and among energy, emissions, export and capital, while the short-run Granger non-causality test shows that there are unidirectional causalities ru...

The power of possibility: causal learning, counterfactual reasoning, and pretend play.

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Buchsbaum, Daphna; Bridgers, Sophie; Skolnick Weisberg, Deena; Gopnik, Alison

2012-08-05

We argue for a theoretical link between the development of an extended period of immaturity in human evolution and the emergence of powerful and wide-ranging causal learning mechanisms, specifically the use of causal models and Bayesian learning. We suggest that exploratory childhood learning, childhood play in particular, and causal cognition are closely connected. We report an empirical study demonstrating one such connection--a link between pretend play and counterfactual causal reasoning. Preschool children given new information about a causal system made very similar inferences both when they considered counterfactuals about the system and when they engaged in pretend play about it. Counterfactual cognition and causally coherent pretence were also significantly correlated even when age, general cognitive development and executive function were controlled for. These findings link a distinctive human form of childhood play and an equally distinctive human form of causal inference. We speculate that, during human evolution, computations that were initially reserved for solving particularly important ecological problems came to be used much more widely and extensively during the long period of protected immaturity.

Weighting-Based Sensitivity Analysis in Causal Mediation Studies

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Hong, Guanglei; Qin, Xu; Yang, Fan

2018-01-01

Through a sensitivity analysis, the analyst attempts to determine whether a conclusion of causal inference could be easily reversed by a plausible violation of an identification assumption. Analytic conclusions that are harder to alter by such a violation are expected to add a higher value to scientific knowledge about causality. This article…

Russell and Humean Inferences

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João Paulo Monteiro

2001-12-01

Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.

Can chance cause cancer? A causal consideration.

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Stensrud, Mats Julius; Strohmaier, Susanne; Valberg, Morten; Aalen, Odd Olai

2017-04-01

The role of randomness, environment and genetics in cancer development is debated. We approach the discussion by using the potential outcomes framework for causal inference. By briefly considering the underlying assumptions, we suggest that the antagonising views arise due to estimation of substantially different causal effects. These effects may be hard to interpret, and the results cannot be immediately compared. Indeed, it is not clear whether it is possible to define a causal effect of chance at all. Copyright © 2017 Elsevier Ltd. All rights reserved.

Do material, psychosocial and behavioural factors mediate the relationship between disability acquisition and mental health? A sequential causal mediation analysis.

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Aitken, Zoe; Simpson, Julie Anne; Gurrin, Lyle; Bentley, Rebecca; Kavanagh, Anne Marie

2018-01-29

There is evidence of a causal relationship between disability acquisition and poor mental health; however, the mechanism by which disability affects mental health is poorly understood. This gap in understanding limits the development of effective interventions to improve the mental health of people with disabilities. We used four waves of data from the Household, Income and Labour Dynamics in Australia Survey (2011-14) to compare self-reported mental health between individuals who acquired any disability (n=387) and those who remained disability-free (n=7936). We tested three possible pathways from disability acquisition to mental health, examining the effect of material, psychosocial and behavioural mediators. The effect was partitioned into natural direct and indirect effects through the mediators using a sequential causal mediation analysis approach. Multiple imputation using chained equations was used to assess the impact of missing data. Disability acquisition was estimated to cause a five-point decline in mental health [estimated mean difference: -5.3, 95% confidence interval (CI) -6.8, -3.7]. The indirect effect through material factors was estimated to be a 1.7-point difference (-1.7, 95% CI -2.8, -0.6), explaining 32% of the total effect, with a negligible proportion of the effect explained by the addition of psychosocial characteristics (material and psychosocial: -1.7, 95% CI -3.0, -0.5) and a further 5% by behavioural factors (material-psychosocial-behavioural: -2.0, 95% CI -3.4, -0.6). The finding that the effect of disability acquisition on mental health operates predominantly through material rather than psychosocial and behavioural factors has important implications. The results highlight the need for better social protection, including income support, employment and education opportunities, and affordable housing for people who acquire a disability. © The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the

Children's Causal Inferences from Conflicting Testimony and Observations

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Bridgers, Sophie; Buchsbaum, Daphna; Seiver, Elizabeth; Griffiths, Thomas L.; Gopnik, Alison

2016-01-01

Preschoolers use both direct observation of statistical data and informant testimony to learn causal relationships. Can children integrate information from these sources, especially when source reliability is uncertain? We investigate how children handle a conflict between what they hear and what they see. In Experiment 1, 4-year-olds were…

A framework for estimating causal effects in latent class analysis: is there a causal link between early sex and subsequent profiles of delinquency?

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Butera, Nicole M; Lanza, Stephanie T; Coffman, Donna L

2014-06-01

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work.

Predictors of Mental Health Resilience in Children who Have Been Parentally Bereaved by AIDS in Urban South Africa.

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Collishaw, Stephan; Gardner, Frances; Lawrence Aber, J; Cluver, Lucie

2016-05-01

Children parentally bereaved by AIDS experience high rates of mental health problems. However, there is considerable variability in outcomes, and some show no mental health problems even when followed over time. Primary aims were to identify predictors of resilient adaptation at child, family and community levels within a group of AIDS-orphaned children, and to consider their cumulative influence. A secondary aim was to test whether predictors were of particular influence among children orphaned by AIDS relative to non-orphaned and other-orphaned children. AIDS-orphaned (n = 290), other-orphaned (n = 163) and non-orphaned (n = 202) adolescents living in informal settlements in Cape Town, South Africa were assessed on two occasions 4 years apart (mean age 13.5 years at Time 1, range = 10-19 years). Self-report mental health screens were used to operationalise resilience in AIDS-orphaned children as the absence of clinical-range symptoms of PTSD, anxiety, depression, conduct problems, and suicidality. A quarter of AIDS-orphaned children (24 %) showed no evidence of mental health problems at either wave. Child physical health, better caregiving quality, food security, better peer relationship quality, and lower exposure to community violence, bullying or stigma at baseline predicted sustained resilience. There were cumulative influences across predictors. Associations with mental health showed little variation by child age or gender, or between orphaned and non-orphaned children. Mental health resilience is associated with multiple processes across child, family and community levels of influence. Caution is needed in making causal inferences.

Qualitative reasoning for biological network inference from systematic perturbation experiments.

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Badaloni, Silvana; Di Camillo, Barbara; Sambo, Francesco

2012-01-01

The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

PPARalpha siRNA-treated expression profiles uncover the causal sufficiency network for compound-induced liver hypertrophy.

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Xudong Dai

2007-03-01

Full Text Available Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs against the gene for peroxisome proliferator-activated receptor alpha (Ppara, our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARalpha-induced liver hypertrophy is supported by their ability to predict non-PPARalpha-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005. Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug

Abnormal resting state effective connectivity within the default mode network in major depressive disorder: A spectral dynamic causal modeling study.

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Li, Liang; Li, Baojuan; Bai, Yuanhan; Liu, Wenlei; Wang, Huaning; Leung, Hoi-Chung; Tian, Ping; Zhang, Linchuan; Guo, Fan; Cui, Long-Biao; Yin, Hong; Lu, Hongbing; Tan, Qingrong

2017-07-01

Understanding the neural basis underlying major depressive disorder (MDD) is essential for the diagnosis and treatment of this mental disorder. Aberrant activation and functional connectivity of the default mode network (DMN) have been consistently found in patients with MDD. It is not known whether effective connectivity within the DMN is altered in MDD. The primary object of this study is to investigate the effective connectivity within the DMN during resting state in MDD patients before and after eight weeks of antidepressant treatment. We defined four regions of the DMN (medial frontal cortex, posterior cingulate cortex, left parietal cortex, and right parietal cortex) for each participant using a group independent component analysis. The coupling parameters reflecting the causal interactions among the DMN regions were estimated using spectral dynamic causal modeling (DCM). Twenty-seven MDD patients and 27 healthy controls were included in the statistical analysis. Our results showed declined influences from the left parietal cortex to other DMN regions in the pre-treatment patients as compared with healthy controls. After eight weeks of treatment, the influence from the right parietal cortex to the posterior cingulate cortex significantly decreased. These findings suggest that the reduced excitatory causal influence of the left parietal cortex is the key alteration of the DMN in patients with MDD, and the disrupted causal influences that parietal cortex exerts on the posterior cingulate cortex is responsive to antidepressant treatment.

Information-Theoretic Inference of Common Ancestors

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Bastian Steudel

2015-04-01

Full Text Available A directed acyclic graph (DAG partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information-theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs, our result can be seen as a quantitative extension of Reichenbach’s principle of common cause to more than two variables. Our conclusions are valid also for non-probabilistic observations, such as binary strings, since we state the proof for an axiomatized notion of “mutual information” that includes the stochastic as well as the algorithmic version.

Viscous causal cosmologies

International Nuclear Information System (INIS)

Novello, M.; Salim, J.M.; Torres, J.; Oliveira, H.P. de

1989-01-01

A set of spatially homogeneous and isotropic cosmological geometries generated by a class of non-perfect is investigated fluids. The irreversibility if this system is studied in the context of causal thermodynamics which provides a useful mechanism to conform to the non-violation of the causal principle. (author) [pt

Structure induction in diagnostic causal reasoning.

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Meder, Björn; Mayrhofer, Ralf; Waldmann, Michael R

2014-07-01

Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences from an effect to its cause should reflect solely the empirically observed conditional probability of cause given effect. We argue against this assumption, as it neglects alternative causal structures that may have generated the sample data. Our structure induction model of diagnostic reasoning takes into account the uncertainty regarding the underlying causal structure. A key prediction of the model is that diagnostic judgments should not only reflect the empirical probability of cause given effect but should also depend on the reasoner's beliefs about the existence and strength of the link between cause and effect. We confirmed this prediction in 2 studies and showed that our theory better accounts for human judgments than alternative theories of diagnostic reasoning. Overall, our findings support the view that in diagnostic reasoning people go "beyond the information given" and use the available data to make inferences on the (unobserved) causal rather than on the (observed) data level. (c) 2014 APA, all rights reserved.

Inductive Reasoning about Causally Transmitted Properties

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Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D.; Tenenbaum, Joshua B.

2008-01-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates'…

Embodied Appraisals and Non-emotional States

Czech Academy of Sciences Publication Activity Database

Hvorecký, Juraj

2010-01-01

Roč. 20, č. 3 (2010), s. 215-223 ISSN 1210-3055 R&D Projects: GA AV ČR(CZ) KJB900090802 Institutional research plan: CEZ:AV0Z90090514 Keywords : embodied appraisal * non-emotional mental states * valence * emotion Subject RIV: AA - Philosophy ; Religion

Causality and headache triggers

Science.gov (United States)

Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.

2013-01-01

Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872

¿CONFIEREN PODERES CAUSALES LOS UNIVERSALES TRASCENDENTES?

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José Tomás Alvarado Marambio

2013-11-01

Full Text Available This work discusses the so-called ‘Eleatic’ argument against the existence of transcendent universals, i. e. universals which does not require instantiation for its existence. The Eleatic Principle states that everything produces a difference in the causal powers of something. As transcendent universals seem not to produce such a difference, transcendent universals seem not to exist. The argument depends crucially on the justification and the interpretation of the Eleatic Principle. It is argued, first, that it is not very clear that the principle is justified, and, second, that there are several alternatives for its interpretation, in relation with the different theories one can endorse about modality or causality. Anti-realist theories of modality or causality are not very appropriate for the understanding of what should be a ‘causal power’. Neither does a realist theory of causality conjoined with a combinatorial theory of possible worlds. A ‘causal power’ seems to be better understood in connection with a realist –non-reductionist– theory of causality and a causal theory of modality. Taken in this way the Eleatic Principle, nonetheless, it is argued that transcendent universals do ‘produce’ a difference in causal powers, for every causal connection requires such universals for its existence.

The causal effect of multitasking on work-related mental health: The more you do, the worse you feel

OpenAIRE

Pikos, Anna Katharina

2017-01-01

This paper analyses whether there is a causal relationship between work-related mental health problems and multitasking, the number of tasks performed at work. The data comes from two cross sectional surveys on the German working population. The empirical strategies uses technological change as an instrument for multitasking. In the first stage, the introduction of new production and information technologies is associated with increases in multitasking. Production technology adoption has larg...

Students Mental Representation of Biology Diagrams/Pictures Conventions Based on Formation of Causal Network

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Sampurno, A. W.; Rahmat, A.; Diana, S.

2017-09-01

Diagrams/pictures conventions is one form of visual media that often used to assist students in understanding the biological concepts. The effectiveness of use diagrams/pictures in biology learning at school level has also been mostly reported. This study examines the ability of high school students in reading diagrams/pictures biological convention which is described by Mental Representation based on formation of causal networks. The study involved 30 students 11th grade MIA senior high school Banten Indonesia who are studying the excretory system. MR data obtained by Instrument worksheet, developed based on CNET-protocol, in which there are diagrams/drawings of nephron structure and urinary mechanism. Three patterns formed MR, namely Markov chain, feedback control with a single measurement, and repeated feedback control with multiple measurement. The third pattern is the most dominating pattern, differences in the pattern of MR reveal the difference in how and from which point the students begin to uncover important information contained in the diagram to establish a causal networks. Further analysis shows that a difference in the pattern of MR relate to how complex the students process the information contained in the diagrams/pictures.

How (not) to interpret a non-causal association in sports injury science.

Science.gov (United States)

Hjerrild, Mette; Videbaek, Solvej; Theisen, Daniel; Malisoux, Laurent; Oestergaard Nielsen, Rasmus

2018-05-16

To discuss the interpretability of non-causal associations to sports injury development exemplified via the relationship between navicular drop (ND) and running-related injury (RRI) in novice runners using neutral shoes. 1-year prospective cohort study. Denmark. 926 novice runners, representing 1852 feet, were included. The outcome was "a musculoskeletal complaint of the lower extremity or back caused by running, which restricted the amount of running for at least a week". Fewer feet with small ND than those feet with a reference ND sustained injuries at 50 (risk difference (RD) = -4.1% [95%CI = -7.9%;-0.4%]) and 100 km (RD = -5.3% [95%CI = -9.9%;-0.7%]). Similarly, fewer feet with a large ND sustained injuries than the feet with a reference drop at 250 (RD = -7.6% [95%CI = -14.9%;-0.3%]) and 500 km (RD = -9.8% [95%CI = -19.1%;-0.4%]). Non-causal associations can help to identify sub-groups of athletes at an increased or decreased risk of sports injury. Based on the current results, those with a small or large navicular drop sustain fewer injuries than those with a reference drop. Importantly, navicular drop does not cause RRIs, but influences the relationship between training load and RRI. This illustrates that non-causal associations are unsuitable to respond to the question: Why do sports injury develop? Copyright © 2018 Elsevier Ltd. All rights reserved.

Agency, time and causality

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Thomas eWidlok

2014-11-01

Full Text Available Cognitive Scientists interested in causal cognition increasingly search for evidence from non-WEIRD people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition.

A case report using the mental state examination scale (MSES): a tool for measuring change in mental state.

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Fernando, Irosh; Carter, Gregory

2016-02-01

There is a need for a simple and brief tool that can be used in routine clinical practice for the quantitative measurement of mental state across all diagnostic groups. The main utilities of such a tool would be to provide a global metric for the mental state examination, and to monitor the progression over time using this metric. We developed the mental state examination scale (MSES), and used it in an acute inpatient setting in routine clinical work to test its initial feasibility. Using a clinical case, the utility of MSES is demonstrated in this paper. When managing the patient described, the MSES assisted the clinician to assess the initial mental state, track the progress of the recovery, and make timely treatment decisions by quantifying the components of the mental state examination. MSES may enhance the quality of clinical practice for clinicians, and potentially serve as an index of universal mental healthcare outcome that can be used in clinical practice, service evaluation, and healthcare economics. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Neurotization indicators and state of mental desadaptation in personnel of internal affairs organs

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Vyshnichenko S.I.

2013-06-01

Full Text Available The article describes the data of psychological testing of personnel of internal affairs organs, using LNP test: levels of neuroticism and psychopathsation, prevalence of levels of neuroticism among the personnel, the relationship between neuroticism level and clinical groups (mentally healthy, with burnout syndrome and with non-psychotic mental disorders. Level of neuroticism reflects both dynamic and static (states and properties personality characteristics, i.e. neuroticism is elective personality variable. The clinical picture is characterized by manifestations of asthenonevurotic and psycho-vegetative syndromes. More often among those with non-psychotic mental disorders a high (100% level of neuroticism, increased (87.5% and in the zone of uncertain diagnosis (50% occur, than among those with burnout syndrome, and lower than normal (36.36% and low (19.08% neuroticism level – more often among with burnout syndrome, than in those with non-psychotic mental disorders. Level of neuroticism on the verge of normal and pathological conditions occurs mostly in people with burnout syndrome (50% and non-psychotic mental disorders (50%.

Summarizing Simulation Results using Causally-relevant States

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Parikh, Nidhi; Marathe, Madhav; Swarup, Samarth

2016-01-01

As increasingly large-scale multiagent simulations are being implemented, new methods are becoming necessary to make sense of the results of these simulations. Even concisely summarizing the results of a given simulation run is a challenge. Here we pose this as the problem of simulation summarization: how to extract the causally-relevant descriptions of the trajectories of the agents in the simulation. We present a simple algorithm to compress agent trajectories through state space by identifying the state transitions which are relevant to determining the distribution of outcomes at the end of the simulation. We present a toy-example to illustrate the working of the algorithm, and then apply it to a complex simulation of a major disaster in an urban area. PMID:28042620

What Should Be the Roles of Conscious States and Brain States in Theories of Mental Activity?**

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Dulany, Donelson E.

2011-01-01

Answers to the title’s question have been influenced by a history in which an early science of consciousness was rejected by behaviourists on the argument that this entails commitment to ontological dualism and “free will” in the sense of indeterminism. This is, however, a confusion of theoretical assertions with metaphysical assertions. Nevertheless, a legacy within computational and information-processing views of mind rejects or de-emphasises a role for consciousness. This paper sketches a mentalistic metatheory in which conscious states are the sole carriers of symbolic representations, and thus have a central role in the explanation of mental activity and action-while specifying determinism and materialism as useful working assumptions. A mentalistic theory of causal learning, experimentally examined with phenomenal reports, is followed by examination of these questions: Are there common roles for phenomenal reports and brain imaging? Is there defensible evidence for unconscious brain states carrying symbolic representations? Are there interesting dissociations within consciousness? PMID:21694964

Causal reports: Context-dependent contributions of intuitive physics and visual impressions of launching.

Science.gov (United States)

Vicovaro, Michele

2018-05-01

Everyday causal reports appear to be based on a blend of perceptual and cognitive processes. Causality can sometimes be perceived automatically through low-level visual processing of stimuli, but it can also be inferred on the basis of an intuitive understanding of the physical mechanism that underlies an observable event. We investigated how visual impressions of launching and the intuitive physics of collisions contribute to the formation of explicit causal responses. In Experiment 1, participants observed collisions between realistic objects differing in apparent material and hence implied mass, whereas in Experiment 2, participants observed collisions between abstract, non-material objects. The results of Experiment 1 showed that ratings of causality were mainly driven by the intuitive physics of collisions, whereas the results of Experiment 2 provide some support to the hypothesis that ratings of causality were mainly driven by visual impressions of launching. These results suggest that stimulus factors and experimental design factors - such as the realism of the stimuli and the variation in the implied mass of the colliding objects - may determine the relative contributions of perceptual and post-perceptual cognitive processes to explicit causal responses. A revised version of the impetus transmission heuristic provides a satisfactory explanation for these results, whereas the hypothesis that causal responses and intuitive physics are based on the internalization of physical laws does not. Copyright © 2018 Elsevier B.V. All rights reserved.

The mistake of the causal relationship

Directory of Open Access Journals (Sweden)

О. Д. Комаров

2015-03-01

Full Text Available The article deals with issues of the mistake of the causal relationship. The modern criminal law science approaches to the content of the mistake of the causal relationship and its significance to the qualification of the crime are described. It is proved that in cases of dolus generalis different mental attitude of the guilty person to two separate acts of his conduct exist. Consequently, in mentioned above cases mistake of the causal relationship does not have place. The rules of qualification of the crimes commited with the mistake of causation and in cases of dolus generalis are proposed .

Stigma and Mental Illness: Investigating Attitudes of Mental Health and Non-Mental-Health Professionals and Trainees

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Smith, Allison L.; Cashwell, Craig S.

2010-01-01

The authors explored attitudes toward adults with mental illness. Results suggest that mental health trainees and professionals had less stigmatizing attitudes than did non-mental-health trainees and professionals. Professionals receiving supervision had higher mean scores on the Benevolence subscale than did professionals who were not receiving…

A mental architecture modeling of inference of sensory stimuli to the teaching of the deaf

Directory of Open Access Journals (Sweden)

Rubens Santos Guimaraes

2016-10-01

Full Text Available The transmission and retention of knowledge rests on the cognitive faculty of the concepts linked to it. The repeatability of your applications builds a solid foundation for Education, according to cultural and behavioral standards set by the Society. This cognitive ability to infer on what we observe and perceive, regarded as intrinsic human beings, independent of their physical capacity. This article presents a conceptual model Mental Architecture Digitized – AMD, enabling reproduce inferences about sensory stimuli deaf, focused on the implementation of a web system that aims to improve the teaching and learning of students with hearing disability. In this proposal, we evaluate the contextual aspects experienced by learners during the interactions between the constituent elements of a study session, based on experiments with two deaf students enrolled in regular high school. The results allow us to infer the potential of a computer communications environment to expand the possibilities of social inclusion of these students.

State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

Directory of Open Access Journals (Sweden)

Tuqyah Abdullah Al Qazlan

2015-01-01

Full Text Available To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

A review of protective factors and causal mechanisms that enhance the mental health of Indigenous Circumpolar youth.

Science.gov (United States)

MacDonald, Joanna Petrasek; Ford, James D; Willox, Ashlee Cunsolo; Ross, Nancy A

2013-12-09

To review the protective factors and causal mechanisms which promote and enhance Indigenous youth mental health in the Circumpolar North. A systematic literature review of peer-reviewed English-language research was conducted to systematically examine the protective factors and causal mechanisms which promote and enhance Indigenous youth mental health in the Circumpolar North. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with elements of a realist review. From 160 records identified in the initial search of 3 databases, 15 met the inclusion criteria and were retained for full review. Data were extracted using a codebook to organize and synthesize relevant information from the articles. More than 40 protective factors at the individual, family, and community levels were identified as enhancing Indigenous youth mental health. These included practicing and holding traditional knowledge and skills, the desire to be useful and to contribute meaningfully to one's community, having positive role models, and believing in one's self. Broadly, protective factors at the family and community levels were identified as positively creating and impacting one's social environment, which interacts with factors at the individual level to enhance resilience. An emphasis on the roles of cultural and land-based activities, history, and language, as well as on the importance of social and family supports, also emerged throughout the literature. More than 40 protective factors at the individual, family, and community levels were identified as enhancing Indigenous youth mental health. These included practicing and holding traditional knowledge and skills, the desire to be useful and to contribute meaningfully to one's community, having positive role models, and believing in one's self. Broadly, protective factors at the family and community levels were identified as positively creating and impacting one's social

Knowledge and causal attributions for mental disorders in HIV-positive children and adolescents: results from rural and urban Uganda.

Science.gov (United States)

Nalukenge, W; Martin, F; Seeley, J; Kinyanda, E

2018-05-02

Increasing availability of antiretroviral treatment (ART) has led HIV to be considered a chronic disease, shifting attention to focus on quality of life including mental wellbeing. We investigated knowledge and causal attributions for mental disorders in HIV-positive children and adolescents in rural and urban Uganda. This qualitative study was nested in an epidemiological mental health study among HIV-positive children and adolescents aged 5-17 years in rural and urban Uganda. In-depth interviews were conducted with caregivers of HIV-positive children (5-11 years) and adolescents (12-17 years) in HIV care. Interviews were audio recorded with permission from participants and written consent and assent sought before study procedures. Thirty eight participants (19 caregivers, 19 children/adolescents) were interviewed. Age range of caregivers was 28-69 years; majority were female (17). Caregivers had little knowledge on mental disorders ;only 3 related the vignette to a mental problem  and attributed it to: improper upbringing, violence, poverty and bereavement. Five adolescents identified vignettes as portraying mental disorders caused by: ill-health of parents, bereavement, child abuse, discrimination, HIV and poverty. Caregivers are not knowledgeable about behavioural and emotional challenges in HIV-positive children/adolescents. Mental health literacy programmes at HIV care clinics are essential to enhance treatment-seeking for mental health.

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

Science.gov (United States)

Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

2016-01-01

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894

An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

KAUST Repository

Zenil, Hector

2017-09-08

We introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information. By applying sequences of controlled interventions to systems and networks, we estimate how changes in their algorithmic information content are reflected in positive/negative shifts towards and away from randomness. The strong connection between approximations to algorithmic complexity (the size of the shortest generating mechanism) and causality induces a sequence of perturbations ranking the network elements by the steering capabilities that each of them is capable of. This new dimension unmasks a separation between causal and non-causal components providing a suite of powerful parameter-free algorithms of wide applicability ranging from optimal dimension reduction, maximal randomness analysis and system control. We introduce methods for reprogramming systems that do not require the full knowledge or access to the system\\'s actual kinetic equations or any probability distributions. A causal interventional analysis of synthetic and regulatory biological networks reveals how the algorithmic reprogramming qualitatively reshapes the system\\'s dynamic landscape. For example, during cellular differentiation we find a decrease in the number of elements corresponding to a transition away from randomness and a combination of the system\\'s intrinsic properties and its intrinsic capabilities to be algorithmically reprogrammed can reconstruct an epigenetic landscape. The interventional calculus is broadly applicable to predictive causal inference of systems such as networks and of relevance to a variety of machine and causal learning techniques driving model-based approaches to better understanding and manipulate complex systems.

Examining the reliability and validity of the Hebrew version of the Mini Mental State Examination.

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Werner, P; Heinik, J; Mendel, A; Reicher, B; Bleich, A

1999-10-01

The Mini Mental State Examination is used worldwide for the screening and diagnosis of dementia. The aim of the present study was to examine the reliability and validity of the Hebrew version of the Mini Mental State Examination. The Hebrew version of the Mini Mental State Examination was administered to 36 demented and 19 non-demented elderly persons. Test-retest reliability scores were calculated as exact agreement rates, and ranged from good to excellent for all the items. Strong convergent validity, as measured by the correlation between the MMSE and the CAM-COG (r = 0.94), was found. Good predictive value was observed as over three-quarters of the participants were correctly classified as demented or non-demented. The Hebrew version of the MMSE was found to be a useful and valid instrument for the determination of dementia in the elderly population.

Selecting appropriate cases when tracing causal mechanisms

DEFF Research Database (Denmark)

Beach, Derek; Pedersen, Rasmus Brun

2016-01-01

The last decade has witnessed resurgence in the interest in studying the causal mechanisms linking causes and outcomes in the social sciences. This article explores the overlooked implications for case selection when tracing mechanisms using in-depth case studies. Our argument is that existing case...... selection guidelines are appropriate for research aimed at making cross-case claims about causal relationships, where case selection is primarily used to control for other causes. However, existing guidelines are not in alignment with case-based research that aims to trace mechanisms, where the goal...... is to unpack the causal mechanism between X and Y, enabling causal inferences to be made because empirical evidence is provided for how the mechanism actually operated in a particular case. The in-depth, within-case tracing of how mechanisms operate in particular cases produces what can be termed mechanistic...

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

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Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

A prospective study of diet quality and mental health in adolescents.

Directory of Open Access Journals (Sweden)

Felice N Jacka

Full Text Available A number of cross-sectional and prospective studies have now been published demonstrating inverse relationships between diet quality and the common mental disorders in adults. However, there are no existing prospective studies of this association in adolescents, the onset period of most disorders, limiting inferences regarding possible causal relationships.In this study, 3040 Australian adolescents, aged 11-18 years at baseline, were measured in 2005-6 and 2007-8. Information on diet and mental health was collected by self-report and anthropometric data by trained researchers.There were cross-sectional, dose response relationships identified between measures of both healthy (positive and unhealthy (inverse diets and scores on the emotional subscale of the Pediatric Quality of Life Inventory (PedsQL, where higher scores mean better mental health, before and after adjustments for age, gender, socio-economic status, dieting behaviours, body mass index and physical activity. Higher healthy diet scores at baseline also predicted higher PedsQL scores at follow-up, while higher unhealthy diet scores at baseline predicted lower PedsQL scores at follow-up. Improvements in diet quality were mirrored by improvements in mental health over the follow-up period, while deteriorating diet quality was associated with poorer psychological functioning. Finally, results did not support the reverse causality hypothesis.This study highlights the importance of diet in adolescence and its potential role in modifying mental health over the life course. Given that the majority of common mental health problems first manifest in adolescence, intervention studies are now required to test the effectiveness of preventing the common mental disorders through dietary modification.

Space/time non-commutative field theories and causality

International Nuclear Information System (INIS)

Bozkaya, H.; Fischer, P.; Pitschmann, M.; Schweda, M.; Grosse, H.; Putz, V.; Wulkenhaar, R.

2003-01-01

As argued previously, amplitudes of quantum field theories on non-commutative space and time cannot be computed using naive path integral Feynman rules. One of the proposals is to use the Gell-Mann-Low formula with time-ordering applied before performing the integrations. We point out that the previously given prescription should rather be regarded as an interaction-point time-ordering. Causality is explicitly violated inside the region of interaction. It is nevertheless a consistent procedure, which seems to be related to the interaction picture of quantum mechanics. In this framework we compute the one-loop self-energy for a space/time non-commutative φ 4 theory. Although in all intermediate steps only three-momenta play a role, the final result is manifestly Lorentz covariant and agrees with the naive calculation. Deriving the Feynman rules for general graphs, we show, however, that such a picture holds for tadpole lines only. (orig.)

42 CFR 431.620 - Agreement with State mental health authority or mental institutions.

Science.gov (United States)

2010-10-01

... 42 Public Health 4 2010-10-01 2010-10-01 false Agreement with State mental health authority or mental institutions. 431.620 Section 431.620 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES... GENERAL ADMINISTRATION Relations With Other Agencies § 431.620 Agreement with State mental health...

Emoticons vs. Emojis on Twitter: A Causal Inference Approach

OpenAIRE

Pavalanathan, Umashanthi; Eisenstein, Jacob

2015-01-01

Online writing lacks the non-verbal cues present in face-to-face communication, which provide additional contextual information about the utterance, such as the speaker's intention or affective state. To fill this void, a number of orthographic features, such as emoticons, expressive lengthening, and non-standard punctuation, have become popular in social media services including Twitter and Instagram. Recently, emojis have been introduced to social media, and are increasingly popular. This r...

Mini mental state examination. Validering af en ny dansk udgave

DEFF Research Database (Denmark)

Korner, E.A.; Lauritzen, L.; Nilsson, F.M.

2008-01-01

INTRODUCTION: The Mini Mental State Examination (MMSE) is widely used in Denmark, but often in non-validated versions. In 2000 a cross-sectional workgroup decided on a new common version of the MMSE with a corresponding manual, which is validated for the first time in the present study. MATERIALS...

Mental state talk by Danish preschool children

Directory of Open Access Journals (Sweden)

Ane Knüppel

2008-02-01

Full Text Available Sixteen 4 to 6-year-old Danish children were video-recorded, while interacting spontaneously with their family in their homes. The mental state talk of the children was identified and analysed with respect to three mental domains: desire, feeling and cognition, and was compared to data from a similar study carried out with Canadian families (Jenkins et al., 2003. Our results suggest some cross-cultural differences in children’s mental state talk. First, Danish children produce a larger variation of mental state talk words than Canadian children do, and second, the distribution of mental state talk across the three domains differed for the two language groups. Semantic variation between Danish and English was identified in the study, which may partly explain the findings. Furthermore we present a usage-based approach to the investigation of children’s development of psychological categories in language as well as cross-linguistically.

Self-rated mental health and race/ethnicity in the United States: support for the epidemiological paradox

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Alexis R. Santos-Lozada

2016-09-01

Full Text Available This paper evaluates racial/ethnic differences in self-rated mental health for adults in the United States, while controlling for demographic and socioeconomic characteristics as well as length of stay in the country. Using data from the 2010 National Health Interview Survey Cancer Control Supplement (NHIS-CCS, binomial logistic regression models are fit to estimate the association between race/ethnicity and poor/fair self-reported mental health among US Adults. The size of the analytical sample was 22,844 persons. Overall prevalence of poor/fair self-rated mental health was 7.72%, with lower prevalence among Hispanics (6.93%. Non-Hispanic blacks had the highest prevalence (10.38%. After controls for socioeconomic characteristics are incorporated in the models, Hispanics were found to have a lower probability of reporting poor/fair self-rated mental health in comparison to non-Hispanic whites (OR = 0.70; 95% CI [0.55–0.90]. No difference was found for other minority groups when compared to the reference group in the final model. Contrary to global self-rated health, Hispanics were found to have a lower probability of reporting poor/fair self-rated mental health in comparison to non-Hispanic whites. No difference was found for non-Hispanic blacks when they were compared to non-Hispanic whites. Self-rated mental health is therefore one case of a self-rating of health in which evidence supporting the epidemiological paradox is found among adults in the United States.

The impact of the UK National Minimum Wage on mental health

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Christoph Kronenberg

2017-12-01

Full Text Available Despite an emerging literature, there is still sparse and mixed evidence on the wider societal benefits of Minimum Wage policies, including their effects on mental health. Furthermore, causal evidence on the relationship between earnings and mental health is limited. We focus on low-wage earners, who are at higher risk of psychological distress, and exploit the quasi-experiment provided by the introduction of the UK National Minimum Wage (NMW to identify the causal impact of wage increases on mental health. We employ difference-in-differences models and find that the introduction of the UK NMW had no effect on mental health. Our estimates do not appear to support earlier findings which indicate that minimum wages affect mental health of low-wage earners. A series of robustness checks accounting for measurement error, as well as treatment and control group composition, confirm our main results. Overall, our findings suggest that policies aimed at improving the mental health of low-wage earners should either consider the non-wage characteristics of employment or potentially larger wage increases.

The impact of the UK National Minimum Wage on mental health.

Science.gov (United States)

Kronenberg, Christoph; Jacobs, Rowena; Zucchelli, Eugenio

2017-12-01

Despite an emerging literature, there is still sparse and mixed evidence on the wider societal benefits of Minimum Wage policies, including their effects on mental health. Furthermore, causal evidence on the relationship between earnings and mental health is limited. We focus on low-wage earners, who are at higher risk of psychological distress, and exploit the quasi-experiment provided by the introduction of the UK National Minimum Wage (NMW) to identify the causal impact of wage increases on mental health. We employ difference-in-differences models and find that the introduction of the UK NMW had no effect on mental health. Our estimates do not appear to support earlier findings which indicate that minimum wages affect mental health of low-wage earners. A series of robustness checks accounting for measurement error, as well as treatment and control group composition, confirm our main results. Overall, our findings suggest that policies aimed at improving the mental health of low-wage earners should either consider the non-wage characteristics of employment or potentially larger wage increases.

Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

Science.gov (United States)

Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

2009-12-01

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet.

Science.gov (United States)

Glenn, Tasha; Monteith, Scott

2014-12-01

With the rapid and ubiquitous acceptance of new technologies, algorithms will be used to estimate new measures of mental state and behavior based on digital data. The algorithms will analyze data collected from sensors in smartphones and wearable technology, and data collected from Internet and smartphone usage and activities. In the future, new medical measures that assist with the screening, diagnosis, and monitoring of psychiatric disorders will be available despite unresolved reliability, usability, and privacy issues. At the same time, similar non-medical commercial measures of mental state are being developed primarily for targeted advertising. There are societal and ethical implications related to the use of these measures of mental state and behavior for both medical and non-medical purposes.

Diagnostic causal reasoning with verbal information.

Science.gov (United States)

Meder, Björn; Mayrhofer, Ralf

2017-08-01

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people's inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., "X occasionally causes A"). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes' prior probabilities and the effects' likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework. Copyright © 2017 Elsevier Inc. All rights reserved.

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

Science.gov (United States)

DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Variable selection for confounder control, flexible modeling and Collaborative Targeted Minimum Loss-based Estimation in causal inference

Science.gov (United States)

Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan

2015-01-01

This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129

Thematic Reasoning and Theory of Mind. Accounting for Social Inference Difficulties in Schizophrenia

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Rhiannon Corcoran

2005-01-01

Full Text Available Background Corcoran (2000, 2001 has suggested that theory of mind judgements can be arrived at using analogical reasoning skills and she has proposed that this is the route that people with schizophrenia take when they make inferences about others' mental states. Recent work has demonstrated a robust relationship between mental state inference and autobiographical memory, providing initial support for the model. This study examines the model further by exploring the assertion that in schizophrenia the ability to infer the mental states of others also depends upon effective social reasoning in conditional contexts. Method 59 people with a DSM IV diagnosis of schizophrenia and 44 healthy subjects performed four versions of the thematic selection task. The versions varied according to the familiarity and social nature of the material they incorporated. The same subjects also completed the Hinting Task, a measure of theory of mind and tests of intellectual functioning and narrative recall. Results The schizophrenia and the normal control groups differed in their performance on all of the measures except that of intellectual functioning. Explorations within the schizophrenia group indicated that social reasoning was most markedly affected in the patients with negative signs and in those with paranoid delusions while for the hinting task, those with negative signs performed significantly worse than those in remission but this difference seemed to be due to these patients' poorer narrative memory. There was evidence in the schizophrenia data to support the hypothesis of a relationship between theory of mind and social conditional reasoning. Conclusion This work provided further support for the idea that in patients with schizophrenia at least, judgements about the mental states of others are achieved using analogical reasoning.

Quantum Steering Beyond Instrumental Causal Networks

Science.gov (United States)

Nery, R. V.; Taddei, M. M.; Chaves, R.; Aolita, L.

2018-04-01

We theoretically predict, and experimentally verify with entangled photons, that outcome communication is not enough for hidden-state models to reproduce quantum steering. Hidden-state models with outcome communication correspond, in turn, to the well-known instrumental processes of causal inference but in the one-sided device-independent scenario of one black-box measurement device and one well-characterized quantum apparatus. We introduce one-sided device-independent instrumental inequalities to test against these models, with the appealing feature of detecting entanglement even when communication of the black box's measurement outcome is allowed. We find that, remarkably, these inequalities can also be violated solely with steering, i.e., without outcome communication. In fact, an efficiently computable formal quantifier—the robustness of noninstrumentality—naturally arises, and we prove that steering alone is enough to maximize it. Our findings imply that quantum theory admits a stronger form of steering than known until now, with fundamental as well as practical potential implications.

Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework.

Science.gov (United States)

Daemi, Mehdi; Harris, Laurence R; Crawford, J Douglas

2016-01-01

Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory cortices based on retinal and cochlear stimulations. Currently, it is not known how the brain compares the temporal and spatial features of these sensory representations to decide whether they originate from the same or separate sources in space. Here, we propose a computational model of how the brain might solve such a task. We reduce the visual and auditory information to time-varying, finite-dimensional signals. We introduce controlled, leaky integrators as working memory that retains the sensory information for the limited time-course of task implementation. We propose our model within an evidence-based, decision-making framework, where the alternative plan units are saliency maps of space. A spatiotemporal similarity measure, computed directly from the unimodal signals, is suggested as the criterion to infer common or separate causes. We provide simulations that (1) validate our model against behavioral, experimental results in tasks where the participants were asked to report common or separate causes for cross-modal stimuli presented with arbitrary spatial and temporal disparities. (2) Predict the behavior in novel experiments where stimuli have different combinations of spatial, temporal, and reliability features. (3) Illustrate the dynamics of the proposed internal system. These results confirm our spatiotemporal similarity measure as a viable criterion for causal inference, and our decision-making framework as a viable mechanism for target selection, which may be used by the brain in cross-modal situations. Further, we suggest that a similar approach can be extended to other cognitive problems where working memory is a limiting factor, such

The effect of military deployment on mental health

DEFF Research Database (Denmark)

Vincent, Stéphanie; Weatherall, Cecilie Dohlmann; W. Jepsen, Peter

2016-01-01

Public concern about soldiers’ mental health has increased over the last decade. Yet the large literature on the mental health problems of returning soldiers relies primarily on self-reported measures that may suffer from non-response bias, usually refers to older conflicts, and focuses mainly...... on specific diagnoses such as PTSD. Another challenge is that the differences between soldiers and non-soldiers are not necessarily causal, instead possibly reflecting an underlying propensity towards active military service. Using the objective measures of hospitalizations and the purchase of mental health...... medication, this paper is the first to investigate the effect of recent military deployments on a broader measure of mental health, for a full population of Danish soldiers and a comparison group of eligible men. We exploit a panel of Danish health administrative records and use propensity score matching...

Causal boundary for strongly causal spacetimes: Pt. 1

International Nuclear Information System (INIS)

Szabados, L.B.

1989-01-01

In a previous paper an analysis of the general structure of the causal boundary constructions and a new explicit identification rule, built up from elementary TIP-TIF gluings, were presented. In the present paper we complete our identification by incorporating TIP-TIP and TIF-TIF gluings as well. An asymptotic causality condition is found which, for physically important cases, ensures the uniqueness of the endpoints of the non-spacelike curves in the completed spacetime. (author)

The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference

Science.gov (United States)

Serang, Oliver

2014-01-01

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234

Features of Speech Reactions to Mental State Concepts

Directory of Open Access Journals (Sweden)

Ekaterina M. Alekseeva

2017-11-01

Full Text Available The article is devoted to the problem of mental state associative speech representation. The study involved 31 Russian-speaking subjects (27 females and 4 males at the age of 18 - 22 years old. The experimental procedure using DMDX program allowed to measure the time of speech response to stimuli - the concepts of 25 mental states. The average reaction time to the concepts of mental states, shown on the computer monitor, made 2114.68 milliseconds. The most rapid associative speech response was the response to the following stimuli: "ecstasy" (1452.54 msec, "meditation" (1569.26 msec, "tranquility" (1685.21 msec, the slowest response is the response to "interest" (2517.5 msec and "indecision" (2454.63 msec. In total, 448 associations were given to the concepts of 25 mental states by tested subjects - speech reactions, i.e. 17.9 associations per mental state on the average. The greatest number of speech associations (24 was given to the concept of love. The smallest number was given to the concept of ecstasy (11 associations. Associative fields of mental states (meditation, ecstasy, melancholy, tiredness, loneliness have the most pronounced core. The prospects of the study consist in the performance of a similar associative experiment among the representatives of another culture, as well as in the studying of an estimated and situational associative representation of mental states.

Comparison of mental health, happiness, and emotion control with adolescents’ residential centers of state welfare organization and family reared adolescents

Directory of Open Access Journals (Sweden)

Laila Bawi

2016-03-01

Full Text Available Background and Objective: Many research indicated that adolescents’ residential centers have the high possibility to diagnose with psychological disorders. Therefore, the aim of this study was investigated the mental health, happiness and emotion control among adolescents’ residential centers of state welfare organization.Materials and Methods: This research is a causal –comparative research. The 80 adolescents’ residential centers were chosen through available sampling and 80 adolescents of schools of Alborz city were selected through cluster method. Statistical analysis was conducted by using the independent t-test. The research instruments were Emotion Control Questionnaire (ECQ, General Health Questionnaire (GHQ, Goldenberg, and Oxford Happiness Inventory (OHI.Results: The significantly different was observed in mental health, happiness and emotion control between two adolescents groups (p<0.05.Conclusion: The results indicate that the institutional-reared decrease the level of mental health, happiness and emotion control in adolescents. Thus, counselors should be considered these factors in therapeutic intervention to enhancing the mental health of adolescents’ residential centers.

The effectiveness of using non-traditional teaching methods to prepare student health care professionals for the delivery of mental state examination: a systematic review.

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Xie, Huiting; Liu, Lei; Wang, Jia; Joon, Kum Eng; Parasuram, Rajni; Gunasekaran, Jamuna; Poh, Chee Lien

2015-08-14

With the evolution of education, there has been a shift from the use of traditional teaching methods, such as didactic or rote teaching, towards non-traditional teaching methods, such as viewing of role plays, simulation, live interviews and the use of virtual environments. Mental state examination is an essential competency for all student healthcare professionals. If mental state examination is not taught in the most effective manner so learners can comprehend its concepts and interpret the findings correctly, it could lead to serious repercussions and subsequently impact on clinical care provided for patients with mental health conditions, such as incorrect assessment of suicidal ideation. However, the methods for teaching mental state examination vary widely between countries, academic institutions and clinical settings. This systematic review aimed to identify and synthesize the best available evidence of effective teaching methods used to prepare student health care professionals for the delivery of mental state examination. This review considered evidence from primary quantitative studies which address the effectiveness of a chosen method used for the teaching of mental state examination published in English, including studies that measure learner outcomes, i.e. improved knowledge and skills, self-confidence and learners' satisfaction. A three-step search strategy was undertaken in this review to search for articles published in English from the inception of the database to December 2014. An initial search of MEDLINE and CINAHL was undertaken to identify keywords. Secondly, the keywords identified were used to search electronic databases, namely, CINAHL, Medline, Cochrane Central Register of Controlled Trials, Ovid, PsycINFO and, ProQuest Dissertations & Theses. Thirdly, reference lists of the articles identified in the second stage were searched for other relevant studies. Studies selected were assessed by two independent reviewers for methodological

Opinion Dynamics on Networks with Inference of Unobservable States of Others

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Fujie, Ryo

In most opinion formation models which have been proposed, the agents decide their states (i.e. opinions) by referring to the states of others. However, the referred states of others are not necessarily observable and may be inferred. To investigate the effect of an inference of the states of others on opinion dynamics, I propose an extended voter model on networks where observable and referable node sets are different. These sets for a node defined as the nearest to the mo-th neighbors for observable nodes and the nearest to the mr-th neighbors for referable nodes. The state of referable but unobservable node which is the m-th neighbor (mo pagerank'' is conserved. This conserved quantity coincides with the fixation probability. On the other hand, in the case of mo =mr = 1 , the model comes down to the standard voter model on networks and the conserved quantity is a degree-weighted superposition of the states. Thus, the introduction of the inference changes the important opinion spreaders from the high-degree nodes to the high-betweenness pagerank nodes. This work is supported by the Collaboration Research Program of IDEAS, Chubu University IDEAS2016233.

Mental state attribution and the temporoparietal junction: an fMRI study comparing belief, emotion, and perception.

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Zaitchik, Deborah; Walker, Caren; Miller, Saul; LaViolette, Pete; Feczko, Eric; Dickerson, Bradford C

2010-07-01

By age 2, children attribute referential mental states such as perceptions and emotions to themselves and others, yet it is not until age 4 that they attribute representational mental states such as beliefs. This raises an interesting question: is attribution of beliefs different from attribution of perceptions and emotions in terms of its neural substrate? To address this question with a high degree of anatomic specificity, we partitioned the TPJ, a broad area often found to be recruited in theory of mind tasks, into 2 neuroanatomically specific regions of interest: Superior Temporal Sulcus (STS) and Inferior Parietal Lobule (IPL). To maximize behavioral specificity, we designed a tightly controlled verbal task comprised of sets of single sentences--sentences identical except for the type of mental state specified in the verb (belief, emotion, perception, syntax control). Results indicated that attribution of beliefs more strongly recruited both regions of interest than did emotions or perceptions. This is especially surprising with respect to STS, since it is widely reported in the literature to mediate the detection of referential states--among them emotions and perceptions--rather than the inference of beliefs. An explanation is offered that focuses on the differences between verbal stimuli and visual stimuli, and between a process of sentence comprehension and a process of visual detection. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

A comparison of mental health, substance use, and sexual risk behaviors between rural and non-rural transgender persons.

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Horvath, Keith J; Iantaffi, Alex; Swinburne-Romine, Rebecca; Bockting, Walter

2014-01-01

The aim of this study was to compare the mental health, substance use, and sexual risk behaviors of rural and non-rural transgender persons. Online banner advertisements were used to recruit 1,229 self-identified rural and non-rural transgender adults (18+ years) residing in the United States. Primary findings include significant differences in mental health between rural and non-rural transmen; relatively low levels of binge drinking across groups, although high levels of marijuana use; and high levels of unprotected sex among transwomen. The results confirm that mental and physical health services for transgender persons residing in rural areas are urgently needed.

Noise resistance of the violation of local causality for pure three-qutrit entangled states

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Laskowski, Wiesław; Ryu, Junghee; Żukowski, Marek

2014-10-01

Bell's theorem started with two qubits (spins 1/2). It is a ‘no-go’ statement on classical (local causal) models of quantum correlations. After 25 years, it turned out that for three qubits the situation is even more astonishing. General statements concerning higher dimensional systems, qutrits, etc, started to appear even later, once the picture with spin (higher than 1/2) was replaced by a broader one, allowing all possible observables. This work is a continuation of the Gdansk effort to take advantage of the fact that Bell's theorem can be put in the form of a linear programming problem, which in turn can be translated into a computer code. Our results are numerical and classify the strength of the violation of local causality by various families of three-qutrit states, as measured by the resistance to noise. This is previously uncharted territory. The results may be helpful in suggesting which three-qutrit states will be handy for applications in quantum information protocols. One of the surprises is that the W state turns out to reveal a stronger violation of local causality than the GHZ (Greenberger-Horne-Zeilinger) state. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘50 years of Bell's theorem’.

A new spin on causality constraints

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Hartman, Thomas; Jain, Sachin; Kundu, Sandipan [Department of Physics, Cornell University, Ithaca, New York (United States)

2016-10-26

Causality in a shockwave state is related to the analytic properties of a four-point correlation function. Extending recent results for scalar probes, we show that this constrains the couplings of the stress tensor to light spinning operators in conformal field theory, and interpret these constraints in terms of the interaction with null energy. For spin-1 and spin-2 conserved currents in four dimensions, the resulting inequalities are a subset of the Hofman-Maldacena conditions for positive energy deposition. It is well known that energy conditions in holographic theories are related to causality on the gravity side; our results make a connection on the CFT side, and extend it to non-holographic theories.

Mental causation and Searle's impossible conception of unconscious intentionality

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Meijers, A.W.M.

2000-01-01

In my article I evaluate Searle's account of mental causation, in particular his account of the causal efficacy of unconscious intentional states. I argue that top-down causation and overdetermination are unsolved problems in Searle's philosophy of mind, despite his assurances to the contrary. I

On causality of extreme events

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Massimiliano Zanin

2016-06-01

Full Text Available Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.

Causal evidence in risk and policy perceptions: Applying the covariation/mechanism framework.

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Baucum, Matt; John, Richard

2018-05-01

Today's information-rich society demands constant evaluation of cause-effect relationships; behaviors and attitudes ranging from medical choices to voting decisions to policy preferences typically entail some form of causal inference ("Will this policy reduce crime?", "Will this activity improve my health?"). Cause-effect relationships such as these can be thought of as depending on two qualitatively distinct forms of evidence: covariation-based evidence (e.g., "states with this policy have fewer homicides") or mechanism-based (e.g., "this policy will reduce crime by discouraging repeat offenses"). Some psychological work has examined how people process these two forms of causal evidence in instances of "everyday" causality (e.g., assessing why a car will not start), but it is not known how these two forms of evidence contribute to causal judgments in matters of public risk or policy. Three studies (n = 715) investigated whether judgments of risk and policy scenarios would be affected by covariation and mechanism evidence and whether the evidence types interacted with one another (as suggested by past studies). Results showed that causal judgments varied linearly with mechanism strength and logarithmically with covariation strength, and that the evidence types produced only additive effects (but no interaction). We discuss the results' implications for risk communication and policy information dissemination. Copyright © 2018 Elsevier B.V. All rights reserved.

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

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Imbens, Guido W.; Rubin, Donald B.

2015-01-01

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding…

Prevalence and Mental Health Treatment of Suicidal Ideation and Behavior Among College Students Aged 18-25 Years and Their Non-College-Attending Peers in the United States.

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Han, Beth; Compton, Wilson M; Eisenberg, Daniel; Milazzo-Sayre, Laura; McKeon, Richard; Hughes, Art

2016-06-01

College students have been the focus of many studies on suicidal ideation with or without suicidal behavior. Little attention has been given to their non-college-attending peers on these issues. We examined the 12-month prevalence and mental health treatment of suicidal ideation with or without suicidal behavior among college students aged 18-25 years and their non-college-attending peers in the United States. We assessed data from 135,300 persons aged 18-25 years who participated in the 2008-2013 National Surveys on Drug Use and Health. Descriptive analyses and multivariate logistic regression models were applied. Compared with full-time college students, high school students, those not enrolled in a school or college, and part-time college students were more likely to attempt suicide with a plan (model-adjusted prevalence = 0.67% vs 1.09%, 1.06%, and 1.07%, respectively). The mental health treatment rate among full-time college students with suicidal ideation with or without suicidal behavior was similar to the rates among the other 3 counterparts. The effects of race/ethnicity and serious mental illness on receipt of mental health treatment were significantly larger among those who did not perceive unmet treatment need than among those who perceived unmet treatment need (P = .019 and P = .001, respectively). Compared to full-time college students, non-college-attending young adults and part-time college students were at higher risk for attempting suicide with a plan. Suicide prevention and intervention strategies should emphasize increasing access to mental health treatment among both college students with suicidal ideation with or without suicidal behavior and their non-college-attending peers (particularly among minorities and those who seem to be at low risk because they are without serious mental illness and report no need for mental health treatment). © Copyright 2016 Physicians Postgraduate Press, Inc.

Causation in risk assessment and management: models, inference, biases, and a microbial risk-benefit case study.

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Cox, L A; Ricci, P F

2005-04-01

Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects: drawing sound inferences about causal relations from one or more observational studies; addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management.

Self-awareness moderates the relation between maternal mental state language about desires and children's mental state vocabulary.

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Taumoepeau, Mele; Ruffman, Ted

2016-04-01

In this intervention study, we tested the differential effect of talking about children's desires versus talking about others' thoughts and knowledge on children's acquisition of mental state vocabulary for children who did and did not have mirror self-recognition. In a sample of 96 mother-toddler dyads, each mother was randomly assigned a specially constructed, interactive lift-the-flap book to read to her child three times a week for 4 weeks. In the child desire condition the story elicited comments regarding the child's desires, and in the cognitive condition the story elicited the mother's comments about her own thoughts and knowledge while reading the story. Children's mirror self-recognition and mental state vocabulary were assessed at pre- and post-test. Children in the condition that focused on the child's desires showed a significantly greater increase in their mental state vocabulary; however, this effect was moderated by their levels of self-awareness, with children benefitting more from the intervention if they also showed self-recognition at pre-test. We argue that the combination of specific types of maternal talk and children's prior insights facilitates gains in children's mental state vocabulary. Copyright © 2015 Elsevier Inc. All rights reserved.

Towards quantum gravity: a framework for probabilistic theories with non-fixed causal structure

International Nuclear Information System (INIS)

Hardy, Lucien

2007-01-01

General relativity is a deterministic theory with non-fixed causal structure. Quantum theory is a probabilistic theory with fixed causal structure. In this paper, we build a framework for probabilistic theories with non-fixed causal structure. This combines the radical elements of general relativity and quantum theory. We adopt an operational methodology for the purposes of theory construction (though without committing to operationalism as a fundamental philosophy). The key idea in the construction is physical compression. A physical theory relates quantities. Thus, if we specify a sufficiently large set of quantities (this is the compressed set), we can calculate all the others. We apply three levels of physical compression. First, we apply it locally to quantities (actually probabilities) that might be measured in a particular region of spacetime. Then we consider composite regions. We find that there is a second level of physical compression for a composite region over and above the first level physical compression for the component regions. Each application of first and second level physical compression is quantified by a matrix. We find that these matrices themselves are related by the physical theory and can therefore be subject to compression. This is the third level of physical compression. The third level of physical compression gives rise to a new mathematical object which we call the causaloid. From the causaloid for a particular physical theory we can calculate everything the physical theory can calculate. This approach allows us to set up a framework for calculating probabilistic correlations in data without imposing a fixed causal structure (such as a background time). We show how to put quantum theory in this framework (thus providing a new formulation of this theory). We indicate how general relativity might be put into this framework and how the framework might be used to construct a theory of quantum gravity

Sometimes we can see some mental states. Comment on "Seeing mental states: An experimental strategy for measuring the observability of other minds" by Cristina Becchio, Atesh Koul, Caterina Asuini, Cesare Bertone, and Andrew Cavallo

Science.gov (United States)

Tversky, Barbara

2018-03-01

Seeing mental states[1] poses an ambitious question: Is it possible to perceive the mental states of others? According to the authors, the answer is yes. To that end, they overview some 15 studies showing that observers of a reaching arm can discern the intentions of the reaching, specifically, whether to grasp, to place, to pass, or to pour. The judgments are made before the hand arrives at the glass so the act that observers are predicting is never seen. In addition, they show how the kinematics of the reaching differ for each case, allowing the perceivers judgments. These findings are remarkable given that the kinematic differences among the actions are subtle. The overview cogently, elegantly, and convincingly summarizes the findings and in doing so, addresses criticisms that have been directed at the methods and conclusions. It is impressive how much can be reliably inferred just from the kinematics of a reaching arm. This is a significant set of findings and it is good to have them in one place.

Combining fixed effects and instrumental variable approaches for estimating the effect of psychosocial job quality on mental health: evidence from 13 waves of a nationally representative cohort study.

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Milner, Allison; Aitken, Zoe; Kavanagh, Anne; LaMontagne, Anthony D; Pega, Frank; Petrie, Dennis

2017-06-23

Previous studies suggest that poor psychosocial job quality is a risk factor for mental health problems, but they use conventional regression analytic methods that cannot rule out reverse causation, unmeasured time-invariant confounding and reporting bias. This study combines two quasi-experimental approaches to improve causal inference by better accounting for these biases: (i) linear fixed effects regression analysis and (ii) linear instrumental variable analysis. We extract 13 annual waves of national cohort data including 13 260 working-age (18-64 years) employees. The exposure variable is self-reported level of psychosocial job quality. The instruments used are two common workplace entitlements. The outcome variable is the Mental Health Inventory (MHI-5). We adjust for measured time-varying confounders. In the fixed effects regression analysis adjusted for time-varying confounders, a 1-point increase in psychosocial job quality is associated with a 1.28-point improvement in mental health on the MHI-5 scale (95% CI: 1.17, 1.40; P variable analysis, a 1-point increase psychosocial job quality is related to 1.62-point improvement on the MHI-5 scale (95% CI: -0.24, 3.48; P = 0.088). Our quasi-experimental results provide evidence to confirm job stressors as risk factors for mental ill health using methods that improve causal inference. © The Author 2017. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

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Gerald eYoung

2015-11-01

Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.

Causal Attribution and Coping Maxims Differences between Immigrants and Non-Immigrants Suffering from Back Pain in Switzerland.

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Mantwill, Sarah; Schulz, Peter J

2016-01-01

This study aimed at investigating the relationship between causal attributions and coping maxims in people suffering from back pain. Further, it aimed at identifying in how far causal attributions and related coping maxims would defer between immigrants and non-immigrants in Switzerland. Data for this study came from a larger survey study that was conducted among immigrant populations in the German- and Italian-speaking part of Switzerland. Included in the analyses were native Swiss participants, as well as Albanian- and Serbian-speaking immigrants, who had indicated to have suffered from back pain within the last 12 months prior to the study. Data was analyzed for overall 495 participants. Items for causal attributions and coping maxims were subject to factor analyses. Cultural differences were assessed with ANOVA and regression analyses. Interaction terms were included to investigate whether the relationship between causal attributions and coping maxims would differ with cultural affiliation. For both immigrant groups the physician's influence on the course of their back pain was more important than for Swiss participants (p immigrant groups were more likely to agree with maxims that were related to the improvement of the back pain, as well as the acceptance of the current situation (p immigrants and non-immigrants exist. Further, the results support the assumption of an association between causal attribution and coping maxims. However cultural affiliation did not considerably moderate this relationship.

Noise resistance of the violation of local causality for pure three-qutrit entangled states

International Nuclear Information System (INIS)

Laskowski, Wiesław; Ryu, Junghee; Żukowski, Marek

2014-01-01

Bell's theorem started with two qubits (spins 1/2). It is a ‘no-go’ statement on classical (local causal) models of quantum correlations. After 25 years, it turned out that for three qubits the situation is even more astonishing. General statements concerning higher dimensional systems, qutrits, etc, started to appear even later, once the picture with spin (higher than 1/2) was replaced by a broader one, allowing all possible observables. This work is a continuation of the Gdansk effort to take advantage of the fact that Bell's theorem can be put in the form of a linear programming problem, which in turn can be translated into a computer code. Our results are numerical and classify the strength of the violation of local causality by various families of three-qutrit states, as measured by the resistance to noise. This is previously uncharted territory. The results may be helpful in suggesting which three-qutrit states will be handy for applications in quantum information protocols. One of the surprises is that the W state turns out to reveal a stronger violation of local causality than the GHZ (Greenberger–Horne–Zeilinger) state. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘50 years of Bell's theorem’. (paper)

A local non-parametric model for trade sign inference

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Blazejewski, Adam; Coggins, Richard

2005-03-01

We investigate a regularity in market order submission strategies for 12 stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest neighbor with three predictor variables achieves an average out-of-sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.

The role of secure attachment, empathic self-efficacy, and stress perception in causal beliefs related to mental illness – a cross-cultural study: Italy versus Israel

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Mannarini S

2017-10-01

Full Text Available Stefania Mannarini,1 Alisa Reikher,1 Sharon Shani,1 Inbal Shani-Zinovich2 1Department of Philosophy, Sociology, Education and Applied Psychology, Interdepartmental Center for Family Research, University of Padova, Padova, Italy; 2Department of Counseling and Human Development, Faculty of Education, University of Haifa, Mount Carmel, Haifa, Israel Background: Research suggests that “mental illness etiological beliefs” and attitudes toward mentally ill people are significantly related; it has also been demonstrated that adult attachment style and empathic self-efficacy affect such attitudes. Moreover, community or regional culture has a significant impact on etiology beliefs and attitudes toward the mentally sick. Materials and methods: We carried out this study in Italy and Israel among psychology students to compare two cultures in regards to causal beliefs of mental disorders and the roles that specific variables, such as secure attachment, empathic self-efficacy, and stress, play in etiological beliefs. The participants (N=305 were students who belonged to two universities: Padua (N=183 and Haifa (N=122. The Many Facet Rasch Model (MFRM was applied in a cross-cultural perspective to analyze the differential functioning of specific etiological beliefs in relation to the above mentioned variables; the effect of gender and religious beliefs was also entered in the MFRM. Results: The two cultures reacted differently to the biogenetic and psychosocial causal explanations of mental disorders: Israeli students endorsed the biogenetic causal beliefs model more frequently than the Italians. Among other findings, concerning the biogenetic model, the Italian students were predominantly males, who declared to be religious and reported lower levels of secure attachment than Israelis. On the other hand, the Israeli students who manifested a preference toward the biogenetic explanation were mostly females, who declared not to be religious and who

Mental health care roles of non-medical primary health and social care services.

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Mitchell, Penny

2009-02-01

Changes in patterns of delivery of mental health care over several decades are putting pressure on primary health and social care services to increase their involvement. Mental health policy in countries like the UK, Australia and New Zealand recognises the need for these services to make a greater contribution and calls for increased intersectoral collaboration. In Australia, most investment to date has focused on the development and integration of specialist mental health services and primary medical care, and evaluation research suggests some progress. Substantial inadequacies remain, however, in the comprehensiveness and continuity of care received by people affected by mental health problems, particularly in relation to social and psychosocial interventions. Very little research has examined the nature of the roles that non-medical primary health and social care services actually or potentially play in mental health care. Lack of information about these roles could have inhibited development of service improvement initiatives targeting these services. The present paper reports the results of an exploratory study that examined the mental health care roles of 41 diverse non-medical primary health and social care services in the state of Victoria, Australia. Data were collected in 2004 using a purposive sampling strategy. A novel method of surveying providers was employed whereby respondents within each agency worked as a group to complete a structured survey that collected quantitative and qualitative data simultaneously. This paper reports results of quantitative analyses including a tentative principal components analysis that examined the structure of roles. Non-medical primary health and social care services are currently performing a wide variety of mental health care roles and they aspire to increase their involvement in this work. However, these providers do not favour approaches involving selective targeting of clients with mental disorders.

Dopamine reward prediction errors reflect hidden state inference across time

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Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.

2017-01-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301

Racial-ethnic variation in U.S. mental health service use among Latino and Asian non-U.S. citizens.

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Lee, Sungkyu; Laiewski, Laurel; Choi, Sunha

2014-01-01

This study examined the factors associated with service utilization for mental health conditions among Latino and Asian non-U.S. citizens in the United States by service type and race. Data were obtained from the National Latino and Asian American Study (NLAAS). The sample for this study was 849 Latino and 595 Asian non-U.S. citizens between ages 18 and 64 (N=1,444). Mental health services obtained through three types of service providers were examined: specialty mental health services, general medical services, and other services. Guided by the modified Andersen health behavioral model, analyses involved logistic regression models conducted with penalized maximum likelihood estimation. Although having a psychiatric disorder increased mental health service use in both groups, only 32% of Latino and 52% of Asian non-U.S. citizens with psychiatric needs reported using mental health services during the past 12 months. Overall, noncitizen Latinos and Asians were more likely to use mental health services from general health care providers and other providers than from specialty mental health providers. Several significant predisposing, enabling, and need factors, such as age, health insurance, and having psychiatric conditions, also interacted with race. Findings of the study suggest that there are ethnoracial variations in mental health service use between Latino and Asian non-U.S. citizens. Mental health professionals should consider developing tailored mental health interventions that account for cultural variations to enhance access to services for these vulnerable subgroups of Latinos and Asians. Further research should examine ethnic disparities in mental health service use among various non-U.S. citizen racial-ethnic subgroups.

Work Intensity and Non-Completion of University: Longitudinal Approach and Causal Inference

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Moulin, Stéphane; Doray, Pierre; Laplante, Benoît; Street, María Constanza

2013-01-01

Researchers focused upon the work-dropping out connection tend to show a U-shaped relationship between the likelihood of dropping out and the number of hours worked outside school, with a higher exit rate for both non-working students and for students whose working hours pass a critical threshold. Yet the data typically used by these researchers…

Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke

Directory of Open Access Journals (Sweden)

Melissa Zavaglia

2015-01-01

Full Text Available Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA, to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS. The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a ‘map of stroke’.

Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke.

Science.gov (United States)

Zavaglia, Melissa; Forkert, Nils D; Cheng, Bastian; Gerloff, Christian; Thomalla, Götz; Hilgetag, Claus C

2015-01-01

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.

Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke

Science.gov (United States)

Zavaglia, Melissa; Forkert, Nils D.; Cheng, Bastian; Gerloff, Christian; Thomalla, Götz; Hilgetag, Claus C.

2015-01-01

Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a ‘map of stroke’. PMID:26448908

Rational Variability in Children's Causal Inferences: The Sampling Hypothesis

Science.gov (United States)

Denison, Stephanie; Bonawitz, Elizabeth; Gopnik, Alison; Griffiths, Thomas L.

2013-01-01

We present a proposal--"The Sampling Hypothesis"--suggesting that the variability in young children's responses may be part of a rational strategy for inductive inference. In particular, we argue that young learners may be randomly sampling from the set of possible hypotheses that explain the observed data, producing different hypotheses with…

Causal relationship: a new tool for the causal characterization of Lorentzian manifolds

International Nuclear Information System (INIS)

Garcia-Parrado, Alfonso; Senovilla, Jose M M

2003-01-01

We define and study a new kind of relation between two diffeomorphic Lorentzian manifolds called a causal relation, which is any diffeomorphism characterized by mapping every causal vector of the first manifold onto a causal vector of the second. We perform a thorough study of the mathematical properties of causal relations and prove in particular that two given Lorentzian manifolds (say V and W) may be causally related only in one direction (say from V to W, but not from W to V). This leads us to the concept of causally equivalent (or isocausal in short) Lorentzian manifolds as those mutually causally related and to a definition of causal structure over a differentiable manifold as the equivalence class formed by isocausal Lorentzian metrics upon it. Isocausality is a more general concept than the conformal relationship, because we prove the remarkable result that a conformal relation φ is characterized by the fact of being a causal relation of the particular kind in which both φ and φ -1 are causal relations. Isocausal Lorentzian manifolds are mutually causally compatible, they share some important causal properties, and there are one-to-one correspondences, which are sometimes non-trivial, between several classes of their respective future (and past) objects. A more important feature is that they satisfy the same standard causality constraints. We also introduce a partial order for the equivalence classes of isocausal Lorentzian manifolds providing a classification of all the causal structures that a given fixed manifold can have. By introducing the concept of causal extension we put forward a new definition of causal boundary for Lorentzian manifolds based on the concept of isocausality, and thereby we generalize the traditional Penrose constructions of conformal infinity, diagrams and embeddings. In particular, the concept of causal diagram is given. Many explicit clarifying examples are presented throughout the paper

A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems

NARCIS (Netherlands)

Fraanje, Rufus; Verhaegen, Michel; Verdult, Vincent; Pintelon, Rik

2003-01-01

The paper extends the subspacc identification method to estimate state-space models from frequency response function (FRF) samples, proposed by McKelvey et al. (1996) for mixed causal/anti-causal systems, and shows that other frequency domain subspace algorithms can be extended similarly. The method

Causal beliefs of the public and social acceptance of persons with mental illness: a comparative analysis of schizophrenia, depression and alcohol dependence.

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Schomerus, G; Matschinger, H; Angermeyer, M C

2014-01-01

There is an ongoing debate whether biological illness explanations improve tolerance towards persons with mental illness or not. Several theoretical models have been proposed to predict the relationship between causal beliefs and social acceptance. This study uses path models to compare different theoretical predictions regarding attitudes towards persons with schizophrenia, depression and alcohol dependence. In a representative population survey in Germany (n = 3642), we elicited agreement with belief in biogenetic causes, current stress and childhood adversities as causes of either disorder as described in an unlabelled case vignette. We further elicited potentially mediating attitudes related to different theories about the consequences of biogenetic causal beliefs (attribution theory: onset responsibility, offset responsibility; genetic essentialism: differentness, dangerousness; genetic optimism: treatability) and social acceptance. For each vignette condition, we calculated a multiple mediator path model containing all variables. Biogenetic beliefs were associated with lower social acceptance in schizophrenia and depression, and with higher acceptance in alcohol dependence. In schizophrenia and depression, perceived differentness and dangerousness mediated the largest indirect effects, the consequences of biogenetic causal explanations thus being in accordance with the predictions of genetic essentialism. Psychosocial causal beliefs had differential effects: belief in current stress as a cause was associated with higher acceptance in schizophrenia, while belief in childhood adversities resulted in lower acceptance of a person with depression. Biological causal explanations seem beneficial in alcohol dependence, but harmful in schizophrenia and depression. The negative correlates of believing in childhood adversities as a cause of depression merit further exploration.

Text comprehension in children: comparing different classes of inferences by using on-line methodology / Compreensão de texto em crianças: comparações entre diferentes classes de inferência a partir de uma metodologia on-line

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Alina Galvão Spinillo

2007-01-01

Full Text Available This study, by means of using an on-line methodology, examined 7 and 9-year-old children's text comprehension in relation to different types of inferences constructed during a story reading task: causal inferences, state inferences and inferences of prediction (what happens next in the story. The on-line methodology consists of making inferential questions to the child during text comprehension immediately after the subject has read a passage. Due to the fact that inferences of prediction involve extratextual information and require to raise hypothesis about the continuity of the narrative, children had difficulties in predicting events that had not occurred yet in the story. It was concluded that the ability to make inferences during text comprehension varies according to the type of inferential question presented and that this ability develops with age. The inovative aspect of the on-line methodology and its relevance to the research on text comprehension are discussed.

Mental Health and Drivers of Need in Emergent and Non-Emergent Emergency Department (ED) Use: Do Living Location and Non-Emergent Care Sources Matter?

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McManus, Moira C; Cramer, Robert J; Boshier, Maureen; Akpinar-Elci, Muge; Van Lunen, Bonnie

2018-01-13

Emergency department (ED) utilization has increased due to factors such as admissions for mental health conditions, including suicide and self-harm. We investigate direct and moderating influences on non-emergent ED utilization through the Behavioral Model of Health Services Use. Through logistic regression, we examined correlates of ED use via 2014 New York State Department of Health Statewide Planning and Research Cooperative System outpatient data. Consistent with the primary hypothesis, mental health admissions were associated with emergent use across models, with only a slight decrease in effect size in rural living locations. Concerning moderating effects, Spanish/Hispanic origin was associated with increased likelihood for emergent ED use in the rural living location model, and non-emergent ED use for the no non-emergent source model. 'Other' ethnic origin increased the likelihood of emergent ED use for rural living location and no non-emergent source models. The findings reveal 'need', including mental health admissions, as the largest driver for ED use. This may be due to mental healthcare access, or patients with mental health emergencies being transported via first responders to the ED, as in the case of suicide, self-harm, manic episodes or psychotic episodes. Further educating ED staff on this patient population through gatekeeper training may ensure patients receive the best treatment and aid in driving access to mental healthcare delivery changes.

The quantum potential and ''causal'' trajectories for stationary states and for coherent states

International Nuclear Information System (INIS)

Barut, A.O.; Bozic, M.

1988-07-01

We show for stationary states in a central potential that the quantum action S is only a part of the classical action W and derive an expression for the ''quantum potential'' U Q in terms of the other part. The association of momenta of some ''particles'' in the causal interpretation of quantum mechanics by p-vector=∇S and by dp-vector'/dt=-∇(V+U Q ) gives for stationary states very different orbits which have no relation to classical orbits but express some flow properties of the quantum mechanical current. For coherent states, on the other hand, p-vector and p-vector' as well as the quantum mechanical average p-vector and classical momenta, all four, lead to essentially the same trajectories except for different integration constants. The spinning particle is also considered. (author). 27 refs, 2 figs

Understanding environmental contributions to autism: Causal concepts and the state of science.

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Hertz-Picciotto, Irva; Schmidt, Rebecca J; Krakowiak, Paula

2018-04-01

The complexity of neurodevelopment, the rapidity of early neurogenesis, and over 100 years of research identifying environmental influences on neurodevelopment serve as backdrop to understanding factors that influence risk and severity of autism spectrum disorder (ASD). This Keynote Lecture, delivered at the May 2016 annual meeting of the International Society for Autism Research, describes concepts of causation, outlines the trajectory of research on nongenetic factors beginning in the 1960s, and briefly reviews the current state of this science. Causal concepts are introduced, including root causes; pitfalls in interpreting time trends as clues to etiologic factors; susceptible time windows for exposure; and implications of a multi-factorial model of ASD. An historical background presents early research into the origins of ASD. The epidemiologic literature from the last fifteen years is briefly but critically reviewed for potential roles of, for example, air pollution, pesticides, plastics, prenatal vitamins, lifestyle and family factors, and maternal obstetric and metabolic conditions during her pregnancy. Three examples from the case-control CHildhood Autism Risks from Genes and the Environment Study are probed to illustrate methodological approaches to central challenges in observational studies: capturing environmental exposure; causal inference when a randomized controlled clinical trial is either unethical or infeasible; and the integration of genetic, epigenetic, and environmental influences on development. We conclude with reflections on future directions, including exposomics, new technologies, the microbiome, gene-by-environment interaction in the era of -omics, and epigenetics as the interface of those two. As the environment is malleable, this research advances the goal of a productive and fulfilling life for all children, teen-agers and adults. Autism Res 2018, 11: 554-586. © 2018 International Society for Autism Research, Wiley Periodicals, Inc

Maternal Mental State Language and Preschool Children's Attachment Security: Relation to Children's Mental State Language and Expressions of Emotional Understanding

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Mcquaid, Nancy; Bigelow, Ann E.; McLaughlin, Jessica; MacLean, Kim

2008-01-01

Mothers' mental state language in conversation with their preschool children, and children's preschool attachment security were examined for their effects on children's mental state language and expressions of emotional understanding in their conversation. Children discussed an emotionally salient event with their mothers and then relayed the…

Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach.

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Gul, Sehrish; Zou, Xiang; Hassan, Che Hashim; Azam, Muhammad; Zaman, Khalid

2015-12-01

This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.

Discontinuity of maximum entropy inference and quantum phase transitions

International Nuclear Information System (INIS)

Chen, Jianxin; Ji, Zhengfeng; Yu, Nengkun; Zeng, Bei; Li, Chi-Kwong; Poon, Yiu-Tung; Shen, Yi; Zhou, Duanlu

2015-01-01

In this paper, we discuss the connection between two genuinely quantum phenomena—the discontinuity of quantum maximum entropy inference and quantum phase transitions at zero temperature. It is shown that the discontinuity of the maximum entropy inference of local observable measurements signals the non-local type of transitions, where local density matrices of the ground state change smoothly at the transition point. We then propose to use the quantum conditional mutual information of the ground state as an indicator to detect the discontinuity and the non-local type of quantum phase transitions in the thermodynamic limit. (paper)

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Directory of Open Access Journals (Sweden)

Wanzeng Kong

2015-08-01

Full Text Available Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies.

Mental State Talk Structure in Children’s Narratives: A Cluster Analysis

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Giuliana Pinto

2017-01-01

Full Text Available This study analysed children’s Theory of Mind (ToM as assessed by mental state talk in oral narratives. We hypothesized that the children’s mental state talk in narratives has an underlying structure, with specific terms organized in clusters. Ninety-eight children attending the last year of kindergarten were asked to tell a story twice, at the beginning and at the end of the school year. Mental state talk was analysed by identifying terms and expressions referring to perceptual, physiological, emotional, willingness, cognitive, moral, and sociorelational states. The cluster analysis showed that children’s mental state talk is organized in two main clusters: perceptual states and affective states. Results from the study confirm the feasibility of narratives as an outlet to inquire mental state talk and offer a more fine-grained analysis of mental state talk structure.

Joint estimation over multiple individuals improves behavioural state inference from animal movement data.

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Jonsen, Ian

2016-02-08

State-space models provide a powerful way to scale up inference of movement behaviours from individuals to populations when the inference is made across multiple individuals. Here, I show how a joint estimation approach that assumes individuals share identical movement parameters can lead to improved inference of behavioural states associated with different movement processes. I use simulated movement paths with known behavioural states to compare estimation error between nonhierarchical and joint estimation formulations of an otherwise identical state-space model. Behavioural state estimation error was strongly affected by the degree of similarity between movement patterns characterising the behavioural states, with less error when movements were strongly dissimilar between states. The joint estimation model improved behavioural state estimation relative to the nonhierarchical model for simulated data with heavy-tailed Argos location errors. When applied to Argos telemetry datasets from 10 Weddell seals, the nonhierarchical model estimated highly uncertain behavioural state switching probabilities for most individuals whereas the joint estimation model yielded substantially less uncertainty. The joint estimation model better resolved the behavioural state sequences across all seals. Hierarchical or joint estimation models should be the preferred choice for estimating behavioural states from animal movement data, especially when location data are error-prone.

The Effects of Simple Necessity and Sufficiency Relationships on Children's Causal Inferences

Science.gov (United States)

Siegler, Robert S.

1976-01-01

Attempted to determine (1) whether developmental differences existed in children's comprehension of simple necessity and simple sufficiency relationships, and (2) the source of developmental differences in children's causal reasoning. (SB)

A Model of Mental State Transition Network

Science.gov (United States)

Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo

Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

Islamic vs. Conventional Banking Role in Non-Oil Growth: A Causal Analysis in the Case of Bahrain

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Abdallah Belhadia

2014-12-01

Full Text Available This paper aims to investigate the type of relationship between Islamic vs. Conventional banking and non-Oil economic growth in the case of Bahrain by using annual data 1990-2012 retrieved from Islamic banks and financial institutions information (Ibis-Online of the Islamic Bank of Development (IDB, World Bank development indicators (WB, and the Central Bank of Bahrain (CBB annual reports.This study employs the Johansen and Juselius Cointegration test and Vector Error Correction Model (VECM as well as Vector Autoregressive model (VAR to reveal the long run and short-run causality between the dual banking development and non-Oil GDP growth. The VECM results of the conventional banking show that there is long-run bidirectional causality between all the conventional banking selected indicators and the non-Oil GDP. For the Islamic banking VAR model, there is a unidirectional causality from Islamic banking indicators to the non-oil GDP. There is no evidence on the role of non-oil GDP on the Islamic banking development. Impulse response functions in the two models shows that through one standards deviation positive shock in Islamic vs. Conventional Credit provided to private sector, the non-Oil GDP will be much higher in the next five years if we stimulate the Islamic credit provided to private sector than the conventional banks.Moreover, the Islamic credit provided to the private sector appears to be more procyclical than the credit provided by the conventional banks. However, the fluctuations in the conventional credit are sharper than the Islamic banks’ private credit. This study provides the policy makers in Bahrain with the appropriate evidences to design their policies in fostering the non-Oil sector.

Inferences of Recent and Ancient Human Population History Using Genetic and Non-Genetic Data

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Kitchen, Andrew

2008-01-01

I have adopted complementary approaches to inferring human demographic history utilizing human and non-human genetic data as well as cultural data. These complementary approaches form an interdisciplinary perspective that allows one to make inferences of human history at varying timescales, from the events that occurred tens of thousands of years…

Counterfactual Reasoning in Non-psychotic First-Degree Relatives of People with Schizophrenia

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Auria eAlbacete

2016-05-01

Full Text Available Counterfactual thinking (CFT is a type of conditional reasoning that enables the generation of mental simulations of alternatives to past factual events. Previous research has found this cognitive feature to be disrupted in schizophrenia. At the same time, the study of cognitive deficits in unaffected relatives of people with schizophrenia has significantly increased, supporting its potential endophenotypic role in this disorder. Using an exploratory approach, the current study examined CFT for the first time in a sample of non-psychotic first-degree relatives of schizophrenia patients (N=43, in comparison with schizophrenia patients (N=54 and healthy controls (N=44. A series of tests that assessed the causal order effect in CFT and the ability to generate counterfactual thoughts and counterfactually derive inferences using the Counterfactual Inference Test was completed. Associations with variables of basic and social cognition, levels of schizotypy and psychotic-like experiences in addition to clinical and sociodemographic characteristics were also explored. Findings showed that first-degree relatives generated a lower number of counterfactual thoughts than controls, and were more adept at counterfactually deriving inferences, specifically in the scenarios related to regret and to judgements of avoidance in an unusual situation. No other significant results were found. These preliminary findings suggest that non-psychotic first-degree relatives of schizophrenia patients show a subtle disruption of global counterfactual thinking compared with what is normally expected in the general population. Because of the potential impact of such deficits, new treatments targeting CFT improvement might be considered in future management strategies.

Mental state attribution and the gaze cueing effect.

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Cole, Geoff G; Smith, Daniel T; Atkinson, Mark A

2015-05-01

Theory of mind is said to be possessed by an individual if he or she is able to impute mental states to others. Recently, some authors have demonstrated that such mental state attributions can mediate the "gaze cueing" effect, in which observation of another individual shifts an observer's attention. One question that follows from this work is whether such mental state attributions produce mandatory modulations of gaze cueing. Employing the basic gaze cueing paradigm, together with a technique commonly used to assess mental-state attribution in nonhuman animals, we manipulated whether the gazing agent could see the same thing as the participant (i.e., the target) or had this view obstructed by a physical barrier. We found robust gaze cueing effects, even when the observed agent in the display could not see the same thing as the participant. These results suggest that the attribution of "seeing" does not necessarily modulate the gaze cueing effect.

Asymptotic inference for jump diffusions with state-dependent intensity

NARCIS (Netherlands)

Becheri, Gaia; Drost, Feico; Werker, Bas

2016-01-01

We establish the local asymptotic normality property for a class of ergodic parametric jump-diffusion processes with state-dependent intensity and known volatility function sampled at high frequency. We prove that the inference problem about the drift and jump parameters is adaptive with respect to

The Downward Causality and the Hard Problem of Consciousness or Why Computer Programs Do not Work in the Dark

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Boldachev Alexander

2015-01-01

Full Text Available Any low-level processes, the sequence of chemical interactions in a living cell, muscle cellular activity, processor commands or neuron interaction, is possible only if there is a downward causality, only due to uniting and controlling power of the highest level. Therefore, there is no special “hard problem of consciousness”, i.e. the problem of relation of ostensibly purely biological materiality and non-causal mentality - we have only the single philosophical problem of relation between the upward and downward causalities, the problem of interrelation between hierarchic levels of existence. It is necessary to conclude that the problem of determinacy of chemical processes by the biological ones and the problem of neuron interactions caused by consciousness are of one nature and must have one solution.

Comparison of Mental Health Components among Athlete and Non-athlete Adolescents

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Zeinab Ghiami

2015-07-01

Full Text Available Background: Adolescence is a period of rapid biological and behavioral changes that may expand the risk of mental health issues. Objective: This study aimed to compare the mental health of male and female athletes and non-athletes among a high school student groups. Methodology: On this base 100 students (50 athletes and 50 non-athletes, Mage = 16 (SD = ±1 were selected through multi stage random sampling and divided equally into four groups (female athlete / non-athlete, male athlete / non-athlete. General Health Questionnaire designed by Goldberg and Hiller (1979 was used for data collections. Results: The analysis of one-way ANOVA displayed significant differences between the mean scores in mental health among the groups in terms of mental health, F (3, 96 =39, P = .01 with less prevalence of these symptoms among athletes comparing to non-athletes. Conclusion: Increasing opportunities for students to take part in sport competitions can protect them against poor psychological well-being. Keywords: Mental Health; Depression; Anxiety; Social dysfunction; Somatic

An Investigation of Human Inductive Biases in Causality and Probability Judgments

OpenAIRE

Yeung, Sai Wing

2011-01-01

People often makes inductive inferences that go beyond the data that are given. In order to generate these inferences, people must rely on inductive biases - constraints on learning that guide conclusion from limited data. This thesis presents a survey of three topics concerning people's inductive biases.The first part of this thesis examines people's expectations about the strengths of causes in elemental causal induction - learning about the relationship between a single cause and effect. T...

Quantum Enhanced Inference in Markov Logic Networks.

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Wittek, Peter; Gogolin, Christian

2017-04-19

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

Quantum Enhanced Inference in Markov Logic Networks

Science.gov (United States)

Wittek, Peter; Gogolin, Christian

2017-04-01

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

Effect of Coping-Therapy on Mental Health of Mothers with Genetic and Non Genetic Mentally Retarded Children

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M Alagheband

2011-04-01

Full Text Available Introdution: Presence of mentally retarded children as a source of pressure can jeopardize the general health of parents, especially mothers. The range of effect depends on the recognitive evaluation and the individual. The aim of this study was to investigate the effect of coping-therapy on mental health of mothers with genetically and non genetically mentally retarded children referring to Yazd clinical center. Methods: This study was semi experimental and included 40 mothers with mentally retarded children studying in schools supported by the welfare organization of Yazd in 2009- 2010 and were selected by available sampling method. They were divided to two groups; case and control. Before any therapy, all of the mothers answered a general health questionnaire(GHQ28. In the next step, coping-therapy was performed on the case group. In the end, all of the mothers answered the same questionnaire(GHQ28 and data were analyzed by covariance method and t test. Results: The research indicated that coping-therapy has a positive effect on the mental health of mothers with genetically mentally retarded children. This effect is similar on mothers of children with non genetically mental retarded children. Coping-therapy decreases the somatic signs of depression in mothers and improves their sleeping and social efficacy. There was no association of age and educational level of mothers with coping-therapy. Conclusion: Coping-therapy can improve the mental health of mothers of both genetically and non genetically mentally retarded children

Causal independence between energy consumption and economic growth in Liberia: Evidence from a non-parametric bootstrapped causality test

International Nuclear Information System (INIS)

Wesseh, Presley K.; Zoumara, Babette

2012-01-01

This contribution investigates causal interdependence between energy consumption and economic growth in Liberia and proposes application of a bootstrap methodology. To better reflect causality, employment is incorporated as additional variable. The study demonstrates evidence of distinct bidirectional Granger causality between energy consumption and economic growth. Additionally, the results show that employment in Liberia Granger causes economic growth and apply irrespective of the short-run or long-run. Evidence from a Monte Carlo experiment reveals that the asymptotic Granger causality test suffers size distortion problem for Liberian data, suggesting that the bootstrap technique employed in this study is more appropriate. Given the empirical results, implications are that energy expansion policies like energy subsidy or low energy tariff for instance, would be necessary to cope with demand exerted as a result of economic growth in Liberia. Furthermore, Liberia might have the performance of its employment generation on the economy partly determined by adequate energy. Therefore, it seems fully justified that a quick shift towards energy production based on clean energy sources may significantly slow down economic growth in Liberia. Hence, the government’s target to implement a long-term strategy to make Liberia a carbon neutral country, and eventually less carbon dependent by 2050 is understandable. - Highlights: ► Causality between energy consumption and economic growth in Liberia investigated. ► There is bidirectional causality between energy consumption and economic growth. ► Energy expansion policies are necessary to cope with demand from economic growth. ► Asymptotic Granger causality test suffers size distortion problem for Liberian data. ► The bootstrap methodology employed in our study is more appropriate.

An Empirical Test of Causal Inference Between Role Perceptions, Satisfaction with Work, Performance and Organizational Level

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Szilagyi, Andrew D.

1977-01-01

Attempts to empirically verify the causal source and direction of causal influence between role ambiguity, role conflict and job satisfaction and performance for three organizational levels in a hospital environment. (Author/RK)

Case Studies Nested in Fuzzy-Set QCA on Sufficiency: Formalizing Case Selection and Causal Inference

Science.gov (United States)

Schneider, Carsten Q.; Rohlfing, Ingo

2016-01-01

Qualitative Comparative Analysis (QCA) is a method for cross-case analyses that works best when complemented with follow-up case studies focusing on the causal quality of the solution and its constitutive terms, the underlying causal mechanisms, and potentially omitted conditions. The anchorage of QCA in set theory demands criteria for follow-up…

Mental Health of Survivors of the 2010 Haitian Earthquake Living in the United States

Centers for Disease Control (CDC) Podcasts

2010-04-16

Thousands of survivors of the 2010 Haitian Earthquake are currently living in the United States. This podcast features a brief non-disease-specific interview with Dr. Marc Safran, CDC's longest serving psychiatrist, about a few of the mental health challenges such survivors may face.  Created: 4/16/2010 by CDC Center of Attribution: Mental and Behavioral Health Team, 2010 CDC Haiti Earthquake Mission, CDC Emergency Operations Center.   Date Released: 5/6/2010.

Disability acquisition and mental health: effect modification by demographic and socioeconomic characteristics using data from an Australian longitudinal study.

Science.gov (United States)

Aitken, Zoe; Simpson, Julie Anne; Bentley, Rebecca; Kavanagh, Anne Marie

2017-09-18

There is evidence of a causal relationship between disability acquisition and poor mental health, but the substantial heterogeneity in the magnitude of the effect is poorly understood and may be aetiologically informative. This study aimed to identify demographic and socioeconomic factors that modify the effect of disability acquisition on mental health. The Household, Income and Labour Dynamics in Australia Survey is a nationally representative longitudinal survey of Australian households that has been conducted annually since 2001. Four waves of data were included in this analysis, from 2011 to 2014. Individuals who acquired a disability (n=387) were compared with those who remained disability-free in all four waves (n=7936). Mental health was measured using the mental health subscale of the Short Form 36 (SF-36) general health questionnaire, which measures symptoms of depression, anxiety and psychological well-being. Linear regression models were fitted to estimate the effect of disability acquisition on mental health, testing for effect modification by key demographic and socioeconomic characteristics. To maximise causal inference, we used a propensity score approach with inverse probability of treatment weighting to control for confounding and multiple imputation using chained equations to assess the impact of missing data. On average, disability acquisition was associated with a 5-point decline in mental health score (estimated mean difference: -5.1, 95% CI -7.2 to -3.0). There was strong evidence that income and relationship status modified the effect, with more detrimental effects in the lowest (-12.5, 95% CI -18.5 to -6.5) compared with highest income quintile (-1.1, 95% CI -4.9 to 2.7) and for people not in a relationship (-8.8, 95% CI -12.9 to -4.8) compared with those who were (-3.7, 95% CI -6.1 to -1.4). Our results suggest that the detrimental effect of disability acquisition on mental health is substantially greater for socioeconomic

MENTAL STATE LANGUAGE DEVELOPMENT: THE LONGITUDINAL ROLES OF ATTACHMENT AND MATERNAL LANGUAGE.

Science.gov (United States)

Becker Razuri, Erin; Hiles Howard, Amanda R; Purvis, Karyn B; Cross, David R

2017-05-01

Maternal mental state language is thought to influence children's mental state language and sociocognitive understanding (e.g., theory of mind), but the mechanism is unclear. The current study examined the longitudinal development of mental state language in mother-child interactions. The methodology included assessments of the child and/or mother-child dyad at six time points between 12 to 52 months of the child's age. Measures determined child's attachment style and language abilities, and mental state language used by mother and child during a block-building task. Results showed that (a) mental state talk, including belief and desire language, increased over time; (b) there were differences between the type of mental state words used by the mother in insecure versus secure dyads; (c) there were differences in patterns of mental state words used in both mothers and children in insecure versus secure dyads; and (d) attachment appeared to exert a consistent influence over time. © 2017 Michigan Association for Infant Mental Health.

Causality Statistical Perspectives and Applications

CERN Document Server

Berzuini, Carlo; Bernardinell, Luisa

2012-01-01

A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book:Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addr

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

Science.gov (United States)

Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Quantifying secondary pest outbreaks in cotton and their monetary cost with causal-inference statistics.

Science.gov (United States)

Gross, Kevin; Rosenheim, Jay A

2011-10-01

Secondary pest outbreaks occur when the use of a pesticide to reduce densities of an unwanted target pest species triggers subsequent outbreaks of other pest species. Although secondary pest outbreaks are thought to be familiar in agriculture, their rigorous documentation is made difficult by the challenges of performing randomized experiments at suitable scales. Here, we quantify the frequency and monetary cost of secondary pest outbreaks elicited by early-season applications of broad-spectrum insecticides to control the plant bug Lygus spp. (primarily L. hesperus) in cotton grown in the San Joaquin Valley, California, USA. We do so by analyzing pest-control management practices for 969 cotton fields spanning nine years and 11 private ranches. Our analysis uses statistical methods to draw formal causal inferences from nonexperimental data that have become popular in public health and economics, but that are not yet widely known in ecology or agriculture. We find that, in fields that received an early-season broad-spectrum insecticide treatment for Lygus, 20.2% +/- 4.4% (mean +/- SE) of late-season pesticide costs were attributable to secondary pest outbreaks elicited by the early-season insecticide application for Lygus. In 2010 U.S. dollars, this equates to an additional $6.00 +/- $1.30 (mean +/- SE) per acre in management costs. To the extent that secondary pest outbreaks may be driven by eliminating pests' natural enemies, these figures place a lower bound on the monetary value of ecosystem services provided by native communities of arthropod predators and parasitoids in this agricultural system.

Causal Analysis for Performance Modeling of Computer Programs

Directory of Open Access Journals (Sweden)

Jan Lemeire

2007-01-01

Full Text Available Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.

Method of fuzzy inference for one class of MISO-structure systems with non-singleton inputs

Science.gov (United States)

Sinuk, V. G.; Panchenko, M. V.

2018-03-01

In fuzzy modeling, the inputs of the simulated systems can receive both crisp values and non-Singleton. Computational complexity of fuzzy inference with fuzzy non-Singleton inputs corresponds to an exponential. This paper describes a new method of inference, based on the theorem of decomposition of a multidimensional fuzzy implication and a fuzzy truth value. This method is considered for fuzzy inputs and has a polynomial complexity, which makes it possible to use it for modeling large-dimensional MISO-structure systems.

Effects of user mental state on EEG-BCI performance

Directory of Open Access Journals (Sweden)

Andrew eMyrden

2015-06-01

Full Text Available Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this study, we investigated the effects of three mental states - fatigue, frustration, and attention - on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the performance of user-specific mental tasks. Following training, participants completed three testing sessions, during which they used the BCI to play a simple maze navigation game while periodically reporting their perceived levels of fatigue, frustration, and attention. Statistical analysis indicated that there is a significant relationship between frustration and BCI performance while the relationship between fatigue and BCI performance approached significance. BCI performance was 7% lower than average when self-reported fatigue was low and 10% lower than average when self-reported frustration was low. A multivariate analysis of mental state revealed the presence of contiguous regions in mental state space where BCI performance was more accurate than average, suggesting the importance of moderate fatigue for achieving effortless focus on BCI control, frustration as a potential motivating factor, and attention as a compensatory mechanism to increasing frustration. Finally, a visual analysis showed the sensitivity of underlying class distributions to changes in mental state. Collectively, these results indicate that mental state is closely related to BCI performance, encouraging future development of psychologically adaptive BCIs.

Putting a cap on causality violations in causal dynamical triangulations

International Nuclear Information System (INIS)

Ambjoern, Jan; Loll, Renate; Westra, Willem; Zohren, Stefan

2007-01-01

The formalism of causal dynamical triangulations (CDT) provides us with a non-perturbatively defined model of quantum gravity, where the sum over histories includes only causal space-time histories. Path integrals of CDT and their continuum limits have been studied in two, three and four dimensions. Here we investigate a generalization of the two-dimensional CDT model, where the causality constraint is partially lifted by introducing branching points with a weight g s , and demonstrate that the system can be solved analytically in the genus-zero sector. The solution is analytic in a neighborhood around weight g s = 0 and cannot be analytically continued to g s = ∞, where the branching is entirely geometric and where one would formally recover standard Euclidean two-dimensional quantum gravity defined via dynamical triangulations or Liouville theory

Inference and learning in sparse systems with multiple states

International Nuclear Information System (INIS)

Braunstein, A.; Ramezanpour, A.; Zhang, P.; Zecchina, R.

2011-01-01

We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.

Systematic review on the association between employee worktime control and work-non-work balance, health and well-being, and job-related outcomes.

Science.gov (United States)

Nijp, Hylco H; Beckers, Debby G J; Geurts, Sabine A E; Tucker, Philip; Kompier, Michiel A J

2012-07-01

The aim of this review was to assess systematically the empirical evidence for associations between employee worktime control (WTC) and work-non-work balance, health/well-being, and job-related outcomes (eg, job satisfaction, job performance). A systematic search of empirical studies published between 1995-2011 resulted in 63 relevant papers from 53 studies. Five different categories of WTC measurements were distinguished (global WTC, multidimensional WTC, flextime, leave control, and "other subdimensions of WTC"). For each WTC category, we examined the strength of evidence for an association with (i) work-non-work balance, (ii) health/well-being, and (iii) job-related outcomes. We distinguished between cross-sectional, longitudinal, and intervention studies. Evidence strength was assessed based on the number of studies and their convergence in terms of study findings. (Moderately) strong cross-sectional evidence was found for positive associations between global WTC and both work-non-work balance and job-related outcomes, whereas no consistent evidence was found regarding health/well-being. Intervention studies on global WTC found moderately strong evidence for a positive causal association with work-non-work balance and no or insufficient evidence for health/well-being and job-related outcomes. Limited to moderately strong cross-sectional evidence was found for positive associations between multidimensional WTC and our outcome categories. Moderately strong cross-sectional evidence was found for positive associations between flextime and all outcome categories. The lack of intervention or longitudinal studies restricts clear causal inferences. This review has shown that there are theoretical and empirical reasons to view WTC as a promising tool for the maintenance of employees' work-non-work balance, health and well-being, and job-related outcomes. At the same time, however, the current state of evidence allows only very limited causal inferences to be made

Revisão sistemática dos estudos epidemiológicos sobre discriminação interpessoal e saúde mental Revisión sistemática de estudios epidemiológicos sobre la discriminación interpersonal y salud mental Systematic review of epidemiological studies on interpersonal discrimination and mental health

Directory of Open Access Journals (Sweden)

Paulo Francisco Mastella Couto

2013-03-01

Full Text Available Foram caracterizados estudos epidemiológicos que avaliaram a relação entre discriminação interpessoal e condições de saúde mental, atualizando revisões prévias sobre o tema. Identificaram-se 34 artigos publicados entre 2000 e 2010 no PubMed, dos quais 68% utilizaram amostras de conveniência e 82% o delineamento transversal. Observaram-se associações positivas e estatisticamente significativas entre discriminação e condições adversas de saúde mental, especialmente uso de substâncias, depressão e transtornos associados ao uso de álcool. Somente um terço dos estudos explicitou um referencial teórico para interpretar as relações examinadas. Similarmente às revisões anteriores, pode-se afirmar que as experiências discriminatórias se associam positiva e consistentemente com desfechos adversos de saúde mental. Entretanto, investigações futuras deverão empregar delineamentos mais robustos para a inferência causal, utilizar instrumentos de discriminação com boas propriedades psicométricas e adotar referencial teórico específico para interpretar os resultados produzidos.Fueron caracterizados los estudios epidemiológicos que evaluaron la relación entre la discriminación interpersonal y salud mental, actualizando revisiones previas sobre el tema. Se identificaron 34 artículos publicados entre 2000 y 2010 a través del PubMed, de los cuales el 68% utilizaron muestras de conveniencia y el 82% fueron estudios transversales. Se observaron asociaciones positivas y estadísticamente significativas entre la discriminación y condiciones adversas de salud mental, especialmente consumo de substancias, depresión y los trastornos asociados al consumo de alcohol. Sólo un tercio de los estudios explicitó un marco teórico para interpretar las relaciones examinadas. Al igual que en las revisiones anteriores, se puede afirmar que las experiencias discriminatorias se asocian positiva y consistentemente con resultados adversos

Vegetarian diet and mental disorders: results from a representative community survey

Science.gov (United States)

2012-01-01

Background The present study investigated associations between vegetarian diet and mental disorders. Methods Participants were drawn from the representative sample of the German Health Interview and Examination Survey and its Mental Health Supplement (GHS-MHS). Completely vegetarian (N = 54) and predominantly vegetarian (N = 190) participants were compared with non-vegetarian participants (N = 3872) and with a non-vegetarian socio-demographically matched subsample (N = 242). Results Vegetarians displayed elevated prevalence rates for depressive disorders, anxiety disorders and somatoform disorders. Due to the matching procedure, the findings cannot be explained by socio-demographic characteristics of vegetarians (e.g. higher rates of females, predominant residency in urban areas, high proportion of singles). The analysis of the respective ages at adoption of a vegetarian diet and onset of a mental disorder showed that the adoption of the vegetarian diet tends to follow the onset of mental disorders. Conclusions In Western cultures vegetarian diet is associated with an elevated risk of mental disorders. However, there was no evidence for a causal role of vegetarian diet in the etiology of mental disorders. PMID:22676203

Mental Health of Survivors of the 2010 Haitian Earthquake Living in the United States

Centers for Disease Control (CDC) Podcasts

Thousands of survivors of the 2010 Haitian Earthquake are currently living in the United States. This podcast features a brief non-disease-specific interview with Dr. Marc Safran, CDC's longest serving psychiatrist, about a few of the mental health challenges such survivors may face.

Methodological Foundations for the Empirical Evaluation of Non-Experimental Methods in Field Settings

Science.gov (United States)

Wong, Vivian C.; Steiner, Peter M.

2015-01-01

Across the disciplines of economics, political science, public policy, and now, education, the randomized controlled trial (RCT) is the preferred methodology for establishing causal inference about program impacts. But randomized experiments are not always feasible because of ethical, political, and/or practical considerations, so non-experimental…

Thinking or feeling? An exploratory study of maternal scaffolding, child mental state talk, and emotion understanding in language-impaired and typically developing school-aged children.

Science.gov (United States)

Yuill, Nicola; Little, Sarah

2018-06-01

Mother-child mental state talk (MST) supports children's developing social-emotional understanding. In typically developing (TD) children, family conversations about emotion, cognition, and causes have been linked to children's emotion understanding. Specific language impairment (SLI) may compromise developing emotion understanding and adjustment. We investigated emotion understanding in children with SLI and TD, in relation to mother-child conversation. Specifically, is cognitive, emotion, or causal MST more important for child emotion understanding and how might maternal scaffolding support this? Nine 5- to 9-year-old children with SLI and nine age-matched typically developing (TD) children, and their mothers. We assessed children's language, emotion understanding and reported behavioural adjustment. Mother-child conversations were coded for MST, including emotion, cognition, and causal talk, and for scaffolding of causal talk. Children with SLI scored lower than TD children on emotion understanding and adjustment. Mothers in each group provided similar amounts of cognitive, emotion, and causal talk, but SLI children used proportionally less cognitive and causal talk than TD children did, and more such child talk predicted better child emotion understanding. Child emotion talk did not differ between groups and did not predict emotion understanding. Both groups participated in maternal-scaffolded causal talk, but causal talk about emotion was more frequent in TD children, and such talk predicted higher emotion understanding. Cognitive and causal language scaffolded by mothers provides tools for articulating increasingly complex ideas about emotion, predicting children's emotion understanding. Our study provides a robust method for studying scaffolding processes for understanding causes of emotion. © 2017 The British Psychological Society.

On parametrised cold dense matter equation of state inference

Science.gov (United States)

Riley, Thomas E.; Raaijmakers, Geert; Watts, Anna L.

2018-04-01

Constraining the equation of state of cold dense matter in compact stars is a major science goal for observing programmes being conducted using X-ray, radio, and gravitational wave telescopes. We discuss Bayesian hierarchical inference of parametrised dense matter equations of state. In particular we generalise and examine two inference paradigms from the literature: (i) direct posterior equation of state parameter estimation, conditioned on observations of a set of rotating compact stars; and (ii) indirect parameter estimation, via transformation of an intermediary joint posterior distribution of exterior spacetime parameters (such as gravitational masses and coordinate equatorial radii). We conclude that the former paradigm is not only tractable for large-scale analyses, but is principled and flexible from a Bayesian perspective whilst the latter paradigm is not. The thematic problem of Bayesian prior definition emerges as the crux of the difference between these paradigms. The second paradigm should in general only be considered as an ill-defined approach to the problem of utilising archival posterior constraints on exterior spacetime parameters; we advocate for an alternative approach whereby such information is repurposed as an approximative likelihood function. We also discuss why conditioning on a piecewise-polytropic equation of state model - currently standard in the field of dense matter study - can easily violate conditions required for transformation of a probability density distribution between spaces of exterior (spacetime) and interior (source matter) parameters.

Mental Health Services to State Corrections Inmates. Staff Brief 86-10.

Science.gov (United States)

Henkel, Jane R.

This report was written for the Advisory Committee on Mentally Ill Inmates of the Wisconsin State Legislative Council's Special Committee on Mental Health Issues. It describes mental health services to inmates of Wisconsin's state prisons. Part I describes the organization of state level responsibilities for corrections, including the state…

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Science.gov (United States)

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015

Modelling the effect of religion on human empathy based on an adaptive temporal–causal network model

OpenAIRE

van Ments, Laila; Roelofsma, Peter; Treur, Jan

2018-01-01

Background Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. Methods The current study integrates a number of these perspectives into one adaptive temporal–causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. Results By first developing a conceptual representation of a...

How Medicaid agencies administer mental health services: results from a 50-state survey.

Science.gov (United States)

Verdier, James; Barrett, Allison

2008-10-01

This brief report describes some notable variations in how state Medicaid agencies administer and fund Medicaid mental health services. Hour-long telephone interviews were conducted with all state and District of Columbia Medicaid directors or their designees. Responses indicated that Medicaid and mental health agencies were located within the same umbrella agency in 28 states, potentially facilitating collaboration. The mental health agency provided funding for some Medicaid mental health services in 32 states, and counties provided such funding in 22 states. Medicaid agencies generally delegated more authority to state mental health agencies in states where some Medicaid funding came from mental health sources and also in states where both agencies were in the same umbrella agency. The increasing role of Medicaid in funding state mental health services, combined with new federal limits on Medicaid financing of these services, underscores the importance of interagency collaboration and better alignment of Medicaid and mental health responsibilities.

Mental health training programmes for non-mental health trained professionals coming into contact with people with mental ill health: a systematic review of effectiveness.

Science.gov (United States)

Booth, Alison; Scantlebury, Arabella; Hughes-Morley, Adwoa; Mitchell, Natasha; Wright, Kath; Scott, William; McDaid, Catriona

2017-05-25

The police and others in occupations where they come into close contact with people experiencing/with mental ill health, often have to manage difficult and complex situations. Training is needed to equip them to recognise and assist when someone has a mental health issue or learning/intellectual disability. We undertook a systematic review of the effectiveness of training programmes aimed at increasing knowledge, changing behaviour and/or attitudes of the trainees with regard to mental ill health, mental vulnerability, and learning disabilities. Databases searched from 1995 onwards included: ASSIA, Cochrane Central Register of Controlled Clinical Trials (CENTRAL), Criminal Justice Abstracts, Embase, ERIC, MEDLINE, PsycINFO, Social Science Citation Index. Courses, training, or learning packages aimed at helping police officers and others who interact with the public in a similar way to deal with people with mental health problems were included. Primary outcomes were change in practice and change in outcomes for the groups of people the trainees come into contact with. Systematic reviews, randomised controlled trials (RCTs) and non- randomised controlled trials (non-RCTs) were included and quality assessed. In addition non-comparative evaluations of training for police in England were included. From 8578 search results, 19 studies met the inclusion criteria: one systematic review, 12 RCTs, three prospective non-RCTs, and three non-comparative studies. The training interventions identified included broad mental health awareness training and packages addressing a variety of specific mental health issues or conditions. Trainees included police officers, teachers and other public sector workers. Some short term positive changes in behaviour were identified for trainees, but for the people the trainees came into contact with there was little or no evidence of benefit. A variety of training programmes exist for non-mental health professionals who come into contact with

College students' stigmatization of people with mental illness: familiarity, implicit person theory, and attribution.

Science.gov (United States)

Lyndon, Amy E; Crowe, Allison; Wuensch, Karl L; McCammon, Susan L; Davis, Karen B

2016-11-25

Stigma associated with mental illness (MI) results in underutilization of mental health care. We must understand factors contributing to stigma to shape anti-stigma campaigns. To investigate the factors influencing stigma in university students. Undergraduate psychology students completed measures on causal attribution, stigma, social distance, implicit person theory (IPT), and familiarity. The hypothesis was partially supported; people who felt personality traits were unchangeable (i.e. entity IPT) were more likely to stigmatize individuals with mental disorders and desired more social distance from them. Familiarity with people with a MI individually predicted less desire for social distance, yet the redundancy of the predictors made the effect of familiarity on stigma fall just short of statistical significance. Judgments of biogenetic causal attribution were related to higher stigma levels, but not so when familiarity and IPT were taken into account. Educational campaigns may be effective by focusing on aspects of MI highlighting similarity with non-diagnosed people, and that people with MI can recover.

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

Science.gov (United States)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

Inference of directed climate networks: role of instability of causality estimation methods

Czech Academy of Sciences Publication Activity Database

Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

2013-01-01

Roč. 15, - (2013), s. 12987 ISSN 1607-7962. [European Geosciences Union General Assembly 2013. 07.04.2013-12.04.2013, Vienna] R&D Projects: GA ČR GCP103/11/J068 Institutional support: RVO:67985807 Keywords : causality analysis * climate networks Subject RIV: BB - Applied Statistics, Operational Research

Non-mental health workers' attitudes and social distance towards ...

African Journals Online (AJOL)

Non-mental health workers' attitudes and social distance towards people with mental illness in a. Nigerian teaching hospital. Olatunji F. Ainaa, O. Yewande Oshodia, Adebayo R. Erinfolamia, Joseph D. Adeyemia, and Tajudeen. F Suleimanb a Department of Psychiatry, College of Medicine, University of Lagos, PMB 12003, ...

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

Science.gov (United States)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger

Recent Advances in System Reliability Signatures, Multi-state Systems and Statistical Inference

CERN Document Server

Frenkel, Ilia

2012-01-01

Recent Advances in System Reliability discusses developments in modern reliability theory such as signatures, multi-state systems and statistical inference. It describes the latest achievements in these fields, and covers the application of these achievements to reliability engineering practice. The chapters cover a wide range of new theoretical subjects and have been written by leading experts in reliability theory and its applications.  The topics include: concepts and different definitions of signatures (D-spectra),  their  properties and applications  to  reliability of coherent systems and network-type structures; Lz-transform of Markov stochastic process and its application to multi-state system reliability analysis; methods for cost-reliability and cost-availability analysis of multi-state systems; optimal replacement and protection strategy; and statistical inference. Recent Advances in System Reliability presents many examples to illustrate the theoretical results. Real world multi-state systems...

Changes in mental state associated with prison environments: a systematic review.

Science.gov (United States)

Walker, J; Illingworth, C; Canning, A; Garner, E; Woolley, J; Taylor, P; Amos, T

2014-06-01

To develop an understanding of the stability of mental health during imprisonment through review of existing research evidence relating physical prison environment to mental state changes in prisoners. A systematic literature search was conducted looking at changes in mental state and how this related to various aspects of imprisonment and the prison environment. Fifteen longitudinal studies were found, and from these, three broad themes were delineated: being imprisoned and aspects of the prison regime; stage of imprisonment and duration of sentence; and social density. Reception into prison results in higher levels of psychiatric symptoms that seem to improve over time; otherwise, duration of imprisonment appears to have no significant impact on mental health. Regardless of social density, larger prisons are associated with poorer mental state, as are extremes of social density. There are large gaps in the literature relating prison environments to changes in mental state; in particular, high-quality longitudinal studies are needed. Existing research suggests that although entry to prison may be associated with deterioration in mental state, it tends to improve with time. Furthermore, overcrowding, ever more likely as prison populations rise, is likely to place a particular burden on mental health services. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Seeing mental states: An experimental strategy for measuring the observability of other minds

Science.gov (United States)

Becchio, Cristina; Koul, Atesh; Ansuini, Caterina; Bertone, Cesare; Cavallo, Andrea

2018-03-01

Is it possible to perceive others' mental states? Are mental states visible in others' behavior? In contrast to the traditional view that mental states are hidden and not directly accessible to perception, in recent years a phenomenologically-motivated account of social cognition has emerged: direct social perception. However, despite numerous published articles that both defend and critique direct perception, researchers have made little progress in articulating the conditions under which direct perception of others' mental states is possible. This paper proposes an empirically anchored approach to the observability of others' mentality - not just in the weak sense of discussing relevant empirical evidence for and against the phenomenon of interest, but also, and more specifically, in the stronger sense of identifying an experimental strategy for measuring the observability of mental states and articulating the conditions under which mental states are observable. We conclude this article by reframing the problem of direct perception in terms of establishing a definable and measurable relationship between movement features and perceived mental states.

Causal beliefs about depression in different cultural groups – What do cognitive psychological theories of causal learning and reasoning predict?

Directory of Open Access Journals (Sweden)

York eHagmayer

2014-11-01

Full Text Available Cognitive psychological research focusses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analysed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given.

Modeling the Effect of Religion on Human Empathy Based on an Adaptive Temporal-Causal Network Model

OpenAIRE

van Ments, L.I.; Roelofsma, P.H.M.P.; Treur, J.

2018-01-01

Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on lit...

Preschoolers prefer to learn causal information

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Aubry eAlvarez

2015-02-01

Full Text Available Young children, in general, appear to have a strong drive to explore the environment in ways that reveal its underlying causal structure. But are they really attuned specifically to casual information in this quest for understanding, or do they show equal interest in other types of non-obvious information about the world? To answer this question, we introduced 20 three-year-old children to two puppets who were anxious to tell the child about a set of novel artifacts and animals. One puppet consistently described causal properties of the items while the other puppet consistently described carefully matched non-causal properties of the same items. After a familiarization period in which children learned which type of information to expect from each informant, children were given the opportunity to choose which they wanted to hear describe each of eight pictured test items. On average, children chose to hear from the informant that provided causal descriptions on 72% of the trials. This preference for causal information has important implications for explaining the role of conceptual information in supporting early learning and may suggest means for maximizing interest and motivation in young children.

How to Make Correct Predictions in False Belief Tasks without Attributing False Beliefs: An Analysis of Alternative Inferences and How to Avoid Them

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Ricardo Augusto Perera

2018-04-01

Full Text Available The use of new paradigms of false belief tasks (FBT allowed to reduce the age of children who pass the test from the previous 4 years in the standard version to only 15 months or even a striking 6 months in the nonverbal modification. These results are often taken as evidence that infants already possess an—at least implicit—theory of mind (ToM. We criticize this inferential leap on the grounds that inferring a ToM from the predictive success on a false belief task requires to assume as premise that a belief reasoning is a necessary condition for correct action prediction. It is argued that the FBT does not satisfactorily constrain the predictive means, leaving room for the use of belief-independent inferences (that can rely on the attribution of non-representational mental states or the consideration of behavioral patterns that dispense any reference to other minds. These heuristics, when applied to the FBT, can achieve the same predictive success of a belief-based inference because information provided by the test stimulus allows the recognition of particular situations that can be subsumed by their ‘laws’. Instead of solving this issue by designing a single experimentum crucis that would render unfeasible the use of non-representational inferences, we suggest the application of a set of tests in which, although individually they can support inferences dissociated from a ToM, only an inference that makes use of false beliefs is able to correctly predict all the outcomes.

Changes in mental state and behaviour in Huntington's disease.

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Eddy, Clare M; Parkinson, Ellice G; Rickards, Hugh E

2016-11-01

Changes in mental state and behaviour have been acknowledged in Huntington's disease since the original monograph in 1872 provided evidence of disinhibition and impaired social cognition. Behavioural problems can manifest before obvious motor symptoms and are frequently the most disabling part of the illness. Although pharmacological treatments are used routinely for psychiatric difficulties in Huntington's disease, the scientific evidence base for their use is somewhat sparse. Moreover, effective treatments for apathy and cognitive decline do not currently exist. Understanding the social cognitive impairments associated with Huntington's disease can assist management, but related therapeutic interventions are needed. Future research should aim to design rating scales for behaviour and mental state in Huntington's disease that can detect change in clinical trials. Generally, communication and understanding of behaviour and mental state in Huntington's would be enhanced by a clear conceptual framework that unifies ideas around movement, cognition, emotion, behaviour, and mental state, reflecting both the experience of the patient and their underlying neuropathology. Copyright © 2016 Elsevier Ltd. All rights reserved.

BIOFEEDBACK AS A METHOD FOR STUDENTS’ MENTAL STATE ASSESSMENT

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M. Yu. Ababkova

2017-01-01

Full Text Available Introduction. Neurotechnologies based on the principles of a nervous system functioning are being introduced into modern educational process more and more actively. Neurotechnology-based devices give the chance to develop new educational products; to enlarge the content of education by means of transition from text, graphic and sound content filling of educational process to use of tactile, motor, emotional, and other content. One of the most perspective neurotechnologies for the field of education is the method of biofeedback (BF which enables to define students’ mental state, change various physiological processes proceeding from the obtained data, correct educational process, and improve its quality and effectiveness.The aim of the present publication is to identify the opportunities of the biofeedback method application for educational purposes.Methodology and research methods. A pilot study on the basis of biofeedback technique was conducted in order to study the influence of active learning methods on students’ mental state mastering in specialty “Advertising and Public Relations”. H. Eysenck’s PEN Model was used to form focus-groups (control and experimental; psychophysiological technique CMS (Current Mental State was applied for results processing. Also, such methods as comparative analysis, induction and generalization were used.Results. A true picture of psychological attributes of students’ mental condition has been received for efficient studying of the current psychological state on psychophysiological functions, and training active methods impact on a condition of mentality of students according to the results of cardiorhythmogram.The main results of a pilot research were quantitative data (as percentage points of the current mental and psychological conditions of examinees. The obtained results have reflected the degree of attributes manifestation such as general adaptive resource, degree of mobility (lability of

Efficient design and inference in distributed Bayesian networks: an overview

NARCIS (Netherlands)

de Oude, P.; Groen, F.C.A.; Pavlin, G.; Bezhanishvili, N.; Löbner, S.; Schwabe, K.; Spada, L.

2011-01-01

This paper discusses an approach to distributed Bayesian modeling and inference, which is relevant for an important class of contemporary real world situation assessment applications. By explicitly considering the locality of causal relations, the presented approach (i) supports coherent distributed

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

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Wang, Yi Kan; Hurley, Daniel G; Schnell, Santiago; Print, Cristin G; Crampin, Edmund J

2013-01-01

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

Predicting everyday functional abilities of dementia patients with the Mini-Mental State Examination.

Science.gov (United States)

Razani, Jill; Wong, Jennifer T; Dafaeeboini, Natalia; Edwards-Lee, Terri; Lu, Po; Alessi, Cathy; Josephson, Karen

2009-03-01

The Mini-Mental State Examination is a widely used cognitive screening measure. The purpose of the present study was to assess how 5 specific clusters of Mini-Mental State Examination items (ie, subscores) correlate with and predict specific areas of daily functioning in dementia patients, 61 patients with varied forms of dementia were administered the Mini-Mental State Examination and an observation-based daily functional test (the Direct Assessment of Functional Status). The results revealed that the orientation and attention subscores of the Mini-Mental State Examination correlated most significantly with most functional domains. The Mini-Mental State Examination language items correlated with all but the shopping and time orientation tasks, while the Mini-Mental State Examination recall items correlated with the Direct Assessment of Functional Status time orientation and shopping tasks. Stepwise regression analyses found that among the Mini-Mental State Examination subscores, orientation was the single, best independent predictor of daily functioning.

Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

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DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

2017-08-01

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All

An Exploration of Secondary Students' Mental States When Learning about Acids and Bases

Science.gov (United States)

Liu, Chia-Ju; Hou, I-Lin; Chiu, Houn-Lin; Treagust, David F.

2014-01-01

This study explored factors of students' mental states, including emotion, intention, internal mental representation, and external mental representation, which can affect their learning performance. In evaluating students' mental states during the science learning process and the relationship between mental states and learning…

Evolution of public and non-profit funding for mental health research in France between 2007 and 2011.

Science.gov (United States)

Gandré, Coralie; Prigent, Amélie; Kemel, Marie-Louise; Leboyer, Marion; Chevreul, Karine

2015-12-01

Since 2007, actions have been undertaken in France to foster mental health research. Our objective was to assess their utility by estimating the evolution of public and non-profit funding for mental health research between 2007 and 2011, both in terms of total funding and the share of health research budgets. Public and non-profit funding was considered. Core funding from public research institutions was determined through a top-down approach by multiplying their total budget by the ratio of the number of psychiatry-related publications to the total number of publications focusing on health issues. A bottom-up method was used to estimate the amount of project-based grants and funding by non-profit organizations, which were directly contacted to obtain this information. Public and non-profit funding for mental health research increased by a factor of 3.4 between 2007 and 2011 reaching €84.8 million, while the share of health research funding allocated to mental health research nearly doubled from 2.2% to 4.1%. Public sources were the main contributors representing 94% of the total funding. Our results have important implications for policy makers, as they suggest that actions specifically aimed at prioritizing mental health research are effective in increasing research funding. There is therefore an urgent need to further undertake such actions as funding in France remains particularly low compared to the United Kingdom and the United States, despite the fact that the epidemiological and economic burden represented by mental disorders is expected to grow rapidly in the coming years. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Reinforcement learning or active inference?

Science.gov (United States)

Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

2009-07-29

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Reinforcement learning or active inference?

Directory of Open Access Journals (Sweden)

Karl J Friston

2009-07-01

Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Sex differences in the inference and perception of causal relations within a video game

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Michael E. Young

2014-08-01

Full Text Available The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations.

Sex differences in the inference and perception of causal relations within a video game.

Science.gov (United States)

Young, Michael E

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations.

The Preservation of Cued Recall in the Acute Mentally Fatigued State: A Randomised Crossover Study.

Science.gov (United States)

Flindall, Ian Richard; Leff, Daniel Richard; Pucks, Neysan; Sugden, Colin; Darzi, Ara

2016-01-01

The objective of this study is to investigate the impact of acute mental fatigue on the recall of clinical information in the non-sleep-deprived state. Acute mental fatigue in the non-sleep-deprived subject is rarely studied in the medical workforce. Patient handover has been highlighted as an area of high risk especially in fatigued subjects. This study evaluates the deterioration in recall of clinical information over 2 h with cognitively demanding work in non-sleep-deprived subjects. A randomised crossover study involving twenty medical students assessed free (presentation) and cued (MCQ) recall of clinical case histories at 0 and 2 h under low and high cognitive load using the N-Back task. Acute mental fatigue was assessed through the Visual Analogue Scale, Stanford Scale and NASA-TLX Mental Workload Rating Scale. Free recall is significantly impaired by increased cognitive load (p cued recall under high and low cognitive load conditions (p = 1). This study demonstrates the loss of clinical information over a short time period involving a mentally fatiguing, high cognitive load task. Free recall for the handover of clinical information is unreliable. Memory cues maintain recall of clinical information. This study provides evidence towards the requirement for standardisation of a structured patient handover. The use of memory cues (involving recognition memory and cued recall methodology) would be beneficial in a handover checklist to aid recall of clinical information and supports evidence for their adoption into clinical practice.

Intimate Partner Violence and Welfare Participation: A Longitudinal Causal Analysis

Science.gov (United States)

Cheng, Tyrone C.

2013-01-01

This longitudinal study examined the temporal-ordered causal relationship between intimate partner violence (IPV), five mental disorders (depression, generalized anxiety disorder, social phobia, panic attack, posttraumatic stress disorder [PTSD]), alcohol abuse/dependence, drug abuse/ dependence, treatment seeking (from physician, counselor, and…

Normative and descriptive accounts of the influence of power and contingency on causal judgement.

Science.gov (United States)

Perales, José C; Shanks, David R

2003-08-01

The power PC theory (Cheng, 1997) is a normative account of causal inference, which predicts that causal judgements are based on the power p of a potential cause, where p is the cause-effect contingency normalized by the base rate of the effect. In three experiments we demonstrate that both cause-effect contingency and effect base-rate independently affect estimates in causal learning tasks. In Experiment 1, causal strength judgements were directly related to power p in a task in which the effect base-rate was manipulated across two positive and two negative contingency conditions. In Experiments 2 and 3 contingency manipulations affected causal estimates in several situations in which power p was held constant, contrary to the power PC theory's predictions. This latter effect cannot be explained by participants' conflation of reliability and causal strength, as Experiment 3 demonstrated independence of causal judgements and confidence. From a descriptive point of view, the data are compatible with Pearce's (1987) model, as well as with several other judgement rules, but not with the Rescorla-Wagner (Rescorla & Wagner, 1972) or power PC models.

Identifying the Average Causal Mediation Effects with Multiple Mediators in the Presence of Treatment Non-Compliance

Science.gov (United States)

Park, Soojin

2015-01-01

Identifying the causal mechanisms is becoming more essential in social and medical sciences. In the presence of treatment non-compliance, the Intent-To-Treated effect (hereafter, ITT effect) is identified as long as the treatment is randomized (Angrist et al., 1996). However, the mediated portion of effect is not identified without additional…

Causal Relationship between Construction Production and GDP in Turkey

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Hakkı Kutay Bolkol

2015-12-01

Full Text Available This study empirically investigates the causal relationship between construction production and GDP for Turkey during 2005Q1-2013Q4 period. Because it is found that, there is no cointegration which means there is no long run relationship between variables, VAR Granger Causality Method is used to test the causality in short run. The findings reveal that, the causality runs from GDP to Building Production and Building Production to Non-Building Production (i.e. bidirectional relationship. Findings of this paper suggest that, because there is no long run relationship between Construction Production (Building and Non-Building and GDP and also in short run the causality runs from GDP to Construction Production, the growth strategy based on mainly Construction Sector growth is not a good idea for Turkey.

Causal Relationship between Construction Production and GDP in Turkey

Directory of Open Access Journals (Sweden)

Hakkı Kutay Bolkol

2015-09-01

Full Text Available This study empirically investigates the causal relationship between construction production and GDP for Turkey during 2005Q1-2013Q4 period. Because it is found that, there is no cointegration which means there is no long run relationship between variables, VAR Granger Causality Method is used to test the causality in short run. The findings reveal that, the causality runs from GDP to Building Production and Building Production to Non-Building Production (i.e. bidirectional relationship. Findings of this paper suggest that, because there is no long run relationship between Construction Production (Building and Non-Building and GDP and also in short run the causality runs from GDP to Construction Production, the growth strategy based on mainly Construction Sector growth is not a good idea for Turkey.

Estimating mental states of a depressed person with bayesian networks

NARCIS (Netherlands)

Klein, Michel C.A.; Modena, Gabriele

2013-01-01

In this work in progress paper we present an approach based on Bayesian Networks to model the relationship between mental states and empirical observations in a depressed person. We encode relationships and domain expertise as a Hierarchical Bayesian Network. Mental states are represented as latent

From Neutron Star Observables to the Equation of State. II. Bayesian Inference of Equation of State Pressures

Science.gov (United States)

Raithel, Carolyn A.; Özel, Feryal; Psaltis, Dimitrios

2017-08-01

One of the key goals of observing neutron stars is to infer the equation of state (EoS) of the cold, ultradense matter in their interiors. Here, we present a Bayesian statistical method of inferring the pressures at five fixed densities, from a sample of mock neutron star masses and radii. We show that while five polytropic segments are needed for maximum flexibility in the absence of any prior knowledge of the EoS, regularizers are also necessary to ensure that simple underlying EoS are not over-parameterized. For ideal data with small measurement uncertainties, we show that the pressure at roughly twice the nuclear saturation density, {ρ }{sat}, can be inferred to within 0.3 dex for many realizations of potential sources of uncertainties. The pressures of more complicated EoS with significant phase transitions can also be inferred to within ˜30%. We also find that marginalizing the multi-dimensional parameter space of pressure to infer a mass-radius relation can lead to biases of nearly 1 km in radius, toward larger radii. Using the full, five-dimensional posterior likelihoods avoids this bias.

Darwin revisited: The vagus nerve is a causal element in controlling recognition of other's emotions.

Science.gov (United States)

Colzato, Lorenza S; Sellaro, Roberta; Beste, Christian

2017-07-01

Charles Darwin proposed that via the vagus nerve, the tenth cranial nerve, emotional facial expressions are evolved, adaptive and serve a crucial communicative function. In line with this idea, the later-developed polyvagal theory assumes that the vagus nerve is the key phylogenetic substrate that regulates emotional and social behavior. The polyvagal theory assumes that optimal social interaction, which includes the recognition of emotion in faces, is modulated by the vagus nerve. So far, in humans, it has not yet been demonstrated that the vagus plays a causal role in emotion recognition. To investigate this we employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique that modulates brain activity via bottom-up mechanisms. A sham/placebo-controlled, randomized cross-over within-subjects design was used to infer a causal relation between the stimulated vagus nerve and the related ability to recognize emotions as indexed by the Reading the Mind in the Eyes Test in 38 healthy young volunteers. Active tVNS, compared to sham stimulation, enhanced emotion recognition for easy items, suggesting that it promoted the ability to decode salient social cues. Our results confirm that the vagus nerve is causally involved in emotion recognition, supporting Darwin's argumentation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ab initio Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism

KAUST Repository

Zenil, Hector

2018-02-18

To extract and learn representations leading to generative mechanisms from data, especially without making arbitrary decisions and biased assumptions, is a central challenge in most areas of scientific research particularly in connection to current major limitations of influential topics and methods of machine and deep learning as they have often lost sight of the model component. Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative mechanisms. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our method and find that it can disentangle observations from discrete dynamic systems, random and complex networks. We think that these causal inference techniques can contribute as key pieces of information for estimations of probability distributions complementing other more statistical-oriented techniques that otherwise lack model inference capabilities.

Impact of the non-contributory social pension program 70 y más on older adults' mental well-being.

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Aarón Salinas-Rodríguez

Full Text Available BACKGROUND: In 2007, a non-contributory pension program was launched in rural areas of Mexico. The program consisted in a non-conditional cash transfer of US$40 monthly to all older adults (OA aged 70 and over. We evaluate the effect of the program on mental well-being of its beneficiaries. METHODS AND FINDINGS: Quantitative and qualitative methods were used. For the quantitative component, we used the selection criteria established by the program (age and locality size to form the Intervention (OA aged 70-74 residing in rural localities, <2500 inhabitants and Control groups (OA aged 70-74, in localities with 2501-2700 inhabitants. Baseline data collection was conducted in 2007 where 5,465 OA were interviewed. The follow-up survey was conducted in 2008, and it was possible to interview 5,270 OA, with a response rate of 96%. A difference-in-difference linear probability model with individual fixed effect was used to estimate the impact of the program on mental well-being indicators. In 2009 a qualitative component was designed to explore possible causal pathways of such effect. RESULTS: After a year of exposure, the program had a significant effect on reduction of depressive symptoms (β = -0.06, CI95% -0.12; -0.01 and an increase in empowerment indicators: OA participated in important household decisions (β = 0.09, CI95% 0.03;0.15; and OA participated in household decisions pertaining to expenses (β = 0.11, CI95% 0.05;0.18. Qualitative analysis found a strong trend showing a reduction of sadness, and feeling of increasing empowerment. CONCLUSIONS: These results suggest that a non-conditional transfer in older ages have an impact beyond the economic sphere, impacting even the mental well-being. This effect could be explained because the pension produces feelings of safety and welfare. It is recommendable that governments should invest efforts towards universalizing the non-contributory pension programs in order to ensure a basic

Causal localizations in relativistic quantum mechanics

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Castrigiano, Domenico P. L.; Leiseifer, Andreas D.

2015-07-01

Causal localizations describe the position of quantum systems moving not faster than light. They are constructed for the systems with finite spinor dimension. At the center of interest are the massive relativistic systems. For every positive mass, there is the sequence of Dirac tensor-localizations, which provides a complete set of inequivalent irreducible causal localizations. They obey the principle of special relativity and are fully Poincaré covariant. The boosters are determined by the causal position operator and the other Poincaré generators. The localization with minimal spinor dimension is the Dirac localization. Thus, the Dirac equation is derived here as a mere consequence of the principle of causality. Moreover, the higher tensor-localizations, not known so far, follow from Dirac's localization by a simple construction. The probability of localization for positive energy states results to be described by causal positive operator valued (PO-) localizations, which are the traces of the causal localizations on the subspaces of positive energy. These causal Poincaré covariant PO-localizations for every irreducible massive relativistic system were, all the more, not known before. They are shown to be separated. Hence, the positive energy systems can be localized within every open region by a suitable preparation as accurately as desired. Finally, the attempt is made to provide an interpretation of the PO-localization operators within the frame of conventional quantum mechanics attributing an important role to the negative energy states.

Gang membership and substance use: guilt as a gendered causal pathway.

Science.gov (United States)

Coffman, Donna L; Melde, Chris; Esbensen, Finn-Aage

2015-03-01

We examine whether anticipated guilt for substance use is a gendered mechanism underlying the noted enhancement effect of gang membership on illegal drug use. We also demonstrate a method for making stronger causal inferences when assessing mediation in the presence of moderation and time-varying confounding. We estimate a series of inverse propensity weighted models to obtain unbiased estimates of mediation in the presence of confounding of the exposure (i.e., gang membership) and mediator (i.e., anticipated guilt) using three waves of data from a multi-site panel study of a law-related education program for youth ( N =1,113). The onset of gang membership significantly decreased anticipated substance use guilt among both male and female respondents. This reduction was significantly associated with increased frequency of substance use only for female respondents, however, suggesting that gender moderates the mechanism through which gang membership influences substance use. Criminologists are often concerned with identifying causal pathways for antisocial and/or delinquent behavior, but confounders of the exposure, mediator, and outcome often interfere with efforts to assess mediation. Many new approaches have been proposed for strengthening causal inference for mediation effects. After controlling for confounding using inverse propensity weighting, our results suggest that interventions aimed at reducing substance use by current and former female gang members should focus on the normative aspects of these behaviors.

Ancestral state reconstruction infers phytopathogenic origins of sooty blotch and flyspeck fungi on apple.

Science.gov (United States)

Ismail, Siti Izera; Batzer, Jean Carlson; Harrington, Thomas C; Crous, Pedro W; Lavrov, Dennis V; Li, Huanyu; Gleason, Mark L

2016-01-01

Members of the sooty blotch and flyspeck (SBFS) complex are epiphytic fungi in the Ascomycota that cause economically damaging blemishes of apples worldwide. SBFS fungi are polyphyletic, but approx. 96% of SBFS species are in the Capnodiales. Evolutionary origins of SBFS fungi remain unclear, so we attempted to infer their origins by means of ancestral state reconstruction on a phylogenetic tree built utilizing genes for the nuc 28S rDNA (approx. 830 bp from near the 59 end) and the second largest subunit of RNA polymerase II (RPB2). The analyzed taxa included the well-known genera of SBFS as well as non-SBFS fungi from seven families within the Capnodiales. The non-SBFS taxa were selected based on their distinct ecological niches, including plant-parasitic and saprophytic species. The phylogenetic analyses revealed that most SBFS species in the Capnodiales are closely related to plant-parasitic fungi. Ancestral state reconstruction provided strong evidence that plant-parasitic fungi were the ancestors of the major SBFS lineages. Knowledge gained from this study may help to better understand the ecology and evolution of epiphytic fungi. © 2016 by The Mycological Society of America.

Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

KAUST Repository

Triantafillou, Sofia; Lagani, Vincenzo; Heinze-Deml, Christina; Schmidt, Angelika; Tegner, Jesper; Tsamardinos, Ioannis

2017-01-01

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated  causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

KAUST Repository

Triantafillou, Sofia

2017-09-29

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated  causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.

Impaired self-agency inferences in schizophrenia: The role of cognitive capacity and causal reasoning style.

Science.gov (United States)

Prikken, M; van der Weiden, A; Kahn, R S; Aarts, H; van Haren, N E M

2018-01-01

The sense of self-agency, i.e., experiencing oneself as the cause of one's own actions, is impaired in patients with schizophrenia. Normally, inferences of self-agency are enhanced when actual outcomes match with pre-activated outcome information, where this pre-activation can result from explicitly set goals (i.e., goal-based route) or implicitly primed outcome information (i.e., prime-based route). Previous research suggests that patients show specific impairments in the prime-based route, implicating that they do not rely on matches between implicitly available outcome information and actual action-outcomes when inferring self-agency. The question remains: Why? Here, we examine whether neurocognitive functioning and self-serving bias (SSB) may explain abnormalities in patients' agency inferences. Thirty-six patients and 36 healthy controls performed a commonly used agency inference task to measure goal- and prime-based self-agency inferences. Neurocognitive functioning was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS) and the SSB was assessed with the Internal Personal and Situational Attributions Questionnaire. Results showed a substantial smaller effect of primed outcome information on agency experiences in patients compared with healthy controls. Whereas patients and controls differed on BACS and marginally on SSB scores, these differences were not related to patients' impairments in prime-based agency inferences. Patients showed impairments in prime-based agency inferences, thereby replicating previous studies. This finding could not be explained by cognitive dysfunction or SSB. Results are discussed in the context of the recent surge to understand and examine deficits in agency experiences in schizophrenia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

A preliminary study of the mini-mental state examination in a Spanish child population.

Science.gov (United States)

Rubial-Alvarez, Sandra; Machado, María-Clara; Sintas, Elena; de Sola, Susana; Böhm, Peter; Peña-Casanova, Jordi

2007-11-01

The Mini-Mental State Examination is one of the most widely used screening tests for the adult population in daily neurologic practice. The aim of this study was to describe and to analyze the results of the Mini-Mental State Examination administered to Spanish children and to assess the relationship between Mini-Mental State Examination scores and the child's mental age/intelligence quotient. The study population included 181 children whose ages ranged between 4 and 12 years. The neuropsychologic battery consisted of the Mini-Mental State Examination and Kaufman Brief Intelligence Test. Percentiles were obtained for the Mini-Mental State Examination total score according to age ranges. Performance gradually increased from 4 to 10 years of age when a plateau in the total Mini-Mental State Examination score was reached. At the age of 6 years, results exceeded 24 on average. Pairwise mean comparisons showed statistically significant differences between the age groups (P Mini-Mental State Examination score correlated significantly with the child's chronologic (r = 0.80, P mental (r = 0.76, P Mini-Mental State Examination in a Spanish child population as well as a first step for the assessment of the usefulness of this instrument as a cognitive screening tool for children's development.

Inferring the Gibbs state of a small quantum system

International Nuclear Information System (INIS)

Rau, Jochen

2011-01-01

Gibbs states are familiar from statistical mechanics, yet their use is not limited to that domain. For instance, they also feature in the maximum entropy reconstruction of quantum states from incomplete measurement data. Outside the macroscopic realm, however, estimating a Gibbs state is a nontrivial inference task, due to two complicating factors: the proper set of relevant observables might not be evident a priori; and whenever data are gathered from a small sample only, the best estimate for the Lagrange parameters is invariably affected by the experimenter's prior bias. I show how the two issues can be tackled with the help of Bayesian model selection and Bayesian interpolation, respectively, and illustrate the use of these Bayesian techniques with a number of simple examples.

Theory of mind in the wild: toward tackling the challenges of everyday mental state reasoning.

Directory of Open Access Journals (Sweden)

Annie E Wertz

Full Text Available A complete understanding of the cognitive systems underwriting theory of mind (ToM abilities requires articulating how mental state representations are generated and processed in everyday situations. Individuals rarely announce their intentions prior to acting, and actions are often consistent with multiple mental states. In order for ToM to operate effectively in such situations, mental state representations should be generated in response to certain actions, even when those actions occur in the presence of mental state content derived from other aspects of the situation. Results from three experiments with preschool children and adults demonstrate that mental state information is indeed generated based on an approach action cue in situations that contain competing mental state information. Further, the frequency with which participants produced or endorsed explanations that include mental states about an approached object decreased when the competing mental state information about a different object was made explicit. This set of experiments provides some of the first steps toward identifying the observable action cues that are used to generate mental state representations in everyday situations and offers insight into how both young children and adults processes multiple mental state representations.

Network inference from functional experimental data (Conference Presentation)

Science.gov (United States)

Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.

2016-03-01

Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic

Statistical inference a short course

CERN Document Server

Panik, Michael J

2012-01-01

A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causal

Sympathoneural and Adrenomedullary Responses to Mental Stress

Science.gov (United States)

Carter, Jason R.; Goldstein, David S.

2017-01-01

This concept-based review provides historical perspectives and updates about sympathetic noradrenergic and sympathetic adrenergic responses to mental stress. The topic of this review has incited perennial debate, because of disagreements over definitions, controversial inferences, and limited availability of relevant measurement tools. The discussion begins appropriately with Cannon's "homeostasis" and his pioneering work in the area. This is followed by mental stress as a scientific idea and the relatively new notions of allostasis and allostatic load. Experimental models of mental stress in rodents and humans are discussed, with particular attention to ethical constraints in humans. Sections follow on sympathoneural to mental stress, reactivity of catecholamine systems, clinical pathophysiologic states, and the cardiovascular reactivity hypothesis. Future advancement of the field will require integrative approaches and coordinated efforts between physiologists and psychologists on this interdisciplinary topic. PMID:25589266

Convergent cross-mapping and pairwise asymmetric inference.

Science.gov (United States)

McCracken, James M; Weigel, Robert S

2014-12-01

Convergent cross-mapping (CCM) is a technique for computing specific kinds of correlations between sets of times series. It was introduced by Sugihara et al. [Science 338, 496 (2012).] and is reported to be "a necessary condition for causation" capable of distinguishing causality from standard correlation. We show that the relationships between CCM correlations proposed by Sugihara et al. do not, in general, agree with intuitive concepts of "driving" and as such should not be considered indicative of causality. It is shown that the fact that the CCM algorithm implies causality is a function of system parameters for simple linear and nonlinear systems. For example, in a circuit containing a single resistor and inductor, both voltage and current can be identified as the driver depending on the frequency of the source voltage. It is shown that the CCM algorithm, however, can be modified to identify relationships between pairs of time series that are consistent with intuition for the considered example systems for which CCM causality analysis provided nonintuitive driver identifications. This modification of the CCM algorithm is introduced as "pairwise asymmetric inference" (PAI) and examples of its use are presented.

Cutaneous Papillomaviruses and Non-melanoma Skin Cancer: Causal Agents or Innocent Bystanders?

Science.gov (United States)

Hasche, Daniel; Vinzón, Sabrina E; Rösl, Frank

2018-01-01

There is still controversy in the scientific field about whether certain types of cutaneous human papillomaviruses (HPVs) are causally involved in the development of non-melanoma skin cancer (NMSC). Deciphering the etiological role of cutaneous HPVs requires - besides tissue culture systems - appropriate preclinical models to match the obtained results with clinical data from affected patients. Clear scientific evidence about the etiology and underlying mechanisms involved in NMSC development is fundamental to provide reasonable arguments for public health institutions to classify at least certain cutaneous HPVs as group 1 carcinogens. This in turn would have implications on fundraising institutions and health care decision makers to force - similarly as for anogenital cancer - the implementation of a broad vaccination program against "high-risk" cutaneous HPVs to prevent NMSC as the most frequent cancer worldwide. Precise knowledge of the multi-step progression from normal cells to cancer is a prerequisite to understand the functional and clinical impact of cofactors that affect the individual outcome and the personalized treatment of a disease. This overview summarizes not only recent arguments that favor the acceptance of a viral etiology in NMSC development but also reflects aspects of causality in medicine, the use of empirically meaningful model systems and strategies for prevention.

Cutaneous Papillomaviruses and Non-melanoma Skin Cancer: Causal Agents or Innocent Bystanders?

Directory of Open Access Journals (Sweden)

Daniel Hasche

2018-05-01

Full Text Available There is still controversy in the scientific field about whether certain types of cutaneous human papillomaviruses (HPVs are causally involved in the development of non-melanoma skin cancer (NMSC. Deciphering the etiological role of cutaneous HPVs requires – besides tissue culture systems – appropriate preclinical models to match the obtained results with clinical data from affected patients. Clear scientific evidence about the etiology and underlying mechanisms involved in NMSC development is fundamental to provide reasonable arguments for public health institutions to classify at least certain cutaneous HPVs as group 1 carcinogens. This in turn would have implications on fundraising institutions and health care decision makers to force – similarly as for anogenital cancer – the implementation of a broad vaccination program against “high-risk” cutaneous HPVs to prevent NMSC as the most frequent cancer worldwide. Precise knowledge of the multi-step progression from normal cells to cancer is a prerequisite to understand the functional and clinical impact of cofactors that affect the individual outcome and the personalized treatment of a disease. This overview summarizes not only recent arguments that favor the acceptance of a viral etiology in NMSC development but also reflects aspects of causality in medicine, the use of empirically meaningful model systems and strategies for prevention.

Design Issues and Inference in Experimental L2 Research

Science.gov (United States)

Hudson, Thom; Llosa, Lorena

2015-01-01

Explicit attention to research design issues is essential in experimental second language (L2) research. Too often, however, such careful attention is not paid. This article examines some of the issues surrounding experimental L2 research and its relationships to causal inferences. It discusses the place of research questions and hypotheses,…

Steady-state evoked potentials possibilities for mental-state estimation

Science.gov (United States)

Junker, Andrew M.; Schnurer, John H.; Ingle, David F.; Downey, Craig W.

1988-01-01

The use of the human steady-state evoked potential (SSEP) as a possible measure of mental-state estimation is explored. A method for evoking a visual response to a sum-of-ten sine waves is presented. This approach provides simultaneous multiple frequency measurements of the human EEG to the evoking stimulus in terms of describing functions (gain and phase) and remnant spectra. Ways in which these quantities vary with the addition of performance tasks (manual tracking, grammatical reasoning, and decision making) are presented. Models of the describing function measures can be formulated using systems engineering technology. Relationships between model parameters and performance scores during manual tracking are discussed. Problems of unresponsiveness and lack of repeatability of subject responses are addressed in terms of a need for loop closure of the SSEP. A technique to achieve loop closure using a lock-in amplifier approach is presented. Results of a study designed to test the effectiveness of using feedback to consciously connect humans to their evoked response are presented. Findings indicate that conscious control of EEG is possible. Implications of these results in terms of secondary tasks for mental-state estimation and brain actuated control are addressed.

Promotion of Well-Being in Person-Centered Mental Health Care

OpenAIRE

Cloninger, C. Robert; Zohar, Ada H.; Cloninger, Kevin M.

2010-01-01

An understanding of the mechanisms of personality development provides a systematic way to promote health as an integrated state of physical, mental, social, and spiritual well-being. Individual differences in personality are causal antecedents of the full range of psychopathology. The maturation with integration of personality appears to be an important mechanism by which diverse modalities of treatment promote wellness and reduce illness. First, the authors review the relationship between p...

Computational Psychiatry: towards a mathematically informed understanding of mental illness

Science.gov (United States)

Huys, Quentin J M; Roiser, Jonathan P

2016-01-01

Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency (‘helplessness’), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods. PMID:26157034

Uncovering the cognitive processes underlying mental rotation: an eye-movement study.

Science.gov (United States)

Xue, Jiguo; Li, Chunyong; Quan, Cheng; Lu, Yiming; Yue, Jingwei; Zhang, Chenggang

2017-08-30

Mental rotation is an important paradigm for spatial ability. Mental-rotation tasks are assumed to involve five or three sequential cognitive-processing states, though this has not been demonstrated experimentally. Here, we investigated how processing states alternate during mental-rotation tasks. Inference was carried out using an advanced statistical modelling and data-driven approach - a discriminative hidden Markov model (dHMM) trained using eye-movement data obtained from an experiment consisting of two different strategies: (I) mentally rotate the right-side figure to be aligned with the left-side figure and (II) mentally rotate the left-side figure to be aligned with the right-side figure. Eye movements were found to contain the necessary information for determining the processing strategy, and the dHMM that best fit our data segmented the mental-rotation process into three hidden states, which we termed encoding and searching, comparison, and searching on one-side pair. Additionally, we applied three classification methods, logistic regression, support vector model and dHMM, of which dHMM predicted the strategies with the highest accuracy (76.8%). Our study did confirm that there are differences in processing states between these two of mental-rotation strategies, and were consistent with the previous suggestion that mental rotation is discrete process that is accomplished in a piecemeal fashion.

Causal mechanisms of subjective cognitive dysfunction in schizophrenic and depressed patients

NARCIS (Netherlands)

van den Bosch, RJ; Rombouts, RP

We examined causal mechanisms of subjective cognitive (dis)abilities in schizophrenic and depressed patients, and in patient and normal control groups. This exploratory study included objective cognitive performance (Continuous Performance Task) as well as mood and mental effort ratings. Self-report

BioCause: Annotating and analysing causality in the biomedical domain.

Science.gov (United States)

Mihăilă, Claudiu; Ohta, Tomoko; Pyysalo, Sampo; Ananiadou, Sophia

2013-01-16

Biomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis, pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining. We have defined an annotation scheme for enriching biomedical domain corpora with causality relations. This schema has subsequently been used to annotate 851 causal relations to form BioCause, a collection of 19 open-access full-text biomedical journal articles belonging to the subdomain of infectious diseases. These documents have been pre-annotated with named entity and event information in the context of previous shared tasks. We report an inter-annotator agreement rate of over 60% for triggers and of over 80% for arguments using an exact match constraint. These increase significantly using a relaxed match setting. Moreover, we analyse and describe the causality relations in BioCause from various points of view. This information can then be leveraged for the training of automatic causality detection systems. Augmenting named entity and event annotations with information about causal discourse relations could benefit the development of more sophisticated IE systems. These will further influence the development of multiple tasks, such as enabling textual inference to detect entailments, discovering new facts and providing new

Concepts in context: Processing mental state concepts with internal or external focus involves different neural systems

Science.gov (United States)

Oosterwijk, Suzanne; Mackey, Scott; Wilson-Mendenhall, Christine; Winkielman, Piotr; Paulus, Martin P.

2015-01-01

According to embodied cognition theories concepts are contextually-situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in which people understand a concept. This neuroimaging study tested this prediction using a set of sentences that described emotional (e.g., fear, joy) and non-emotional (e.g., thinking, hunger) mental states with internal focus (i.e. focusing on bodily sensations and introspection) or external focus (i.e. focusing on expression and action). Consistent with our predictions, data suggested that the inferior frontal gyrus, a region associated with action representation, was engaged more by external than internal sentences. By contrast, the ventromedial prefrontal cortex, a region associated with the generation of internal states, was engaged more by internal emotion sentences than external sentence categories. Similar patterns emerged when we examined the relationship between neural activity and independent ratings of sentence focus. Furthermore, ratings of emotion were associated with activation in the medial prefrontal cortex, whereas ratings of activity were associated with activation in the inferior frontal gyrus. These results suggest that mental state concepts are represented in a dynamic way, using context-relevant interoceptive and sensorimotor resources. PMID:25748274

Constraint Satisfaction Inference : Non-probabilistic Global Inference for Sequence Labelling

NARCIS (Netherlands)

Canisius, S.V.M.; van den Bosch, A.; Daelemans, W.; Basili, R.; Moschitti, A.

2006-01-01

We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar

Mental Dimension in Social Causalities: Past and Future

Directory of Open Access Journals (Sweden)

Akop P. Nazaretyan

2017-02-01

Full Text Available Following an old tradition, the political analysts tend to exaggerate the role of economic and resource factors, which entail prognostication errors. This paper shows that in most historical cases, mental contents, conditions and fluctuation in mass moods, ambitions, talents of authoritative leaders and other “subjective’ factors of the kind determine social events more substantially than any outside factors. Economic and political spheres intersect during quiet phases and far diverge as crisis phases approach. In addition, the specific weight of mental phenomena in system dependencies has been growing throughout human history, so that in the 21st century, it is the evolution in worldviews, values and meanings that ultimately determines the viability of our planetary civilization.

The relationships between work characteristics and mental health: Examining normal, reversed and reciprocal relationships in a 4-wave study

NARCIS (Netherlands)

Lange, A.H. de; Taris, T.W.; Kompier, M.A.J.; Houtman, I.L.D.; Bongers, P.M.

2004-01-01

This longitudinal study examined the causal relationships between job demands, job control and supervisor support on the one hand and mental health on the other. Whereas we assumed that work characteristics affect mental health, we also examined reversed causal relationships (mental health

Prenatal nutrition, epigenetics and schizophrenia risk: can we test causal effects?

Science.gov (United States)

Kirkbride, James B; Susser, Ezra; Kundakovic, Marija; Kresovich, Jacob K; Davey Smith, George; Relton, Caroline L

2012-06-01

We posit that maternal prenatal nutrition can influence offspring schizophrenia risk via epigenetic effects. In this article, we consider evidence that prenatal nutrition is linked to epigenetic outcomes in offspring and schizophrenia in offspring, and that schizophrenia is associated with epigenetic changes. We focus upon one-carbon metabolism as a mediator of the pathway between perturbed prenatal nutrition and the subsequent risk of schizophrenia. Although post-mortem human studies demonstrate DNA methylation changes in brains of people with schizophrenia, such studies cannot establish causality. We suggest a testable hypothesis that utilizes a novel two-step Mendelian randomization approach, to test the component parts of the proposed causal pathway leading from prenatal nutritional exposure to schizophrenia. Applied here to a specific example, such an approach is applicable for wider use to strengthen causal inference of the mediating role of epigenetic factors linking exposures to health outcomes in population-based studies.

Amodal causal capture in the tunnel effect.

Science.gov (United States)

Bae, Gi Yeul; Flombaum, Jonathan I

2011-01-01

In addition to identifying individual objects in the world, the visual system must also characterize the relationships between objects, for instance when objects occlude one another or cause one another to move. Here we explored the relationship between perceived causality and occlusion. Can one perceive causality in an occluded location? In several experiments, observers judged whether a centrally presented event involved a single object passing behind an occluder, or one object causally launching another (out of view and behind the occluder). With no additional context, the centrally presented event was typically judged as a non-causal pass, even when the occluding and disoccluding objects were different colors--an illusion known as the 'tunnel effect' that results from spatiotemporal continuity. However, when a synchronized context event involved an unambiguous causal launch, participants perceived a causal launch behind the occluder. This percept of an occluded causal interaction could also be driven by grouping and synchrony cues in the absence of any explicitly causal interaction. These results reinforce the hypothesis that causality is an aspect of perception. It is among the interpretations of the world that are independently available to vision when resolving ambiguity, and that the visual system can 'fill in' amodally.

Implications of causality for quantum biology - I: topology change

Science.gov (United States)

Scofield, D. F.; Collins, T. C.

2018-06-01

A framework for describing the causal, topology changing, evolution of interacting biomolecules is developed. The quantum dynamical manifold equations (QDMEs) derived from this framework can be related to the causality restrictions implied by a finite speed of light and to Planck's constant to set a transition frequency scale. The QDMEs imply conserved stress-energy, angular-momentum and Noether currents. The functional whose extremisation leads to this result provides a causal, time-dependent, non-equilibrium generalisation of the Hohenberg-Kohn theorem. The system of dynamical equations derived from this functional and the currents J derived from the QDMEs are shown to be causal and consistent with the first and second laws of thermodynamics. This has the potential of allowing living systems to be quantum mechanically distinguished from non-living ones.

Interfering with the neural activity of mirror-related frontal areas impairs mentalistic inferences.

Science.gov (United States)

Herbet, Guillaume; Lafargue, Gilles; Moritz-Gasser, Sylvie; Bonnetblanc, François; Duffau, Hugues

2015-07-01

According to recently proposed interactive dual-process theories, mentalizing abilities emerge from the coherent interaction between two physically distinct neural systems: (1) the mirror network, coding for the low-level embodied representations involved in pre-reflective sociocognitive processes and (2) the mentalizing network per se, which codes for higher level representations subtending the reflective attribution of psychological states. However, although the latest studies have shown that the core areas forming these two neurocognitive systems do indeed maintain effective connectivity during mentalizing, it is unclear whether an intact mirror system (and, more specifically, its anterior node, namely the posterior inferior frontal cortex) is a prerequisite for accurate mentalistic inferences. Intraoperative brain mapping via direct electrical stimulation offers a unique opportunity to address this issue. Electrical stimulation of the brain creates a "virtual" lesion, which provides functional information on well-defined parts of the cerebral cortex. In the present study, five patients were mapped in real time while they performed a mentalizing task. We found six responsive sites: four in the lateral part of the right pars opercularis and two in the dorsal part of the right pars triangularis. On the subcortical level, two additional sites were located within the white matter connectivity of the pars opercularis. Taken as a whole, our results suggest that the right inferior frontal cortex and its underlying axonal connectivity have a key role in mentalizing. Specifically, our findings support the hypothesis whereby transient, functional disruption of the mirror network influences higher order mentalistic inferences.

Feasibility of a multiple-choice mini mental state examination for chronically critically ill patients.

Science.gov (United States)

Miguélez, Marta; Merlani, Paolo; Gigon, Fabienne; Verdon, Mélanie; Annoni, Jean-Marie; Ricou, Bara

2014-08-01

Following treatment in an ICU, up to 70% of chronically critically ill patients present neurocognitive impairment that can have negative effects on their quality of life, daily activities, and return to work. The Mini Mental State Examination is a simple, widely used tool for neurocognitive assessment. Although of interest when evaluating ICU patients, the current version is restricted to patients who are able to speak. This study aimed to evaluate the feasibility of a visual, multiple-choice Mini Mental State Examination for ICU patients who are unable to speak. The multiple-choice Mini Mental State Examination and the standard Mini Mental State Examination were compared across three different speaking populations. The interrater and intrarater reliabilities of the multiple-choice Mini Mental State Examination were tested on both intubated and tracheostomized ICU patients. Mixed 36-bed ICU and neuropsychology department in a university hospital. Twenty-six healthy volunteers, 20 neurological patients, 46 ICU patients able to speak, and 30 intubated or tracheostomized ICU patients. None. Multiple-choice Mini Mental State Examination results correlated satisfactorily with standard Mini Mental State Examination results in all three speaking groups: healthy volunteers: intraclass correlation coefficient = 0.43 (95% CI, -0.18 to 0.62); neurology patients: 0.90 (95% CI, 0.82-0.95); and ICU patients able to speak: 0.86 (95% CI, 0.70-0.92). The interrater and intrarater reliabilities were good (0.95 [0.87-0.98] and 0.94 [0.31-0.99], respectively). In all populations, a Bland-Altman analysis showed systematically higher scores using the multiple-choice Mini Mental State Examination. Administration of the multiple-choice Mini Mental State Examination to ICU patients was straightforward and produced exploitable results comparable to those of the standard Mini Mental State Examination. It should be of interest for the assessment and monitoring of the neurocognitive

Causality, spin, and equal-time commutators

International Nuclear Information System (INIS)

Abdel-Rahman, A.M.

1975-01-01

We study the causality constraints on the structure of the Lorentz-antisymmetric component of the commutator of two conserved isovector currents between fermion states of equal momenta. We discuss the sum rules that follow from causality and scaling, using the recently introduced refined infinite-momentum technique. The complete set of sum rules is found to include the spin-dependent fixed-mass sum rules obtained from light-cone commutators. The causality and scaling restrictions on the structure of the electromagnetic equal-time commutators are discussed, and it is found, in particular, that causality requires the spin-dependent part of the matrix element for the time-space electromagnetic equal-time commutator to vanish identically. It is also shown, in comparison with the electromagnetic case, that the corresponding matrix element for the time-space isovector current equal-time commutator is required, by causality, to have isospin-antisymmetric tensor and scalar operator Schwinger terms