Human Papilloma Viruses and Breast Cancer - Assessment of Causality.

Science.gov (United States)

Lawson, James Sutherland; Glenn, Wendy K; Whitaker, Noel James

2016-01-01

High risk human papilloma viruses (HPVs) may have a causal role in some breast cancers. Case-control studies, conducted in many different countries, consistently indicate that HPVs are more frequently present in breast cancers as compared to benign breast and normal breast controls (odds ratio 4.02). The assessment of causality of HPVs in breast cancer is difficult because (i) the HPV viral load is extremely low, (ii) HPV infections are common but HPV associated breast cancers are uncommon, and (iii) HPV infections may precede the development of breast and other cancers by years or even decades. Further, HPV oncogenesis can be indirect. Despite these difficulties, the emergence of new evidence has made the assessment of HPV causality, in breast cancer, a practical proposition. With one exception, the evidence meets all the conventional criteria for a causal role of HPVs in breast cancer. The exception is "specificity." HPVs are ubiquitous, which is the exact opposite of specificity. An additional reservation is that the prevalence of breast cancer is not increased in immunocompromised patients as is the case with respect to HPV-associated cervical cancer. This indicates that HPVs may have an indirect causal influence in breast cancer. Based on the overall evidence, high-risk HPVs may have a causal role in some breast cancers.

Remnant cholesterol as a causal risk factor for ischemic heart disease

DEFF Research Database (Denmark)

Varbo, Anette; Benn, Marianne; Tybjærg-Hansen, Anne

2013-01-01

The aim of this study was to test the hypothesis that elevated nonfasting remnant cholesterol is a causal risk factor for ischemic heart disease independent of reduced high-density lipoprotein (HDL) cholesterol.......The aim of this study was to test the hypothesis that elevated nonfasting remnant cholesterol is a causal risk factor for ischemic heart disease independent of reduced high-density lipoprotein (HDL) cholesterol....

On causality of extreme events

Directory of Open Access Journals (Sweden)

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.

Neural Correlates of Causal Power Judgments

Directory of Open Access Journals (Sweden)

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.

Human Papilloma Viruses and Breast Cancer – Assessment of Causality

Science.gov (United States)

Lawson, James Sutherland; Glenn, Wendy K.; Whitaker, Noel James

2016-01-01

High risk human papilloma viruses (HPVs) may have a causal role in some breast cancers. Case–control studies, conducted in many different countries, consistently indicate that HPVs are more frequently present in breast cancers as compared to benign breast and normal breast controls (odds ratio 4.02). The assessment of causality of HPVs in breast cancer is difficult because (i) the HPV viral load is extremely low, (ii) HPV infections are common but HPV associated breast cancers are uncommon, and (iii) HPV infections may precede the development of breast and other cancers by years or even decades. Further, HPV oncogenesis can be indirect. Despite these difficulties, the emergence of new evidence has made the assessment of HPV causality, in breast cancer, a practical proposition. With one exception, the evidence meets all the conventional criteria for a causal role of HPVs in breast cancer. The exception is “specificity.” HPVs are ubiquitous, which is the exact opposite of specificity. An additional reservation is that the prevalence of breast cancer is not increased in immunocompromised patients as is the case with respect to HPV-associated cervical cancer. This indicates that HPVs may have an indirect causal influence in breast cancer. Based on the overall evidence, high-risk HPVs may have a causal role in some breast cancers. PMID:27747193

The Analysis of the Contribution of Human Factors to the In-Flight Loss of Control Accidents

Science.gov (United States)

Ancel, Ersin; Shih, Ann T.

2012-01-01

In-flight loss of control (LOC) is currently the leading cause of fatal accidents based on various commercial aircraft accident statistics. As the Next Generation Air Transportation System (NextGen) emerges, new contributing factors leading to LOC are anticipated. The NASA Aviation Safety Program (AvSP), along with other aviation agencies and communities are actively developing safety products to mitigate the LOC risk. This paper discusses the approach used to construct a generic integrated LOC accident framework (LOCAF) model based on a detailed review of LOC accidents over the past two decades. The LOCAF model is comprised of causal factors from the domain of human factors, aircraft system component failures, and atmospheric environment. The multiple interdependent causal factors are expressed in an Object-Oriented Bayesian belief network. In addition to predicting the likelihood of LOC accident occurrence, the system-level integrated LOCAF model is able to evaluate the impact of new safety technology products developed in AvSP. This provides valuable information to decision makers in strategizing NASA's aviation safety technology portfolio. The focus of this paper is on the analysis of human causal factors in the model, including the contributions from flight crew and maintenance workers. The Human Factors Analysis and Classification System (HFACS) taxonomy was used to develop human related causal factors. The preliminary results from the baseline LOCAF model are also presented.

Human factors analysis of U.S. Navy afloat mishaps

OpenAIRE

Lacy, Rex D.

1998-01-01

The effects of maritime mishaps, which include loss of life as well as environmental and economic considerations, are significant. It has been estimated that over 80percent of maritime accidents areat least partially attributable to human error. Human error has been extensively studied in a number of fields, particularly aviation. The present research involves application of the Human Factors Accident Classification System (HFACS), developed by the Naval Safety Center, to human error causal f...

Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors.

Science.gov (United States)

Burgess, Stephen; Scott, Robert A; Timpson, Nicholas J; Davey Smith, George; Thompson, Simon G

2015-07-01

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval -0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect.

A causal examination of the effects of confounding factors on multimetric indices

Science.gov (United States)

Schoolmaster, Donald R.; Grace, James B.; Schweiger, E. William; Mitchell, Brian R.; Guntenspergen, Glenn R.

2013-01-01

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.

Space and time in perceptual causality

Directory of Open Access Journals (Sweden)

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.

The causality analysis of climate change and large-scale human crisis.

Science.gov (United States)

Zhang, David D; Lee, Harry F; Wang, Cong; Li, Baosheng; Pei, Qing; Zhang, Jane; An, Yulun

2011-10-18

Recent studies have shown strong temporal correlations between past climate changes and societal crises. However, the specific causal mechanisms underlying this relation have not been addressed. We explored quantitative responses of 14 fine-grained agro-ecological, socioeconomic, and demographic variables to climate fluctuations from A.D. 1500-1800 in Europe. Results show that cooling from A.D. 1560-1660 caused successive agro-ecological, socioeconomic, and demographic catastrophes, leading to the General Crisis of the Seventeenth Century. We identified a set of causal linkages between climate change and human crisis. Using temperature data and climate-driven economic variables, we simulated the alternation of defined "golden" and "dark" ages in Europe and the Northern Hemisphere during the past millennium. Our findings indicate that climate change was the ultimate cause, and climate-driven economic downturn was the direct cause, of large-scale human crises in preindustrial Europe and the Northern Hemisphere.

Human error and the problem of causality in analysis of accidents

DEFF Research Database (Denmark)

Rasmussen, Jens

1990-01-01

, designers or managers have played a major role. There are, however, several basic problems in analysis of accidents and identification of human error. This paper addresses the nature of causal explanations and the ambiguity of the rules applied for identification of the events to include in analysis......Present technology is characterized by complexity, rapid change and growing size of technical systems. This has caused increasing concern with the human involvement in system safety. Analyses of the major accidents during recent decades have concluded that human errors on part of operators...

Causal factors of corporate crime in Taiwan: qualitative and quantitative findings.

Science.gov (United States)

Mon, Wei-Teh

2002-04-01

Street crimes are a primary concern of most criminologists in Taiwan. In recent years, however, crimes committed by corporations have increased greatly in this country. Employing the empirical approach to collect data about causal factors of corporate crime, the research presented in this article is the first systematic empirical study concerning corporate crime in Taiwan. The research sample was selected from a corporation with a criminal record of pollution caused by the release of toxic chemicals into the environment and a corporation with no criminal record. Questionnaire survey and interviews of corporate employees and managers were conducted, and secondary data were collected from official agencies. This research indicated the causal factors of corporate crime as follows: the failure of government regulation, lack of corporate self-regulation, lack of public concern about corporate crime, corporate mechanistic structure, and the low self-control tendency of corporate managers.

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

Significance of functional disease-causal/susceptible variants identified by whole-genome analyses for the understanding of human diseases.

Science.gov (United States)

Hitomi, Yuki; Tokunaga, Katsushi

2017-01-01

Human genome variation may cause differences in traits and disease risks. Disease-causal/susceptible genes and variants for both common and rare diseases can be detected by comprehensive whole-genome analyses, such as whole-genome sequencing (WGS), using next-generation sequencing (NGS) technology and genome-wide association studies (GWAS). Here, in addition to the application of an NGS as a whole-genome analysis method, we summarize approaches for the identification of functional disease-causal/susceptible variants from abundant genetic variants in the human genome and methods for evaluating their functional effects in human diseases, using an NGS and in silico and in vitro functional analyses. We also discuss the clinical applications of the functional disease causal/susceptible variants to personalized medicine.

Simultaneous estimation of the in-mean and in-variance causal connectomes of the human brain.

Science.gov (United States)

Duggento, A; Passamonti, L; Guerrisi, M; Toschi, N

2017-07-01

In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

The influence of linguistic and cognitive factors on the time course of verb-based implicit causality.

Science.gov (United States)

Koornneef, Arnout; Dotlačil, Jakub; van den Broek, Paul; Sanders, Ted

2016-01-01

In three eye-tracking experiments the influence of the Dutch causal connective "want" (because) and the working memory capacity of readers on the usage of verb-based implicit causality was examined. Experiments 1 and 2 showed that although a causal connective is not required to activate implicit causality information during reading, effects of implicit causality surfaced more rapidly and were more pronounced when a connective was present in the discourse than when it was absent. In addition, Experiment 3 revealed that-in contrast to previous claims-the activation of implicit causality is not a resource-consuming mental operation. Moreover, readers with higher and lower working memory capacities behaved differently in a dual-task situation. Higher span readers were more likely to use implicit causality when they had all their working memory resources at their disposal. Lower span readers showed the opposite pattern as they were more likely to use the implicit causality cue in the case of an additional working memory load. The results emphasize that both linguistic and cognitive factors mediate the impact of implicit causality on text comprehension. The implications of these results are discussed in terms of the ongoing controversies in the literature-that is, the focusing-integration debate and the debates on the source of implicit causality.

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

Agency, time and causality

Directory of Open Access Journals (Sweden)

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.

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

KAUST Repository

Triantafillou, Sofia

2017-03-31

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.

Capturing cognitive causal paths in human reliability analysis with Bayesian network models

International Nuclear Information System (INIS)

Zwirglmaier, Kilian; Straub, Daniel; Groth, Katrina M.

2017-01-01

reIn the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over traditional HRA methods. In this paper we illustrate how BNs can be used to include additional, qualitative causal paths to provide traceability. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. First, the developed extended BN structure reflects the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, the use of node reduction algorithms allows the BN to be condensed to a level of detail at which quantification is as straightforward as the techniques used in existing HRA. We illustrate the framework by developing a BN version of the critical data misperceived crew failure mode in the IDHEAS HRA method, which is currently under development at the US NRC . We illustrate how the model could be quantified with a combination of expert-probabilities and information from operator performance databases such as SACADA. This paper lays the foundations necessary to expand the cognitive and quantitative foundations of HRA. - Highlights: • A framework for building traceable BNs for HRA, based on cognitive causal paths. • A qualitative BN structure, directly showing these causal paths is developed. • Node reduction algorithms are used for making the BN structure quantifiable. • BN quantified through expert estimates and observed data (Bayesian updating). • The framework is illustrated for a crew failure mode of IDHEAS.

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

Science.gov (United States)

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.

Dynamic factor analysis in the frequency domain: causal modeling of multivariate psychophysiological time series

NARCIS (Netherlands)

Molenaar, P.C.M.

1987-01-01

Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic

Interactive analysis of human error factors in NPP operation events

International Nuclear Information System (INIS)

Zhang Li; Zou Yanhua; Huang Weigang

2010-01-01

Interactive of human error factors in NPP operation events were introduced, and 645 WANO operation event reports from 1999 to 2008 were analyzed, among which 432 were found relative to human errors. After classifying these errors with the Root Causes or Causal Factors, and then applying SPSS for correlation analysis,we concluded: (1) Personnel work practices are restricted by many factors. Forming a good personnel work practices is a systematic work which need supports in many aspects. (2)Verbal communications,personnel work practices, man-machine interface and written procedures and documents play great roles. They are four interaction factors which often come in bundle. If some improvements need to be made on one of them,synchronous measures are also necessary for the others.(3) Management direction and decision process, which are related to management,have a significant interaction with personnel factors. (authors)

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.

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

Causality discovery technology

Science.gov (United States)

Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.

2012-11-01

Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.

Energy, human capital and economic growth in Asia Pacific countries — Evidence from a panel cointegration and causality analysis

International Nuclear Information System (INIS)

Fang, Zheng; Chang, Youngho

2016-01-01

This paper examines the cointegration and causal relationship between energy consumption and economic development in 16 Asia Pacific countries over the period 1970–2011 using the augmented production function which considers not only physical capital and labor but also human capital. This is likely among the first of the energy–growth nexus literature to include human capital in the multivariate framework. Using recently developed panel unit root test and cointegration test that allow for cross-sectional dependence, this paper finds a long-run cointegrating relationship between these variables. Continuously-updated fully modified (Cup-FM) estimates are subsequently compared with panel heterogeneous fully modified ordinary least squares (FMOLS) results to confirm the importance of accounting for interdependence across countries. The bootstrap panel Granger causality test results find economic growth Granger cause energy use in the region but the relationship varies for individual countries. - Highlights: • We study the causal link between energy and growth in 16 AP countries for 1970–2011. • Human capital is for the first time incorporated into the multivariate framework. • Recent panel methods allowing for cross sectional dependence is used. • Bootstrap panel Granger causality test results find GDP Granger causing energy use in the region. • The energy–growth relationship varies for individual countries.

Exploring causal associations between alcohol and coronary heart disease risk factors

DEFF Research Database (Denmark)

Lawlor, Debbie A; Nordestgaard, Børge G; Benn, Marianne

2013-01-01

association with triglycerides [-14.9% (-25.6, -4.3)] in IV analyses; P = 0.006 and 0.01, respectively, for difference between the two. Alcohol was not associated with non-HDLc or glucose.ConclusionOur results show adverse effects of long-term alcohol consumption on BP and BMI. We also found novel evidence......AimsTo explore the causal effect of long-term alcohol consumption on coronary heart disease risk factors.Methods and resultsWe used variants in ADH1B and ADH1C genes as instrumental variables (IV) to estimate the causal effect of long-term alcohol consumption on body mass index (BMI), blood...... pressure (BP), lipids, fibrinogen, and glucose. Analyses were undertaken in 54 604 Danes (mean age 56 years). Both confounder-adjusted multivariable and IV analyses suggested that a greater alcohol consumption among those who drank any alcohol resulted in a higher BP [mean difference in SBP per doubling...

Illness causal beliefs in Turkish immigrants

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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

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...

Entropy for theories with indefinite causal structure

International Nuclear Information System (INIS)

Markes, Sonia; Hardy, Lucien

2011-01-01

Any theory with definite causal structure has a defined past and future, be it defined by light cones or an absolute time scale. Entropy is a concept that has traditionally been reliant on a definite notion of causality. However, without a definite notion of causality, the concept of entropy is not all lost. Indefinite causal structure results from combining probabilistic predictions and dynamical space-time. The causaloid framework lays the mathematical groundwork to be able to treat indefinite causal structure. In this paper, we build on the causaloid mathematics and define a causally-unbiased entropy for an indefinite causal structure. In defining a causally-unbiased entropy, there comes about an emergent idea of causality in the form of a measure of causal connectedness, termed the Q factor.

The multidimensional causal factors of 'wet litter' in chicken-meat production.

Science.gov (United States)

Dunlop, Mark W; Moss, Amy F; Groves, Peter J; Wilkinson, Stuart J; Stuetz, Richard M; Selle, Peter H

2016-08-15

The problem of 'wet litter', which occurs primarily in grow-out sheds for meat chickens (broilers), has been recognised for nearly a century. Nevertheless, it is an increasingly important problem in contemporary chicken-meat production as wet litter and associated conditions, especially footpad dermatitis, have developed into tangible welfare issues. This is only compounded by the market demand for chicken paws and compromised bird performance. This review considers the multidimensional causal factors of wet litter. While many causal factors can be listed it is evident that the critical ones could be described as micro-environmental factors and chief amongst them is proper management of drinking systems and adequate shed ventilation. Thus, this review focuses on these environmental factors and pays less attention to issues stemming from health and nutrition. Clearly, there are times when related avian health issues of coccidiosis and necrotic enteritis cannot be overlooked and the development of efficacious vaccines for the latter disease would be advantageous. Presently, the inclusion of phytate-degrading enzymes in meat chicken diets is routine and, therefore, the implication that exogenous phytases may contribute to wet litter is given consideration. Opinion is somewhat divided as how best to counter the problem of wet litter as some see education and extension as being more beneficial than furthering research efforts. However, it may prove instructive to assess the practice of whole grain feeding in relation to litter quality and the incidence of footpad dermatitis. Additional research could investigate the relationships between dietary concentrations of key minerals and the application of exogenous enzymes with litter quality. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

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

Science.gov (United States)

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.

The productivity from a human perspective: Dimensions and factors

Directory of Open Access Journals (Sweden)

Mirza Marvel Cequea

2011-11-01

Full Text Available Purpose: The purpose of this paper is to review the literature, for both theoretical foundations and empirical research, in order to establish relationships between the variables related to human factors and their impact on productivity.Design/methodology/approach: The strategy employed corresponds to a descriptive non-experimental design, which is the establishment of three criteria for the literature review, in order to narrow down the topic to research works relating productivity with the human factor. This was investigated in databases and journals dealing with related topics, in addition to consulting doctoral theses and published books concerning the influence of human factors on productivity. About 250 papers which were considered the most relevant for the research were selected.Findings:  As a result of this exploration the classification of the factors in two dimensions that are manifested in people when they act in organizations was highlighted: the psychological and the psychosocial dimension. Human factors included in these dimensions are: individual factors (motivation, skills, job satisfaction, identification, commitment and involvement with the organization, group factors (participation, cohesion and management conflict and organizational factors (organizational culture, organizational climate and leadership. All these factors have an impact on the productivity of the organization and are addressed in this research.Originality/value: The selected variables were used to formulate a model that incorporates the human factors identified and considers the phenomenon in a comprehensive manner. It will be addressed through multivariate analysis, with the possible application of structural equations in order to assess the causal relationships that may exist between factors and productivity.

Causal Factors of Corruption in Construction Project Management: An Overview.

Science.gov (United States)

Owusu, Emmanuel Kingsford; Chan, Albert P C; Shan, Ming

2017-11-11

The development of efficient and strategic anti-corruption measures can be better achieved if a deeper understanding and identification of the causes of corruption are established. Over the past years, many studies have been devoted to the research of corruption in construction management (CM). This has resulted in a significant increase in the body of knowledge on the subject matter, including the causative factors triggering these corrupt practices. However, an apropos systematic assessment of both past and current studies on the subject matter which is needful for the future endeavor is lacking. Moreover, there is an absence of unified view of the causative factors of corruption identified in construction project management (CPM). This paper, therefore, presents a comprehensive review of the causes of corruption from selected articles in recognized construction management journals to address the mentioned gaps. A total number of 44 causes of corruption were identified from 37 publications and analyzed in terms of existing causal factors of corruption, annual trend of publications and the thematic categorization of the identified variables. The most identifiable causes were over close relationships, poor professional ethical standards, negative industrial and working conditions, negative role models and inadequate sanctions. A conceptual framework of causes of corruption was established, after categorizing the 44 variables into five unique categories. In descending order, the five constructs are Psychosocial-Specific Causes, Organizational-Specific Causes, Regulatory-Specific Causes, Project-Specific Causes and Statutory-Specific Causes. This study extends the current literature of corruption research in construction management and contributes to a deepened understanding of the causal instigators of corruption identified in CPM. The findings from this study provide valuable information and extended knowledge to industry practitioners and policymakers as well as

Pengalokasian Tenaga Kerja dengan Human Factor Engineering di PT. Pelindo I

Directory of Open Access Journals (Sweden)

Yusnawati Yusnawati

2017-05-01

Full Text Available Indonesia Port Corporation I (PT Pelabuhan Indonesia I (Persero or PT. Pelindo I is one of the Indonesian state-owned enterprises which manages port services in western Indonesia. Shipyard unit (Unit Galangan Kapal (UGK is a branch of PT. Pelindo I. At present, a problem arises if more than 2 ships are being repaired at once in the unit, UGK scheduling overlaps the repairing activities. In order to solve the problem, study of human factor is important. Human factor is the study of the limitations, capabilities, and human behavior, as well as its interaction with the product, environment, equipment and the establishment of tasks and activities. One part of the human factor is the human factor in system design. In order to improve the effectiveness of the system, the human factor must be involved in each phase of the design process in the system design. This includes a number of activities to obtain input specification work, therefore the working methods and the optimal amount of labor can be determined. Human factors engineering is the application of science that utilizes research on the human factor and use the basic knowledge to design, to repair and to install the system. This research method is causal, searching for the causes which led to delays in the completion of ship repairing. Through human factor engineering approach to the allocation of labor increased by 12.23 per cent of the actual conditions, so that the delay of ship repair were not found during normal conditions.

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

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

Science.gov (United States)

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.

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)

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

Science.gov (United States)

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.

Correlation Relationship of Performance Shaping Factors (PSFs) for Human Reliability Analysis

Energy Technology Data Exchange (ETDEWEB)

Bheka, M. Khumalo; Kim, Jonghyun [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

2014-10-15

between PSFs using correlation analysis and identify patterns in the PSFs using Principal Factor Analysis (PFA). The study is specifically based on Operational Performance Information Systems (OPIS) database. This study was conducted to determine causal relationships between PSFs and also find sets of PSFs (error forcing context) which contribute more to human error probabilities. These goals were achieved using correlation and principal factor analysis.

Causal inference in public health.

Science.gov (United States)

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.

Consciousness and the "Causal Paradox"

OpenAIRE

Velmans, Max

1996-01-01

Viewed from a first-person perspective consciousness appears to be necessary for complex, novel human activity - but viewed from a third-person perspective consciousness appears to play no role in the activity of brains, producing a "causal paradox". To resolve this paradox one needs to distinguish consciousness of processing from consciousness accompanying processing or causing processing. Accounts of consciousness/brain causal interactions switch between first- and third-person perspectives...

The Causal Factors Associated with the Loving

Directory of Open Access Journals (Sweden)

Seyed Taghi Heydari

2015-10-01

Full Text Available Background: Families with disabled children need more psycho-social considerations. Motherhood care of the children with multiple disabilities is difficult. Due to its importance, the aim of this study was to investigate the causal factors affecting loving care of mothers of children with multiple disabilities. Methods: The study used a cross-sectional method in which 75 mothers of exceptional children with multiple disabilities (physical and mental in elementary schools in Shiraz, Iran. The data were collected through questionnaires which, besides demographical factors, evaluated the relationship between mothers’ loving care of children with multiple disabilities and four other variables including purpose in life, life satisfaction, religious attitude, and sense of coherence. Mann-Whitney U was used for comparison between mothers’ loving care and other variables. Results: Results revealed that demographic variables did not have a significant relationship with loving care. In the case of social variables, there was a significant relationship between mothers’ loving care and purpose in life (P<0.001, religious attitude (P<0.001, and life satisfaction (P=0.01. Conclusion: Motherhood care of disabled children is a unique phenomenon which is due to attachment of mother-child situation. Nevertheless, these mothers are vulnerable and marginalized people who need more attention and social supports provided by related governmental institutions and also NGOs actors.

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

NARCIS (Netherlands)

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

Cortical hierarchies perform Bayesian causal inference in multisensory perception.

Directory of Open Access Journals (Sweden)

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.

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.

Human factors in aviation: Terminal control area boundary conflicts

Science.gov (United States)

Monan, William P.

1989-01-01

Air-to-air conflicts in the vicinity of Terminal Control Area (TCA) boundaries were studied to obtain a better understanding of the causal dynamics of these events with particular focus on human factor issues. The study dataset consisted of 381 Instrument Flight Rules/Visual Flight Rules (IFR/VFR) traffic conflicts in airspace layers above TCA ceiling and below TCA floors; 213 reports of incursions in TCA terminal airspace by VFR aircraft, of which 123 resulted in conflicts; and an additional set of reports describing problems with Air Traffic Control (ATC) services in and around TCAs. Results and conclusions are detailed.

Assessment of the human factor in the quantification of technical system reliability taking into consideration cognitive-causal aspects. Partial project 2. Modeling of the human behavior for reliability considerations. Final report

International Nuclear Information System (INIS)

Jennerich, Marco; Imbsweiler, Jonas; Straeter, Oliver; Arenius, Marcus

2015-03-01

This report presents the findings of the project for the consideration of human factor in the quantification of the reliability of technical systems, taking into account cognitive-causal aspects concerning the modeling of human behavior of reliability issues (funded by the Federal Ministry of Economics and Technology; grant number 15014328). This project is part of a joint project with the University of Applied Sciences Zittau / Goerlitz for assessing the human factor in the quantification of the reliability of technical systems. The concern of the University of Applied Sciences Zittau / Goerlitz is the mathematical modeling of human reliability by means of a fuzzy set approach (grant number 1501432A). The part of the project presented here provides the necessary data basis for the evaluation of the mathematical modeling using fuzzy set approach. At the appropriate places in this report, the interfaces and data bases between the two projects are outlined accordingly. HRA-methods (Human Reliability Analysis) are an essential component to analyze the reliability of socio-technical systems. Various methods have been established and are used in different areas of application. The established HRA methods have been checked on their congruence. In particular the underlying models and their parameters such as performance-influencing factors and situational influences have been investigated. The elaborated parameters were combined into a hierarchical class structure. Cross-domain incidents were studied. The specific performance-influencing factors have been worked out and have been integrated into a cross-domain database. The dominant (critical) situational factors and their interactions within the event data were identified using the CAHR method (connectionism Assessment of Human Reliability). Task dependent cognitive load profiles have been defined. Within these profiles qualitative and quantitative data of the possibility of emergence of errors have been acquired. This

Expert explanations of honeybee losses in areas of extensive agriculture in France: Gaucho compared with other supposed causal factors

Energy Technology Data Exchange (ETDEWEB)

Maxim, L [Institut des Sciences de la Communication, CNRS UPS 3088, 27 Rue Damesme, 75013 Paris (France); Van der Sluijs, J P, E-mail: laura.maxim@iscc.cnrs.f, E-mail: J.P.vanderSluijs@uu.n [Copernicus Institute for Sustainable Development and Innovation, Department of Science, Technology and Society, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht (Netherlands)

2010-01-15

Debates on causality are at the core of controversies as regards environmental changes. The present paper presents a new method for analyzing controversies on causality in a context of social debate and the results of its empirical testing. The case study used is the controversy as regards the role played by the insecticide Gaucho, compared with other supposed causal factors, in the substantial honeybee (Apis mellifera L.) losses reported to have occurred in France between 1994 and 2004. The method makes use of expert elicitation of the perceived strength of evidence regarding each of Bradford Hill's causality criteria, as regards the link between each of eight possible causal factors identified in attempts to explain each of five signs observed in honeybee colonies. These judgments are elicited from stakeholders and experts involved in the debate, i.e., representatives of Bayer Cropscience, of the Ministry of Agriculture, of the French Food Safety Authority, of beekeepers and of public scientists. We show that the intense controversy observed in confused and passionate public discourses is much less salient when the various arguments are structured using causation criteria. The contradictions between the different expert views have a triple origin: (1) the lack of shared definition and quantification of the signs observed in colonies; (2) the lack of specialist knowledge on honeybees; and (3) the strategic discursive practices associated with the lack of trust between experts representing stakeholders having diverging stakes in the case.

Expert explanations of honeybee losses in areas of extensive agriculture in France: Gaucho compared with other supposed causal factors

International Nuclear Information System (INIS)

Maxim, L; Van der Sluijs, J P

2010-01-01

Debates on causality are at the core of controversies as regards environmental changes. The present paper presents a new method for analyzing controversies on causality in a context of social debate and the results of its empirical testing. The case study used is the controversy as regards the role played by the insecticide Gaucho, compared with other supposed causal factors, in the substantial honeybee (Apis mellifera L.) losses reported to have occurred in France between 1994 and 2004. The method makes use of expert elicitation of the perceived strength of evidence regarding each of Bradford Hill's causality criteria, as regards the link between each of eight possible causal factors identified in attempts to explain each of five signs observed in honeybee colonies. These judgments are elicited from stakeholders and experts involved in the debate, i.e., representatives of Bayer Cropscience, of the Ministry of Agriculture, of the French Food Safety Authority, of beekeepers and of public scientists. We show that the intense controversy observed in confused and passionate public discourses is much less salient when the various arguments are structured using causation criteria. The contradictions between the different expert views have a triple origin: (1) the lack of shared definition and quantification of the signs observed in colonies; (2) the lack of specialist knowledge on honeybees; and (3) the strategic discursive practices associated with the lack of trust between experts representing stakeholders having diverging stakes in the case.

Human factors in the development of complications of airway management: preliminary evaluation of an interview tool.

Science.gov (United States)

Flin, R; Fioratou, E; Frerk, C; Trotter, C; Cook, T M

2013-08-01

The 4th National Audit Project of the Royal College of Anaesthetists and the Difficult Airway Society (NAP4) analysed reports of serious events arising from airway management during anaesthesia, intensive care and the emergency department. We conducted supplementary telephone interviews with 12 anaesthetists who had reported to NAP4, aiming to identify causal factors using a method based on the Human Factors Investigation Tool (HFIT). We identified contributing human factors in all cases (median [range] 4.5 [1-10] per case). The most frequent related to: situation awareness (failures to anticipate, wrong decision) (nine cases); job factors (e.g. task difficulty; staffing, time pressure) (eight cases); and person factors (e.g. tiredness, hunger, stress) (six cases). Protective factors, such as teamwork and communication, were also revealed. The post-report HFIT interview method identified relevant human factors and this approach merits further testing as part of the investigation of anaesthetic incidents. Anaesthesia © 2013 The Association of Anaesthetists of Great Britain and Ireland.

The Functions of Danish Causal Conjunctions

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Rita Therkelsen

2004-01-01

Full Text Available In the article I propose an analysis of the Danish causal conjunctions fordi, siden and for based on the framework of Danish Functional Grammar. As conjunctions they relate two clauses, and their semantics have in common that it indicates a causal relationship between the clauses. The causal conjunctions are different as far as their distribution is concerned; siden conjoins a subordinate clause and a main clause, for conjoins two main clauses, and fordi is able to do both. Methodologically I have based my analysis on these distributional properties comparing siden and fordi conjoining a subordinate and a main clause, and comparing for and fordi conjoining two main clauses, following the thesis that they would establish a causal relationship between different kinds of content. My main findings are that fordi establishes a causal relationship between the events referred to by the two clauses, and the whole utterance functions as a statement of this causal relationship. Siden presupposes such a general causal relationship between the two events and puts forward the causing event as a reason for assuming or wishing or ordering the caused event, siden thus establishes a causal relationship between an event and a speech act. For equally presupposes a general causal relationship between two events and it establishes a causal relationship between speech acts, and fordi conjoining two main clauses is able to do this too, but in this position it also maintains its event-relating ability, the interpretation depending on contextual factors.

Factores causales de la explotación sexual infantil en niños, niñas y adolescentes en Colombia (causal factors of child sexual exploitation in boys, girls, and adolescents in Colombia

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Nora H. Londoño

2015-01-01

Full Text Available Resumen: El propósito de la presente investigación fue identificar factores causales de la explotación sexual comercial en la infancia y la adolescencia en Colombia. Para ello se analizaron tres casos de estudio: Medellín, Sincelejo y Magangué. Se realizaron talleres y se aplicaron encuestas en cada una de las ciudades en Instituciones gubernamentales encargadas de intervenir el fenómeno y en algunas instituciones educativas, con la participación de funcionarios, profesionales, docentes, directivos y estudiantes. Para el análisis de datos se realizó una matriz explicativa con la participación de 4 jurados. Las categorías de análisis fueron factores medioambientales, familiares e individuales. Abstract: The purpose of this piece of research aimed to identify causal factors of trade sexual exploitation on children and adolescents in Colombia. For this purpose, three case studies were analyzed: Medellín, Sincelejo, and Magangué. Workshops were conducted and surveys were implemented in each of the cities in both governmental institutions, in charge of intervening the phenomenon and in some educational institutions, with the participation of officials, professionals, teachers, directors, and students. As for the data analysis, an explanatory matrix was carried out, with the participation of 4 judges. The categories of analysis were those related to the environment, the family, and the individual.

Causal imprinting in causal structure learning.

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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.

BOLD Granger causality reflects vascular anatomy.

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J Taylor Webb

Full Text Available A number of studies have tried to exploit subtle phase differences in BOLD time series to resolve the order of sequential activation of brain regions, or more generally the ability of signal in one region to predict subsequent signal in another region. More recently, such lag-based measures have been applied to investigate directed functional connectivity, although this application has been controversial. We attempted to use large publicly available datasets (FCON 1000, ADHD 200, Human Connectome Project to determine whether consistent spatial patterns of Granger Causality are observed in typical fMRI data. For BOLD datasets from 1,240 typically developing subjects ages 7-40, we measured Granger causality between time series for every pair of 7,266 spherical ROIs covering the gray matter and 264 seed ROIs at hubs of the brain's functional network architecture. Granger causality estimates were strongly reproducible for connections in a test and replication sample (n=620 subjects for each group, as well as in data from a single subject scanned repeatedly, both during resting and passive video viewing. The same effect was even stronger in high temporal resolution fMRI data from the Human Connectome Project, and was observed independently in data collected during performance of 7 task paradigms. The spatial distribution of Granger causality reflected vascular anatomy with a progression from Granger causality sources, in Circle of Willis arterial inflow distributions, to sinks, near large venous vascular structures such as dural venous sinuses and at the periphery of the brain. Attempts to resolve BOLD phase differences with Granger causality should consider the possibility of reproducible vascular confounds, a problem that is independent of the known regional variability of the hemodynamic response.

Causal knowledge promotes behavioral self-regulation: An example using climate change dynamics.

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David K Sewell

Full Text Available Adopting successful climate change mitigation policies requires the public to choose how to balance the sometimes competing goals of managing CO2 emissions and achieving economic growth. It follows that collective action on climate change depends on members of the public to be knowledgeable of the causes and economic ramifications of climate change. The existing literature, however, shows that people often struggle to correctly reason about the fundamental accumulation dynamics that drive climate change. Previous research has focused on using analogy to improve people's reasoning about accumulation, which has been met with some success. However, these existing studies have neglected the role economic factors might play in shaping people's decisions in relation to climate change. Here, we introduce a novel iterated decision task in which people attempt to achieve a specific economic goal by interacting with a causal dynamic system in which human economic activities, CO2 emissions, and warming are all causally interrelated. We show that when the causal links between these factors are highlighted, people's ability to achieve the economic goal of the task is enhanced in a way that approaches optimal responding, and avoids dangerous levels of warming.

Dynamics of large-scale cortical interactions at high gamma frequencies during word production: event related causality (ERC) analysis of human electrocorticography (ECoG).

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Korzeniewska, Anna; Franaszczuk, Piotr J; Crainiceanu, Ciprian M; Kuś, Rafał; Crone, Nathan E

2011-06-15

Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional-anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (>60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions. To determine whether such causal interactions occur at high gamma frequencies and to investigate their functional significance, we used event-related causality (ERC) analysis to estimate the dynamics, directionality, and magnitude of event-related causal interactions using subdural electrocorticography (ECoG) recorded during two word production tasks: picture naming and auditory word repetition. A clinical subject who had normal hearing but was skilled in American Signed Language (ASL) provided a unique opportunity to test our hypothesis with reference to a predictable pattern of causal interactions, i.e. that language cortex interacts with different areas of sensorimotor cortex during spoken vs. signed responses. Our ERC analyses confirmed this prediction. During word production with spoken responses, perisylvian language sites had prominent causal interactions with mouth/tongue areas of motor cortex, and when responses were gestured in sign language, the most prominent interactions involved hand and arm areas of motor cortex. Furthermore, we found that the sites from which the most numerous and prominent causal interactions originated, i.e. sites with a pattern of ERC "divergence", were also sites where high gamma power increases were most prominent and where electrocortical stimulation mapping

Optimal causal inference: estimating stored information and approximating causal architecture.

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Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.

Causality

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Pearl, Judea

2000-03-01

Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

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

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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

Causal events enter awareness faster than non-causal events

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Pieter Moors

2017-01-01

Full Text Available Philosophers have long argued that causality cannot be directly observed but requires a conscious inference (Hume, 1967. Albert Michotte however developed numerous visual phenomena in which people seemed to perceive causality akin to primary visual properties like colour or motion (Michotte, 1946. Michotte claimed that the perception of causality did not require a conscious, deliberate inference but, working over 70 years ago, he did not have access to the experimental methods to test this claim. Here we employ Continuous Flash Suppression (CFS—an interocular suppression technique to render stimuli invisible (Tsuchiya & Koch, 2005—to test whether causal events enter awareness faster than non-causal events. We presented observers with ‘causal’ and ‘non-causal’ events, and found consistent evidence that participants become aware of causal events more rapidly than non-causal events. Our results suggest that, whilst causality must be inferred from sensory evidence, this inference might be computed at low levels of perceptual processing, and does not depend on a deliberative conscious evaluation of the stimulus. This work therefore supports Michotte’s contention that, like colour or motion, causality is an immediate property of our perception of the world.

Causal gene identification using combinatorial V-structure search.

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Cai, Ruichu; Zhang, Zhenjie; Hao, Zhifeng

2013-07-01

With the advances of biomedical techniques in the last decade, the costs of human genomic sequencing and genomic activity monitoring are coming down rapidly. To support the huge genome-based business in the near future, researchers are eager to find killer applications based on human genome information. Causal gene identification is one of the most promising applications, which may help the potential patients to estimate the risk of certain genetic diseases and locate the target gene for further genetic therapy. Unfortunately, existing pattern recognition techniques, such as Bayesian networks, cannot be directly applied to find the accurate causal relationship between genes and diseases. This is mainly due to the insufficient number of samples and the extremely high dimensionality of the gene space. In this paper, we present the first practical solution to causal gene identification, utilizing a new combinatorial formulation over V-Structures commonly used in conventional Bayesian networks, by exploring the combinations of significant V-Structures. We prove the NP-hardness of the combinatorial search problem under a general settings on the significance measure on the V-Structures, and present a greedy algorithm to find sub-optimal results. Extensive experiments show that our proposal is both scalable and effective, particularly with interesting findings on the causal genes over real human genome data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Theories of Causality

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Jones, Robert

2010-03-01

There are a wide range of views on causality. To some (e.g. Karl Popper) causality is superfluous. Bertrand Russell said ``In advanced science the word cause never occurs. Causality is a relic of a bygone age.'' At the other extreme Rafael Sorkin and L. Bombelli suggest that space and time do not exist but are only an approximation to a reality that is simply a discrete ordered set, a ``causal set.'' For them causality IS reality. Others, like Judea Pearl and Nancy Cartwright are seaking to build a complex fundamental theory of causality (Causality, Cambridge Univ. Press, 2000) Or perhaps a theory of causality is simply the theory of functions. This is more or less my take on causality.

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.

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

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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.

Development and evaluation of a computer-aided system for analyzing human error in railway operations

International Nuclear Information System (INIS)

Kim, Dong San; Baek, Dong Hyun; Yoon, Wan Chul

2010-01-01

As human error has been recognized as one of the major contributors to accidents in safety-critical systems, there has been a strong need for techniques that can analyze human error effectively. Although many techniques have been developed so far, much room for improvement remains. As human error analysis is a cognitively demanding and time-consuming task, it is particularly necessary to develop a computerized system supporting this task. This paper presents a computer-aided system for analyzing human error in railway operations, called Computer-Aided System for Human Error Analysis and Reduction (CAS-HEAR). It supports analysts to find multiple levels of error causes and their causal relations by using predefined links between contextual factors and causal factors as well as links between causal factors. In addition, it is based on a complete accident model; hence, it helps analysts to conduct a thorough analysis without missing any important part of human error analysis. A prototype of CAS-HEAR was evaluated by nine field investigators from six railway organizations in Korea. Its overall usefulness in human error analysis was confirmed, although development of its simplified version and some modification of the contextual factors and causal factors are required in order to ensure its practical use.

Causal boundary for stably causal space-times

International Nuclear Information System (INIS)

Racz, I.

1987-12-01

The usual boundary constructions for space-times often yield an unsatisfactory boundary set. This problem is reviewed and a new solution is proposed. An explicit identification rule is given on the set of the ideal points of the space-time. This construction leads to a satisfactory boundary point set structure for stably causal space-times. The topological properties of the resulting causal boundary construction are examined. For the stably causal space-times each causal curve has a unique endpoint on the boundary set according to the extended Alexandrov topology. The extension of the space-time through the boundary is discussed. To describe the singularities the defined boundary sets have to be separated into two disjoint sets. (D.Gy.) 8 refs

Neural theory for the perception of causal actions.

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Fleischer, Falk; Christensen, Andrea; Caggiano, Vittorio; Thier, Peter; Giese, Martin A

2012-07-01

The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.

Evaluating WAIS-IV structure through a different psychometric lens: structural causal model discovery as an alternative to confirmatory factor analysis.

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van Dijk, Marjolein J A M; Claassen, Tom; Suwartono, Christiany; van der Veld, William M; van der Heijden, Paul T; Hendriks, Marc P H

Since the publication of the WAIS-IV in the U.S. in 2008, efforts have been made to explore the structural validity by applying factor analysis to various samples. This study aims to achieve a more fine-grained understanding of the structure of the Dutch language version of the WAIS-IV (WAIS-IV-NL) by applying an alternative analysis based on causal modeling in addition to confirmatory factor analysis (CFA). The Bayesian Constraint-based Causal Discovery (BCCD) algorithm learns underlying network structures directly from data and assesses more complex structures than is possible with factor analysis. WAIS-IV-NL profiles of two clinical samples of 202 patients (i.e. patients with temporal lobe epilepsy and a mixed psychiatric outpatient group) were analyzed and contrasted with a matched control group (N = 202) selected from the Dutch standardization sample of the WAIS-IV-NL to investigate internal structure by means of CFA and BCCD. With CFA, the four-factor structure as proposed by Wechsler demonstrates acceptable fit in all three subsamples. However, BCCD revealed three consistent clusters (verbal comprehension, visual processing, and processing speed) in all three subsamples. The combination of Arithmetic and Digit Span as a coherent working memory factor could not be verified, and Matrix Reasoning appeared to be isolated. With BCCD, some discrepancies from the proposed four-factor structure are exemplified. Furthermore, these results fit CHC theory of intelligence more clearly. Consistent clustering patterns indicate these results are robust. The structural causal discovery approach may be helpful in better interpreting existing tests, the development of new tests, and aid in diagnostic instruments.

Causal universe

CERN Document Server

Ellis, George FR; Pabjan, Tadeusz

2013-01-01

Written by philosophers, cosmologists, and physicists, this collection of essays deals with causality, which is a core issue for both science and philosophy. Readers will learn about different types of causality in complex systems and about new perspectives on this issue based on physical and cosmological considerations. In addition, the book includes essays pertaining to the problem of causality in ancient Greek philosophy, and to the problem of God's relation to the causal structures of nature viewed in the light of contemporary physics and cosmology.

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.

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...

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

Causal uncertainty, claimed and behavioural self-handicapping.

Science.gov (United States)

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 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.

Causality in Science

Directory of Open Access Journals (Sweden)

Cristina Puente Águeda

2011-10-01

Full Text Available Causality is a fundamental notion in every field of science. Since the times of Aristotle, causal relationships have been a matter of study as a way to generate knowledge and provide for explanations. In this paper I review the notion of causality through different scientific areas such as physics, biology, engineering, etc. In the scientific area, causality is usually seen as a precise relation: the same cause provokes always the same effect. But in the everyday world, the links between cause and effect are frequently imprecise or imperfect in nature. Fuzzy logic offers an adequate framework for dealing with imperfect causality, so a few notions of fuzzy causality are introduced.

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...

Tools for Developing a Quality Management Program: Human Factors and Systems Engineering Tools

International Nuclear Information System (INIS)

Caldwell, Barrett S.

2008-01-01

During the past 10 years, there has been growing acceptance and encouragement of partnerships between medical teams and engineers. Using human factors and systems engineering descriptions of process flows and operational sequences, the author's research laboratory has helped highlight opportunities for reducing adverse events and improving performance in health care and other high-consequence environments. This research emphasized studying human behavior that enhances system performance and a range of factors affecting adverse events, rather than a sole emphasis on human error causation. Developing a balanced evaluation requires novel approaches to causal analyses of adverse events and, more importantly, methods of recovery from adverse conditions. Recent work by the author's laboratory in collaboration with the Regenstrief Center for Healthcare Engineering has started to address possible improvements in taxonomies describing health care tasks. One major finding includes enhanced understanding of events and how event dynamics influence provider tasks and constraints. Another element of this research examines team coordination tasks that strongly affect patient care and quality management, but may be undervalued as 'indirect patient care' activities

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.

Spatial Causality. An application to the Deforestation Process in Bolivia

Directory of Open Access Journals (Sweden)

Javier Aliaga

2011-01-01

Full Text Available This paper analyses the causes of deforestation for a representative set of Bolivian municipalities. The literature on environmental economics insists on the importance of physical and social factors. We focus on the last group of variables. Our objective is to identify causal mechanisms between these factors of risk and the problem of deforestation. To this end, we present a testing strategy for spatial causality, based on a sequence of Lagrange Multipliers. The results that we obtain for the Bolivian case confirm only partially the traditional view of the problem of deforestation. Indeed, we only find unequivocal signs of causality in relation to the structure of property rights.

What causes breast cancer? A systematic review of causal attributions among breast cancer survivors and how these compare to expert-endorsed risk factors.

Science.gov (United States)

Dumalaon-Canaria, Jo Anne; Hutchinson, Amanda D; Prichard, Ivanka; Wilson, Carlene

2014-07-01

The aim of this paper was to review published research that analyzed causal attributions for breast cancer among women previously diagnosed with breast cancer. These attributions were compared with risk factors identified by published scientific evidence in order to determine the level of agreement between cancer survivors' attributions and expert opinion. A comprehensive search for articles, published between 1982 and 2012, reporting studies on causal attributions for breast cancer among patients and survivors was undertaken. Of 5,135 potentially relevant articles, 22 studies met the inclusion criteria. Two additional articles were sourced from reference lists of included studies. Results indicated a consistent belief among survivors that their own breast cancer could be attributed to family history, environmental factors, stress, fate, or chance. Lifestyle factors were less frequently identified, despite expert health information highlighting the importance of these factors in controlling and modifying cancer risk. This review demonstrated that misperceptions about the contribution of modifiable lifestyle factors to the risk of breast cancer have remained largely unchanged over the past 30 years. The findings of this review indicate that beliefs about the causes of breast cancer among affected women are not always consistent with the judgement of experts. Breast cancer survivors did not regularly identify causal factors supported by expert consensus such as age, physical inactivity, breast density, alcohol consumption, and reproductive history. Further research examining psychological predictors of attributions and the impact of cancer prevention messages on adjustment and well-being of cancer survivors is warranted.

Causal reasoning in physics

CERN Document Server

Frisch, Mathias

2014-01-01

Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly through a detailed examination of actual examples of causal notions in physics, including causal principles invoked in linear response theory and in representations of radiation phenomena. Offering a new perspective on the nature of scientific theories and causal reasoning, this book will be of interest to professional philosophers, graduate students, and anyone interested in the role of causal thinking in science.

A Hierarchical Causal Taxonomy of Psychopathology across the Life Span

Science.gov (United States)

Lahey, Benjamin B.; Krueger, Robert F.; Rathouz, Paul J.; Waldman, Irwin D.; Zald, David H.

2016-01-01

We propose a taxonomy of psychopathology based on patterns of shared causal influences identified in a review of multivariate behavior genetic studies that distinguish genetic and environmental influences that are either common to multiple dimensions of psychopathology or unique to each dimension. At the phenotypic level, first-order dimensions are defined by correlations among symptoms; correlations among first-order dimensions similarly define higher-order domains (e.g., internalizing or externalizing psychopathology). We hypothesize that the robust phenotypic correlations among first-order dimensions reflect a hierarchy of increasingly specific etiologic influences. Some nonspecific etiologic factors increase risk for all first-order dimensions of psychopathology to varying degrees through a general factor of psychopathology. Other nonspecific etiologic factors increase risk only for all first-order dimensions within a more specific higher-order domain. Furthermore, each first-order dimension has its own unique causal influences. Genetic and environmental influences common to family members tend to be nonspecific, whereas environmental influences unique to each individual are more dimension-specific. We posit that these causal influences on psychopathology are moderated by sex and developmental processes. This causal taxonomy also provides a novel framework for understanding the heterogeneity of each first-order dimension: Different persons exhibiting similar symptoms may be influenced by different combinations of etiologic influences from each of the three levels of the etiologic hierarchy. Furthermore, we relate the proposed causal taxonomy to transdimensional psychobiological processes, which also impact the heterogeneity of each psychopathology dimension. This causal taxonomy implies the need for changes in strategies for studying the etiology, psychobiology, prevention, and treatment of psychopathology. PMID:28004947

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...

Increasing fMRI sampling rate improves Granger causality estimates.

Directory of Open Access Journals (Sweden)

Fa-Hsuan Lin

Full Text Available Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD contrast based whole-head inverse imaging (InI. Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

The connexion of duality and causal properties for generalized free fields

International Nuclear Information System (INIS)

Garber, W.D.

1975-01-01

It is shown that the time-slice axiom and the diamond property are equivalent for the generalized free field. If, in addition, there is a mass gap, duality is equivalent to either causality requirement. It is further shown that the local rings associated with certain space-time regions are factors in the case of causal generalized free fields with mass gap. Necessary and sufficient conditions for causality and duality and some examples for causal and acausal generalized free fields are also given. (orig.) [de

An Empirical Investigation into Causality of Unsafe Act and Recovery during EOP Simulation

Energy Technology Data Exchange (ETDEWEB)

Choi, Sun Yeong; Jung, Won Dea [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2014-08-15

A data collection worksheet and guideline to collect HRA (Human Reliability Analysis) data with simulator data sources were developed for the HRA data handbook project by KAERI. Using the data worksheet, simulator data were collected and analyzed for an HRA qualitative database. The purpose of this paper is to define the causalities of operators' UAs (Unsafe Acts) ending in an inappropriate component manipulation and recovery during an EOP (Emergency Operating Procedure) operation, and to show some results for the causality from a case study. The reason we suggest the causality of an UA is because an inappropriate manipulation during an EOP operation can be resulted by the causality among operators in an MCR (Main Control Room). Therefore, a 'causality' data field was inserted into the data worksheet to identify the real initiator, and related operators for an inappropriate component manipulation. With this 'causality' data field, an HRA analyzer can establish who caused an UA (or a recovery) and who was involved in the process. They can also calculate the HEP (Human Error Probability) grouped by the initiator if they are interested in the HEP by the initiator.

Repeated causal decision making.

Science.gov (United States)

Hagmayer, York; Meder, Björn

2013-01-01

Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Triglyceride-rich lipoproteins as a causal factor for cardiovascular disease

Science.gov (United States)

Toth, Peter P

2016-01-01

Approximately 25% of US adults are estimated to have hypertriglyceridemia (triglyceride [TG] level ≥150 mg/dL [≥1.7 mmol/L]). Elevated TG levels are associated with increased cardiovascular disease (CVD) risk, and severe hypertriglyceridemia (TG levels ≥500 mg/dL [≥5.6 mmol/L]) is a well-established risk factor for acute pancreatitis. Plasma TG levels correspond to the sum of the TG content in TG-rich lipoproteins (TRLs; ie, very low-density lipoproteins plus chylomicrons) and their remnants. There remains some uncertainty regarding the direct causal role of TRLs in the progression of atherosclerosis and CVD, with cardiovascular outcome studies of TG-lowering agents, to date, having produced inconsistent results. Although low-density lipoprotein cholesterol (LDL-C) remains the primary treatment target to reduce CVD risk, a number of large-scale epidemiological studies have shown that elevated TG levels are independently associated with increased incidence of cardiovascular events, even in patients treated effectively with statins. Genetic studies have further clarified the causal association between TRLs and CVD. Variants in several key genes involved in TRL metabolism are strongly associated with CVD risk, with the strength of a variant’s effect on TG levels correlating with the magnitude of the variant’s effect on CVD. TRLs are thought to contribute to the progression of atherosclerosis and CVD via a number of direct and indirect mechanisms. They directly contribute to intimal cholesterol deposition and are also involved in the activation and enhancement of several proinflammatory, proapoptotic, and procoagulant pathways. Evidence suggests that non-high-density lipoprotein cholesterol, the sum of the total cholesterol carried by atherogenic lipoproteins (including LDL, TRL, and TRL remnants), provides a better indication of CVD risk than LDL-C, particularly in patients with hypertriglyceridemia. This article aims to provide an overview of the

Triglyceride-rich lipoproteins as a causal factor for cardiovascular disease.

Science.gov (United States)

Toth, Peter P

2016-01-01

Approximately 25% of US adults are estimated to have hypertriglyceridemia (triglyceride [TG] level ≥150 mg/dL [≥1.7 mmol/L]). Elevated TG levels are associated with increased cardiovascular disease (CVD) risk, and severe hypertriglyceridemia (TG levels ≥500 mg/dL [≥5.6 mmol/L]) is a well-established risk factor for acute pancreatitis. Plasma TG levels correspond to the sum of the TG content in TG-rich lipoproteins (TRLs; ie, very low-density lipoproteins plus chylomicrons) and their remnants. There remains some uncertainty regarding the direct causal role of TRLs in the progression of atherosclerosis and CVD, with cardiovascular outcome studies of TG-lowering agents, to date, having produced inconsistent results. Although low-density lipoprotein cholesterol (LDL-C) remains the primary treatment target to reduce CVD risk, a number of large-scale epidemiological studies have shown that elevated TG levels are independently associated with increased incidence of cardiovascular events, even in patients treated effectively with statins. Genetic studies have further clarified the causal association between TRLs and CVD. Variants in several key genes involved in TRL metabolism are strongly associated with CVD risk, with the strength of a variant's effect on TG levels correlating with the magnitude of the variant's effect on CVD. TRLs are thought to contribute to the progression of atherosclerosis and CVD via a number of direct and indirect mechanisms. They directly contribute to intimal cholesterol deposition and are also involved in the activation and enhancement of several proinflammatory, proapoptotic, and procoagulant pathways. Evidence suggests that non-high-density lipoprotein cholesterol, the sum of the total cholesterol carried by atherogenic lipoproteins (including LDL, TRL, and TRL remnants), provides a better indication of CVD risk than LDL-C, particularly in patients with hypertriglyceridemia. This article aims to provide an overview of the available

Second-order conditioning and conditioned inhibition: influences of speed versus accuracy on human causal learning.

Directory of Open Access Journals (Sweden)

Jessica C Lee

Full Text Available In human causal learning, excitatory and inhibitory learning effects can sometimes be found in the same paradigm by altering the learning conditions. This study aims to explore whether learning in the feature negative paradigm can be dissociated by emphasising speed over accuracy. In two causal learning experiments, participants were given a feature negative discrimination in which the outcome caused by one cue was prevented by the addition of another. Participants completed training trials either in a self-paced fashion with instructions emphasising accuracy, or under strict time constraints with instructions emphasising speed. Using summation tests in which the preventative cue was paired with another causal cue, participants in the accuracy groups correctly rated the preventative cue as if it reduced the probability of the outcome. However, participants in the speed groups rated the preventative cue as if it increased the probability of the outcome. In Experiment 1, both speed and accuracy groups later judged the same cue to be preventative in a reasoned inference task. Experiment 2 failed to find evidence of similar dissociations in retrospective revaluation (release from overshadowing vs. mediated extinction or learning about a redundant cue (blocking vs. augmentation. However in the same experiment, the tendency for the accuracy group to show conditioned inhibition and the speed group to show second-order conditioning was consistent even across sub-sets of the speed and accuracy groups with equivalent accuracy in training, suggesting that second-order conditioning is not merely a consequence of poorer acquisition. This dissociation mirrors the trade-off between second-order conditioning and conditioned inhibition observed in animal conditioning when training is extended.

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.

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

CERN Document Server

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

Causal reasoning and models of cognitive tasks for naval nuclear power plant operators; Raisonnement causal et modelisation de l`activite cognitive d`operateurs de chaufferie nucleaire navale

Energy Technology Data Exchange (ETDEWEB)

Salazar-Ferrer, P

1995-06-01

In complex industrial process control, causal reasoning appears as a major component in operators` cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs.

Human factors in training

International Nuclear Information System (INIS)

Dutton, J.W.; Brown, W.R.

1981-01-01

The Human Factors concept is a focused effort directed at those activities which require human involvement. Training is, by its nature, an activity totally dependent on the Human Factor. This paper identifies several concerns significant to training situations and discusses how Human Factor awareness can increase the quality of learning. Psychology in the training arena is applied Human Factors. Training is a method of communication represented by sender, medium, and receiver. Two-thirds of this communications model involves the human element directly

Problems of causality in environmental penal law. The relevance of causality problems on the environmental sector from the view of penal law. Kausalitaetsprobleme im Umweltstrafrecht. Die strafrechtliche Relevanz der Schwierigkeiten naturwissenschaftlicher Kausalfeststellung im Umweltbereich

Energy Technology Data Exchange (ETDEWEB)

Kleine-Cosack, E.

1988-01-01

The 'classic' elements of an offence against human health or property are not applicable in environmental law, owing to problems of causality. The new environmental penal law therefore focuses on the 'capability' of any act to damage human health, animal health, vegetation, water, air, or soil. It remarks doubtful whether this approach is more efficient. Further, there is still the problem of assessing damage. The book discusses causality problems in environmental penal law. Causality in a given case is discussed from the view of general causality laws and problems of proof. Other possible causes of damage must be excluded. The author discusses: Interdependences between scientific and penal causality, the problems of successful and potential offences, the relationship between individual and universal objects of legal protection, and procedural issues (e.g. the binding effect of experts' opinions on a given subject). (orig./HSCH).

Evidence and causality assessment in environmental epidemiology: methodological aspects

International Nuclear Information System (INIS)

Valleron, A.J.

2000-01-01

There are usually three major steps in the study of the possible impact of environmental factors on health: 1) to demonstrate that there is an association between exposure to the factor and the disease under study; 2) to demonstrate that this association is causal; 3) to evaluate the health benefit that could be obtained by removing the source of exposure. Statistical methods are commonly assumed to provide an objective way of achieving these three steps. This paper reviews some of the conditions that have to be met to allow proper interpretations and to avoid some of the controversies that are often found in health-environment studies. First, it should be remembered that the so-called P value which is used to qualify 'statistically significant' associations between risk factors and diseases does not give any indication of the probability that this association is actual, while far too often it is believed that it does. The probability that an association between an environmental factor and a disease is real could, however, be estimated by using Bayesian methods. These methods require that the a priori probabilities be stated, which is difficult to do in practice. Some directions to overcome this difficulty are presented. Second, the analysis of causality cannot be carried out on statistical grounds alone and the so-called 'causality criteria' are of limited practical interest. Definition of what is a cause, and upon which conditions a candidate factor of a disease can be considered as a cause, deserves much research effort, and careful consideration of the huge literature (mostly outside of the epidemiological field, for example in logic) which is devoted to this subject. Finally, the measurement of the role of a factor in a disease is very often assessed through the use of 'attributable fraction' or 'attributable mortality'. This should be done only when it is demonstrated that the considered factor is causal. Moreover, the interpretation of attributable fractions

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.

Causality re-established.

Science.gov (United States)

D'Ariano, Giacomo Mauro

2018-07-13

Causality has never gained the status of a 'law' or 'principle' in physics. Some recent literature has even popularized the false idea that causality is a notion that should be banned from theory. Such misconception relies on an alleged universality of the reversibility of the laws of physics, based either on the determinism of classical theory, or on the multiverse interpretation of quantum theory, in both cases motivated by mere interpretational requirements for realism of the theory. Here, I will show that a properly defined unambiguous notion of causality is a theorem of quantum theory, which is also a falsifiable proposition of the theory. Such a notion of causality appeared in the literature within the framework of operational probabilistic theories. It is a genuinely theoretical notion, corresponding to establishing a definite partial order among events, in the same way as we do by using the future causal cone on Minkowski space. The notion of causality is logically completely independent of the misidentified concept of 'determinism', and, being a consequence of quantum theory, is ubiquitous in physics. In addition, as classical theory can be regarded as a restriction of quantum theory, causality holds also in the classical case, although the determinism of the theory trivializes it. I then conclude by arguing that causality naturally establishes an arrow of time. This implies that the scenario of the 'block Universe' and the connected 'past hypothesis' are incompatible with causality, and thus with quantum theory: they are both doomed to remain mere interpretations and, as such, are not falsifiable, similar to the hypothesis of 'super-determinism'.This article is part of a discussion meeting issue 'Foundations of quantum mechanics and their impact on contemporary society'. © 2018 The Author(s).

Identifying causal linkages between environmental variables and African conflicts

Science.gov (United States)

Nguy-Robertson, A. L.; Dartevelle, S.

2017-12-01

Environmental variables that contribute to droughts, flooding, and other natural hazards are often identified as factors contributing to conflict; however, few studies attempt to quantify these causal linkages. Recent research has demonstrated that the environment operates within a dynamical system framework and the influence of variables can be identified from convergent cross mapping (CCM) between shadow manifolds. We propose to use CCM to identify causal linkages between environmental variables and incidences of conflict. This study utilizes time series data from Climate Forecast System ver. 2 and MODIS satellite sensors processed using Google Earth Engine to aggregate country and regional trends. These variables are then compared to Armed Conflict Location & Event Data Project observations at similar scales. Results provide relative rankings of variables and their linkage to conflict. Being able to identify which factors contributed more strongly to a conflict can allow policy makers to prepare solutions to mitigate future crises. Knowledge of the primary environmental factors can lead to the identification of other variables to examine in the causal network influencing conflict.

P3-10: Crossmodal Perceptual Grouping Modulates Subjective Causality between Action and Outcome

Directory of Open Access Journals (Sweden)

Takahiro Kawabe

2012-10-01

Full Text Available Agents have to determine which external events their action has causally produced. A sensation of causal relation between action and outcome is called subjective causality. Subjective causality has been linked to the comparator model. This model assumes that the brain compares an internal prediction for action outcome with an actual sensory outcome, distinguishing between self and externally produced outcomes depending on spatiotemporal congruency. However, recent studies have expressed some doubt about the idea that subjective causality arises depending solely on the spatiotemporal congruency, suggesting instead that other perceptual/cognitive factors play a critical role in determining subjective causality. We hypothesized that crossmodal grouping between action and outcome contributed to subjective causality. Crossmodal temporal grouping is an essential factor for crossmodal simultaneity judgments with ungrouped crossmodal signals likely to be judged as non-simultaneous. We predicted that subjective causality would decrease when an agent's action was not temporally grouped with action outcome. In the experiment, observers were asked to press a key in order to trigger a display change with some temporal delay. To disrupt temporal grouping between action and outcome, a task-irrelevant visual flash or tone was sometimes presented synchronously with the button press and/or the display change. Subjective causality was decreased when the flash or the tone was coincided with the button press. This demonstrates that perceptual grouping has a key role in determination of subjective causality, a result that is not accounted for by the standard comparator model.

Molecular epidemiology of acute leukemia in children: causal model, interaction of three factors-susceptibility, environmental exposure and vulnerability period.

Science.gov (United States)

Mejía-Aranguré, Juan Manuel

Acute leukemias have a huge morphological, cytogenetic and molecular heterogeneity and genetic polymorphisms associated with susceptibility. Every leukemia presents causal factors associated with the development of the disease. Particularly, when three factors are present, they result in the development of acute leukemia. These phenomena are susceptibility, environmental exposure and a period that, for this model, has been called the period of vulnerability. This framework shows how the concepts of molecular epidemiology have established a reference from which it is more feasible to identify the environmental factors associated with the development of leukemia in children. Subsequently, the arguments show that only susceptible children are likely to develop leukemia once exposed to an environmental factor. For additional exposure, if the child is not susceptible to leukemia, the disease does not develop. In addition, this exposure should occur during a time window when hematopoietic cells and their environment are more vulnerable to such interaction, causing the development of leukemia. This model seeks to predict the time when the leukemia develops and attempts to give a context in which the causality of childhood leukemia should be studied. This information can influence and reduce the risk of a child developing leukemia. Copyright © 2016 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.

Further properties of causal relationship: causal structure stability, new criteria for isocausality and counterexamples

International Nuclear Information System (INIS)

Garcia-Parrado, Alfonso; Sanchez, Miguel

2005-01-01

Recently (Garcia-Parrado and Senovilla 2003 Class. Quantum Grav. 20 625-64) the concept of causal mapping between spacetimes, essentially equivalent in this context to the chronological map defined in abstract chronological spaces, and the related notion of causal structure, have been introduced as new tools to study causality in Lorentzian geometry. In the present paper, these tools are further developed in several directions such as (i) causal mappings-and, thus, abstract chronological ones-do not preserve two levels of the standard hierarchy of causality conditions (however, they preserve the remaining levels as shown in the above reference), (ii) even though global hyperbolicity is a stable property (in the set of all time-oriented Lorentzian metrics on a fixed manifold), the causal structure of a globally hyperbolic spacetime can be unstable against perturbations; in fact, we show that the causal structures of Minkowski and Einstein static spacetimes remain stable, whereas that of de Sitter becomes unstable, (iii) general criteria allow us to discriminate different causal structures in some general spacetimes (e.g. globally hyperbolic, stationary standard); in particular, there are infinitely many different globally hyperbolic causal structures (and thus, different conformal ones) on R 2 (iv) plane waves with the same number of positive eigenvalues in the frequency matrix share the same causal structure and, thus, they have equal causal extensions and causal boundaries

Statistical Evaluation of Causal Factors Associated with Astronaut Shoulder Injury in Space Suits.

Science.gov (United States)

Anderson, Allison P; Newman, Dava J; Welsch, Roy E

2015-07-01

Shoulder injuries due to working inside the space suit are some of the most serious and debilitating injuries astronauts encounter. Space suit injuries occur primarily in the Neutral Buoyancy Laboratory (NBL) underwater training facility due to accumulated musculoskeletal stress. We quantitatively explored the underlying causal mechanisms of injury. Logistic regression was used to identify relevant space suit components, training environment variables, and anthropometric dimensions related to an increased propensity for space-suited injury. Two groups of subjects were analyzed: those whose reported shoulder incident is attributable to the NBL or working in the space suit, and those whose shoulder incidence began in active duty, meaning working in the suit could be a contributing factor. For both groups, percent of training performed in the space suit planar hard upper torso (HUT) was the most important predictor variable for injury. Frequency of training and recovery between training were also significant metrics. The most relevant anthropometric dimensions were bideltoid breadth, expanded chest depth, and shoulder circumference. Finally, record of previous injury was found to be a relevant predictor for subsequent injury. The first statistical model correctly identifies 39% of injured subjects, while the second model correctly identifies 68% of injured subjects. A review of the literature suggests this is the first work to quantitatively evaluate the hypothesized causal mechanisms of all space-suited shoulder injuries. Although limited in predictive capability, each of the identified variables can be monitored and modified operationally to reduce future impacts on an astronaut's health.

Assessing the Causality Factors in the Association between (Abdominal Obesity and Physical Activity among the Newfoundland Population–-A Mendelian Randomization Analysis

Directory of Open Access Journals (Sweden)

Frank Barning

2016-01-01

Full Text Available A total of 1,263 adults from Newfoundland and Labrador were studied in the research. Body mass index (BMI and percent trunk fat (PTF were analyzed as biomarkers for obesity. The Mendelian randomization (MR approach with two single-nucleotide polymorphisms in the fat-mass and obesity (FTO gene as instruments was employed to assess the causal effect. In both genders, increasing physical activity significantly reduced BMI and PTF when adjusted for age and the FTO gene. The effect of physical activity was stronger on PTF than BMI. Direct observational analyses showed significant increase in BMI/PTF when physical activity decreased. A similar association in MR analyses was not significant. The association between physical activity and BMI/PTF could be due to reversed causality or common confounding factors. Our study provides insights into the causal contributions of obesity to physical activity in adults. Health intervention strategies to increase physical activity among adults should include some other plans such as improving diet for reducing obesity.

A quantum causal discovery algorithm

Science.gov (United States)

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.

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.

Discrete causal theory emergent spacetime and the causal metric hypothesis

CERN Document Server

Dribus, Benjamin F

2017-01-01

This book evaluates and suggests potentially critical improvements to causal set theory, one of the best-motivated approaches to the outstanding problems of fundamental physics. Spacetime structure is of central importance to physics beyond general relativity and the standard model. The causal metric hypothesis treats causal relations as the basis of this structure. The book develops the consequences of this hypothesis under the assumption of a fundamental scale, with smooth spacetime geometry viewed as emergent. This approach resembles causal set theory, but differs in important ways; for example, the relative viewpoint, emphasizing relations between pairs of events, and relationships between pairs of histories, is central. The book culminates in a dynamical law for quantum spacetime, derived via generalized path summation.

Causal Analysis After Haavelmo

Science.gov (United States)

Heckman, James; Pinto, Rodrigo

2014-01-01

Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123

Applying causal mediation analysis to personality disorder research.

Science.gov (United States)

Walters, Glenn D

2018-01-01

This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Hybrid causal methodology and software platform for probabilistic risk assessment and safety monitoring of socio-technical systems

Energy Technology Data Exchange (ETDEWEB)

Groth, Katrina, E-mail: kgroth@umd.ed [Center for Risk and Reliability, 0151 Glenn L. Martin Hall, University of Maryland, College Park, MD 20742 (United States); Wang Chengdong; Mosleh, Ali [Center for Risk and Reliability, 0151 Glenn L. Martin Hall, University of Maryland, College Park, MD 20742 (United States)

2010-12-15

This paper introduces an integrated framework and software platform for probabilistic risk assessment (PRA) and safety monitoring of complex socio-technical systems. An overview of the three-layer hybrid causal logic (HCL) modeling approach and corresponding algorithms, implemented in the Trilith software platform, are provided. The HCL approach enhances typical PRA methods by quantitatively including the influence of soft causal factors introduced by human and organizational aspects of a system. The framework allows different modeling techniques to be used for different aspects of the socio-technical system. The HCL approach combines the power of traditional event sequence diagram (ESD)event tree (ET) and fault tree (FT) techniques for modeling deterministic causal paths, with the flexibility of Bayesian belief networks for modeling non-deterministic cause-effect relationships among system elements (suitable for modeling human and organizational influences). Trilith enables analysts to construct HCL models and perform quantitative risk assessment and management of complex systems. The risk management capabilities included are HCL-based risk importance measures, hazard identification and ranking, precursor analysis, safety indicator monitoring, and root cause analysis. This paper describes the capabilities of the Trilith platform and power of the HCL algorithm by use of example risk models for a type of aviation accident (aircraft taking off from the wrong runway).

Hybrid causal methodology and software platform for probabilistic risk assessment and safety monitoring of socio-technical systems

International Nuclear Information System (INIS)

Groth, Katrina; Wang Chengdong; Mosleh, Ali

2010-01-01

This paper introduces an integrated framework and software platform for probabilistic risk assessment (PRA) and safety monitoring of complex socio-technical systems. An overview of the three-layer hybrid causal logic (HCL) modeling approach and corresponding algorithms, implemented in the Trilith software platform, are provided. The HCL approach enhances typical PRA methods by quantitatively including the influence of soft causal factors introduced by human and organizational aspects of a system. The framework allows different modeling techniques to be used for different aspects of the socio-technical system. The HCL approach combines the power of traditional event sequence diagram (ESD)event tree (ET) and fault tree (FT) techniques for modeling deterministic causal paths, with the flexibility of Bayesian belief networks for modeling non-deterministic cause-effect relationships among system elements (suitable for modeling human and organizational influences). Trilith enables analysts to construct HCL models and perform quantitative risk assessment and management of complex systems. The risk management capabilities included are HCL-based risk importance measures, hazard identification and ranking, precursor analysis, safety indicator monitoring, and root cause analysis. This paper describes the capabilities of the Trilith platform and power of the HCL algorithm by use of example risk models for a type of aviation accident (aircraft taking off from the wrong runway).

Triglyceride-rich lipoproteins as a causal factor for cardiovascular disease

Directory of Open Access Journals (Sweden)

Toth PP

2016-05-01

Full Text Available Peter P Toth1,2 1Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, 2Preventive Cardiology, CGH Medical Center, Sterling, IL, USA Abstract: Approximately 25% of US adults are estimated to have hypertriglyceridemia (triglyceride [TG] level ≥150 mg/dL [≥1.7 mmol/L]. Elevated TG levels are associated with increased cardiovascular disease (CVD risk, and severe hypertriglyceridemia (TG levels ≥500 mg/dL [≥5.6 mmol/L] is a well-established risk factor for acute pancreatitis. Plasma TG levels correspond to the sum of the TG content in TG-rich lipoproteins (TRLs; ie, very low-density lipoproteins plus chylomicrons and their remnants. There remains some uncertainty regarding the direct causal role of TRLs in the progression of atherosclerosis and CVD, with cardiovascular outcome studies of TG-lowering agents, to date, having produced inconsistent results. Although low-density lipoprotein cholesterol (LDL-C remains the primary treatment target to reduce CVD risk, a number of large-scale epidemiological studies have shown that elevated TG levels are independently associated with increased incidence of cardiovascular events, even in patients treated effectively with statins. Genetic studies have further clarified the causal association between TRLs and CVD. Variants in several key genes involved in TRL metabolism are strongly associated with CVD risk, with the strength of a variant's effect on TG levels correlating with the magnitude of the variant's effect on CVD. TRLs are thought to contribute to the progression of atherosclerosis and CVD via a number of direct and indirect mechanisms. They directly contribute to intimal cholesterol deposition and are also involved in the activation and enhancement of several proinflammatory, proapoptotic, and procoagulant pathways. Evidence suggests that non-high-density lipoprotein cholesterol, the sum of the total cholesterol carried by

Structure induction in diagnostic causal reasoning.

Science.gov (United States)

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.

Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.

Science.gov (United States)

Matiasz, Nicholas J; Wood, Justin; Wang, Wei; Silva, Alcino J; Hsu, William

2017-01-01

Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.

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…

Human factor reliability program

International Nuclear Information System (INIS)

Knoblochova, L.

2017-01-01

The human factor's reliability program was at Slovenske elektrarne, a.s. (SE) nuclear power plants. introduced as one of the components Initiatives of Excellent Performance in 2011. The initiative's goal was to increase the reliability of both people and facilities, in response to 3 major areas of improvement - Need for improvement of the results, Troubleshooting support, Supporting the achievement of the company's goals. The human agent's reliability program is in practice included: - Tools to prevent human error; - Managerial observation and coaching; - Human factor analysis; -Quick information about the event with a human agent; -Human reliability timeline and performance indicators; - Basic, periodic and extraordinary training in human factor reliability(authors)

Critical human-factors issues in nuclear-power regulation and a recommended comprehensive human-factors long-range plan. Critical discussion of human factors areas of concern

International Nuclear Information System (INIS)

Hopkins, C.O.; Snyder, H.L.; Price, H.E.; Hornick, R.J.; Mackie, R.R.; Smillie, R.J.; Sugarman, R.C.

1982-08-01

This comprehensive long-range human factors plan for nuclear reactor regulation was developed by a Study Group of the Human Factors Society, Inc. This Study Group was selected by the Executive Council of the Society to provide a balanced, experienced human factors perspective to the applications of human factors scientific and engineering knowledge to nuclear power generation. The report is presented in three volumes. Volume 1 contains an Executive Summary of the 18-month effort and its conclusions. Volume 2 summarizes all known nuclear-related human factors activities, evaluates these activities wherever adequate information is available, and describes the recommended long-range (10-year) plan for human factors in regulation. Volume 3 elaborates upon each of the human factors issues and areas of recommended human factors involvement contained in the plan, and discusses the logic that led to the recommendations

Causal Evidence from Humans for the Role of Mediodorsal Nucleus of the Thalamus in Working Memory.

Science.gov (United States)

Peräkylä, Jari; Sun, Lihua; Lehtimäki, Kai; Peltola, Jukka; Öhman, Juha; Möttönen, Timo; Ogawa, Keith H; Hartikainen, Kaisa M

2017-12-01

The mediodorsal nucleus of the thalamus (MD), with its extensive connections to the lateral pFC, has been implicated in human working memory and executive functions. However, this understanding is based solely on indirect evidence from human lesion and imaging studies and animal studies. Direct, causal evidence from humans is missing. To obtain direct evidence for MD's role in humans, we studied patients treated with deep brain stimulation (DBS) for refractory epilepsy. This treatment is thought to prevent the generalization of a seizure by disrupting the functioning of the patient's anterior nuclei of the thalamus (ANT) with high-frequency electric stimulation. This structure is located superior and anterior to MD, and when the DBS lead is implanted in ANT, tip contacts of the lead typically penetrate through ANT into the adjoining MD. To study the role of MD in human executive functions and working memory, we periodically disrupted and recovered MD's function with high-frequency electric stimulation using DBS contacts reaching MD while participants performed a cognitive task engaging several aspects of executive functions. We hypothesized that the efficacy of executive functions, specifically working memory, is impaired when the functioning of MD is perturbed by high-frequency stimulation. Eight participants treated with ANT-DBS for refractory epilepsy performed a computer-based test of executive functions while DBS was repeatedly switched ON and OFF at MD and at the control location (ANT). In comparison to stimulation of the control location, when MD was stimulated, participants committed 2.26 times more errors in general (total errors; OR = 2.26, 95% CI [1.69, 3.01]) and 2.86 times more working memory-related errors specifically (incorrect button presses; OR = 2.88, CI [1.95, 4.24]). Similarly, participants committed 1.81 more errors in general ( OR = 1.81, CI [1.45, 2.24]) and 2.08 times more working memory-related errors ( OR = 2.08, CI [1.57, 2.75]) in

A Causal Model of Faculty Turnover Intentions.

Science.gov (United States)

Smart, John C.

1990-01-01

A causal model assesses the relative influence of individual attributes, institutional characteristics, contextual-work environment variables, and multiple measures of job satisfaction on faculty intentions to leave their current institutions. Factors considered include tenure status, age, institutional status, governance style, organizational…

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)

Causal reasoning and models of cognitive tasks for naval nuclear power plant operators

International Nuclear Information System (INIS)

Salazar-Ferrer, P.

1995-06-01

In complex industrial process control, causal reasoning appears as a major component in operators' cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs

Structural Equations and Causal Explanations: Some Challenges for Causal SEM

Science.gov (United States)

Markus, Keith A.

2010-01-01

One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…

The Causal Relationship between Health and Education Expenditures in Malaysia

Directory of Open Access Journals (Sweden)

Chor Foon TANG

2011-08-01

Full Text Available A major macroeconomic policy in generating economic growth is to encourage investments on human capital such as health and education. This is because both health and education make significant contribution to increasing productivity of the labour force which ultimately exerts a positive effect on raising output levels. A question that arises is whether investments on health and education have a causal relationship and if so, what is the directional causality? The objective of this study is to examine the causal relationship between health and education expenditures in Malaysia. This study covered annual data from 1970 to 2007. Using Granger causality as well as Toda and Yamamoto MWALD causality approaches, this study suggests that education Granger-causes health expenditure in both the short run and long run. The findings of this study implied that the Malaysian society places preference on education expenditure rather than health. This preference is not unexpected as generally, an educated and knowledgeable society precedes a healthy one. Before a society has attained a relatively higher level of education, it is less aware of the importance of health. Thus, expenditure on education should lead expenditure on health.

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.

Causal Reasoning in Medicine: Analysis of a Protocol.

Science.gov (United States)

Kuipers, Benjamin; Kassirer, Jerome P.

1984-01-01

Describes the construction of a knowledge representation from the identification of the problem (nephrotic syndrome) to a running computer simulation of causal reasoning to provide a vertical slice of the construction of a cognitive model. Interactions between textbook knowledge, observations of human experts, and computational requirements are…

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

Application of activity theory to analysis of human-related accidents: Method and case studies

International Nuclear Information System (INIS)

Yoon, Young Sik; Ham, Dong-Han; Yoon, Wan Chul

2016-01-01

This study proposes a new approach to human-related accident analysis based on activity theory. Most of the existing methods seem to be insufficient for comprehensive analysis of human activity-related contextual aspects of accidents when investigating the causes of human errors. Additionally, they identify causal factors and their interrelationships with a weak theoretical basis. We argue that activity theory offers useful concepts and insights to supplement existing methods. The proposed approach gives holistic contextual backgrounds for understanding and diagnosing human-related accidents. It also helps identify and organise causal factors in a consistent, systematic way. Two case studies in Korean nuclear power plants are presented to demonstrate the applicability of the proposed method. Human Factors Analysis and Classification System (HFACS) was also applied to the case studies. The results of using HFACS were then compared with those of using the proposed method. These case studies showed that the proposed approach could produce a meaningful set of human activity-related contextual factors, which cannot easily be obtained by using existing methods. It can be especially effective when analysts think it is important to diagnose accident situations with human activity-related contextual factors derived from a theoretically sound model and to identify accident-related contextual factors systematically. - Highlights: • This study proposes a new method for analysing human-related accidents. • The method was developed based on activity theory. • The concept of activity system model and contradiction was used in the method. • Two case studies in nuclear power plants are presented. • The method is helpful to consider causal factors systematically and comprehensively.

Reward-Guided Learning with and without Causal Attribution

Science.gov (United States)

Jocham, Gerhard; Brodersen, Kay H.; Constantinescu, Alexandra O.; Kahn, Martin C.; Ianni, Angela M.; Walton, Mark E.; Rushworth, Matthew F.S.; Behrens, Timothy E.J.

2016-01-01

Summary When an organism receives a reward, it is crucial to know which of many candidate actions caused this reward. However, recent work suggests that learning is possible even when this most fundamental assumption is not met. We used novel reward-guided learning paradigms in two fMRI studies to show that humans deploy separable learning mechanisms that operate in parallel. While behavior was dominated by precise contingent learning, it also revealed hallmarks of noncontingent learning strategies. These learning mechanisms were separable behaviorally and neurally. Lateral orbitofrontal cortex supported contingent learning and reflected contingencies between outcomes and their causal choices. Amygdala responses around reward times related to statistical patterns of learning. Time-based heuristic mechanisms were related to activity in sensorimotor corticostriatal circuitry. Our data point to the existence of several learning mechanisms in the human brain, of which only one relies on applying known rules about the causal structure of the task. PMID:26971947

Assessing the validity of road safety evaluation studies by analysing causal chains.

Science.gov (United States)

Elvik, Rune

2003-09-01

This paper discusses how the validity of road safety evaluation studies can be assessed by analysing causal chains. A causal chain denotes the path through which a road safety measure influences the number of accidents. Two cases are examined. One involves chemical de-icing of roads (salting). The intended causal chain of this measure is: spread of salt --> removal of snow and ice from the road surface --> improved friction --> shorter stopping distance --> fewer accidents. A Norwegian study that evaluated the effects of salting on accident rate provides information that describes this causal chain. This information indicates that the study overestimated the effect of salting on accident rate, and suggests that this estimate is influenced by confounding variables the study did not control for. The other case involves a traffic club for children. The intended causal chain in this study was: join the club --> improve knowledge --> improve behaviour --> reduce accident rate. In this case, results are rather messy, which suggests that the observed difference in accident rate between members and non-members of the traffic club is not primarily attributable to membership in the club. The two cases show that by analysing causal chains, one may uncover confounding factors that were not adequately controlled in a study. Lack of control for confounding factors remains the most serious threat to the validity of road safety evaluation studies.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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

Reasoning with Causal Cycles

Science.gov (United States)

Rehder, Bob

2017-01-01

This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…

David Bohm : causality and chance, letters to three women

CERN Document Server

2017-01-01

The letters transcribed in this book were written by physicist David Bohm to three close female acquaintances in the period 1950 to 1956. They provide a background to his causal interpretation of quantum mechanics and the Marxist philosophy that inspired his scientific work in quantum theory, probability and statistical mechanics. In his letters, Bohm reveals the ideas that led to his ground breaking book Causality and Chance in Modern Physics. The political arguments as well as the acute personal problems contained in these letters help to give a rounded, human picture of this leading scientist and twentieth century thinker.

Human factors in network security

OpenAIRE

Jones, Francis B.

1991-01-01

Human factors, such as ethics and education, are important factors in network information security. This thesis determines which human factors have significant influence on network security. Those factors are examined in relation to current security devices and procedures. Methods are introduced to evaluate security effectiveness by incorporating the appropriate human factors into network security controls

Revisiting Aristotle's causality: model for development in Nigeria ...

African Journals Online (AJOL)

Thus, the development equation must be balanced. Aristotle‟s theory of causality balances the equation. Since Aristotle has no theory of development therefore every individual, nation or industry in pursuit of development should seek to drive economic growth and human capital development together rather than focus on ...

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

Science.gov (United States)

Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

[Human factors in medicine].

Science.gov (United States)

Lazarovici, M; Trentzsch, H; Prückner, S

2017-01-01

The concept of human factors is commonly used in the context of patient safety and medical errors, all too often ambiguously. In actual fact, the term comprises a wide range of meanings from human-machine interfaces through human performance and limitations up to the point of working process design; however, human factors prevail as a substantial cause of error in complex systems. This article presents the full range of the term human factors from the (emergency) medical perspective. Based on the so-called Swiss cheese model by Reason, we explain the different types of error, what promotes their emergence and on which level of the model error prevention can be initiated.

The causality between energy consumption and economic growth in Turkey

International Nuclear Information System (INIS)

Erdal, Guelistan; Erdal, Hilmi; Esenguen, Kemal

2008-01-01

This paper applies the causality test to examine the causal relationship between primary energy consumption (EC) and real Gross National Product (GNP) for Turkey during 1970-2006. We employ unit root tests, the augmented Dickey-Fuller (ADF) and the Philips-Perron (PP), Johansen cointegration test, and Pair-wise Granger causality test to examine relation between EC and GNP. Our empirical results indicate that the two series are found to be non-stationary. However, first differences of these series lead to stationarity. Further, the results indicate that EC and GNP are cointegrated and there is bidirectional causality running from EC to GNP and vice versa. This means that an increase in EC directly affects economic growth and that economic growth also stimulates further EC. This bidirectional causality relationship between EC and GNP determined for Turkey at 1970-2006 period is in accordance with the ones in literature reported for similar countries. Consequently, we conclude that energy is a limiting factor to economic growth in Turkey and, hence, shocks to energy supply will have a negative impact on economic growth

Structure and Strength in Causal Induction

Science.gov (United States)

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2005-01-01

We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship…

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

Probable autoimmune causal relationship between periodontitis and ...

African Journals Online (AJOL)

Periodontitis is a multifactorial disease with microbial dental plaque as the initiator of periodontal disease. However, the manifestation and progression of the disease is influenced by a wide variety of determinants and factors. The strongest type of causal relationship is the association of systemic and periodontal disease.

Causality in Europeanization Research

DEFF Research Database (Denmark)

Lynggaard, Kennet

2012-01-01

to develop discursive institutional analytical frameworks and something that comes close to the formulation of hypothesis on the effects of European Union (EU) policies and institutions on domestic change. Even if these efforts so far do not necessarily amount to substantive theories or claims of causality......Discourse analysis as a methodology is perhaps not readily associated with substantive causality claims. At the same time the study of discourses is very much the study of conceptions of causal relations among a set, or sets, of agents. Within Europeanization research we have seen endeavours......, it suggests that discourse analysis and the study of causality are by no means opposites. The study of Europeanization discourses may even be seen as an essential step in the move towards claims of causality in Europeanization research. This chapter deals with the question of how we may move from the study...

Causality Illusion and Overconfidence in Predicting (QuasiStochastic Financial Events

Directory of Open Access Journals (Sweden)

Petr Houdek

2017-03-01

Full Text Available We argue that individuals systematically interpret sequences of events in a  causal manner. The aim of this article is to show that people do so even if they are aware of the stochastic nature of the respective sequence. The bias can explain some anomalous behaviour of investors in financial markets. Small as well as professional investors may illusorily perceive causality of former random success and future yield. Laboratory experiments testing the interpretation of stochastically occurring events in financial designs as well as analyses of real trading data from financial markets confirm that investors indeed interpret (quasirandom events casually; they make incorrect predictions and they egocentrically allocate responsibility for their success. The causality illusion induces overconfidence, inefficient investment and risk seeking. In the conclusion, we discuss factors that may limit effects of the causality illusion and suggest future areas for research.

Human factors guides

International Nuclear Information System (INIS)

Penington, J.

1995-10-01

This document presents human factors guides, which have been developed in order to provide licensees of the AECB with advice as to how to address human factors issues within the design and assessment process. This documents presents the results of a three part study undertaken to develop three guides which are enclosed in this document as Parts B, C and D. As part of the study human factors standards, guidelines, handbooks and other texts were researched, to define those which would be most useful to the users of the guides and for the production of the guides themselves. Detailed specifications were then produced to outline the proposed contents and format of the three guides. (author). 100 refs., 3 tabs., 11 figs

Human factors guides

Energy Technology Data Exchange (ETDEWEB)

Penington, J [PHF Services Inc., (Canada)

1995-10-01

This document presents human factors guides, which have been developed in order to provide licensees of the AECB with advice as to how to address human factors issues within the design and assessment process. This documents presents the results of a three part study undertaken to develop three guides which are enclosed in this document as Parts B, C and D. As part of the study human factors standards, guidelines, handbooks and other texts were researched, to define those which would be most useful to the users of the guides and for the production of the guides themselves. Detailed specifications were then produced to outline the proposed contents and format of the three guides. (author). 100 refs., 3 tabs., 11 figs.

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.

Do New Caledonian crows solve physical problems through causal reasoning?

Science.gov (United States)

Taylor, A.H.; Hunt, G.R.; Medina, F.S.; Gray, R.D.

2008-01-01

The extent to which animals other than humans can reason about physical problems is contentious. The benchmark test for this ability has been the trap-tube task. We presented New Caledonian crows with a series of two-trap versions of this problem. Three out of six crows solved the initial trap-tube. These crows continued to avoid the trap when the arbitrary features that had previously been associated with successful performances were removed. However, they did not avoid the trap when a hole and a functional trap were in the tube. In contrast to a recent primate study, the three crows then solved a causally equivalent but visually distinct problem—the trap-table task. The performance of the three crows across the four transfers made explanations based on chance, associative learning, visual and tactile generalization, and previous dispositions unlikely. Our findings suggest that New Caledonian crows can solve complex physical problems by reasoning both causally and analogically about causal relations. Causal and analogical reasoning may form the basis of the New Caledonian crow's exceptional tool skills. PMID:18796393

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

Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature.

Science.gov (United States)

Lee, Dong-Gi; Shin, Hyunjung

2017-05-18

Recently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an association. This representation results in the difficulty of identifying prior diseases and their influence on posterior diseases. In this paper, we propose a causal disease network that implements disease causality through text mining on biomedical literature. To identify the causality between diseases, the proposed method includes two schemes: the first is the lexicon-based causality term strength, which provides the causal strength on a variety of causality terms based on lexicon analysis. The second is the frequency-based causality strength, which determines the direction and strength of causality based on document and clause frequencies in the literature. We applied the proposed method to 6,617,833 PubMed literature, and chose 195 diseases to construct a causal disease network. From all possible pairs of disease nodes in the network, 1011 causal pairs of 149 diseases were extracted. The resulting network was compared with that of a previous study. In terms of both coverage and quality, the proposed method showed outperforming results; it determined 2.7 times more causalities and showed higher correlation with associated diseases than the existing method. This research has novelty in which the proposed method circumvents the limitations of time and cost in applying all possible causalities in biological experiments and it is a more advanced text mining technique by defining the concepts of causality term strength.

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

Dynamics of Quantum Causal Structures

Science.gov (United States)

Castro-Ruiz, Esteban; Giacomini, Flaminia; Brukner, Časlav

2018-01-01

It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B ). Here, we develop a framework for "dynamics of causal structures," i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B , via superposition of causal orders, to a channel from B to A . We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.

Causal inference based on counterfactuals

Directory of Open Access Journals (Sweden)

Höfler M

2005-09-01

Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

The argumentative impact of causal relations

DEFF Research Database (Denmark)

Nielsen, Anne Ellerup

1996-01-01

such as causality, explanation and justification. In certain types of discourse, causal relations also imply an intentional element. This paper describes the way in which the semantic and pragmatic functions of causal markers can be accounted for in terms of linguistic and rhetorical theories of argumentation.......The semantic relations between and within utterances are marked by the use of connectors and adverbials. One type of semantic relations is causal relations expressed by causal markers such as because, therefore, so, for, etc. Some of these markers cover different types of causal relations...

The latent causal chain of industrial water pollution in China.

Science.gov (United States)

Miao, Xin; Tang, Yanhong; Wong, Christina W Y; Zang, Hongyu

2015-01-01

The purpose of this paper is to discover the latent causal chain of industrial water pollution in China and find ways to cure the want on discharge of toxic waste from industries. It draws evidences from the past pollution incidents in China. Through further digging the back interests and relations by analyzing representative cases, extended theory about loophole derivations and causal chain effect is drawn. This theoretical breakthrough reflects deeper causality. Institutional defect instead of human error is confirmed as the deeper reason of frequent outbreaks of water pollution incidents in China. Ways for collaborative environmental governance are proposed. This paper contributes to a better understanding about the deep inducements of industrial water pollution in China, and, is meaningful for ensuring future prevention and mitigation of environmental pollution. It illuminates multiple dimensions for collaborative environmental governance to cure the stubborn problem.

Factores causales en las complicaciones de estomas intestinales en cirugía de emergencia. Hospital Luis Vernaza, 2013.

OpenAIRE

Morán Mancero, María Cristina

2014-01-01

El estoma intestinal es una apertura del intestino en la superficie del abdomen. Las estadísticas según diversos autores indica que hay aproximadamente unas 32.000 personas ostomizadas, de las cuales el 75% presentan una colostomía y el 10% una ileostomía. Objetivo:Determinar los factores causales en las complicaciones tempranas y tardías de los estomas intestinales en la cirugía de emergencia.Metodología:Estudio de tipo Retrospectivo, Observacional, Transversal, de nivel Descriptivo y diseño...

Granger-Causality Maps of Diffusion Processes

Czech Academy of Sciences Publication Activity Database

Wahl, B.; Feudel, U.; Hlinka, Jaroslav; Wächter, M.; Peinke, J.; Freund, J.A.

2016-01-01

Roč. 93, č. 2 16 February (2016), č. článku 022213. ISSN 2470-0045 R&D Projects: GA ČR GA13-23940S; GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Granger causality * stochastic process * diffusion process * nonlinear dynamical systems Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.366, year: 2016

Causal inference of asynchronous audiovisual speech

Directory of Open Access Journals (Sweden)

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.

Does Sufficient Evidence Exist to Support a Causal Association between Vitamin D Status and Cardiovascular Disease Risk? An Assessment Using Hill’s Criteria for Causality

OpenAIRE

Weyland, Patricia G.; Grant, William B.; Howie-Esquivel, Jill

2014-01-01

Serum 25-hydroxyvitamin D (25(OH)D) levels have been found to be inversely associated with both prevalent and incident cardiovascular disease (CVD) risk factors; dyslipidemia, hypertension and diabetes mellitus. This review looks for evidence of a causal association between low 25(OH)D levels and increased CVD risk. We evaluated journal articles in light of Hill’s criteria for causality in a biological system. The results of our assessment are as follows. Strength of association: many randomi...

Human factors information system

International Nuclear Information System (INIS)

Goodman, P.C.; DiPalo, C.A.

1991-01-01

Nuclear power plant safety is dependent upon human performance related to plant operations. To provide improvements in human performance, data collection and assessment play key roles. This paper reports on the Human factors Information System (HFIS) which is designed to meet the needs of the human factors specialists of the United States Nuclear Regulatory Commission. These specialists identify personnel errors and provide guidance designed to prevent such errors. HFIS is a simple and modular system designed for use on a personal computer. It is designed to contain four separate modules that provide information indicative of program or function effectiveness as well as safety-related human performance based on programmatic and performance data. These modules include the Human Factors Status module; the Regulatory Programs module; the Licensee Event Report module; and the Operator Requalification Performance module. Information form these modules can either be used separately or can be combined due to the integrated nature of the system. HFIS has the capability, therefore, to provide insights into those areas of human factors that can reduce the probability of events caused by personnel error at nuclear power plants and promote the health and safety of the public. This information system concept can be applied to other industries as well as the nuclear industry

Dynamics of Quantum Causal Structures

Directory of Open Access Journals (Sweden)

Esteban Castro-Ruiz

2018-03-01

Full Text Available It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B. Here, we develop a framework for “dynamics of causal structures,” i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B, via superposition of causal orders, to a channel from B to A. We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.

Research on injury compensation and health outcomes: ignoring the problem of reverse causality led to a biased conclusion.

Science.gov (United States)

Spearing, Natalie M; Connelly, Luke B; Nghiem, Hong S; Pobereskin, Louis

2012-11-01

This study highlights the serious consequences of ignoring reverse causality bias in studies on compensation-related factors and health outcomes and demonstrates a technique for resolving this problem of observational data. Data from an English longitudinal study on factors, including claims for compensation, associated with recovery from neck pain (whiplash) after rear-end collisions are used to demonstrate the potential for reverse causality bias. Although it is commonly believed that claiming compensation leads to worse recovery, it is also possible that poor recovery may lead to compensation claims--a point that is seldom considered and never addressed empirically. This pedagogical study compares the association between compensation claiming and recovery when reverse causality bias is ignored and when it is addressed, controlling for the same observable factors. When reverse causality is ignored, claimants appear to have a worse recovery than nonclaimants; however, when reverse causality bias is addressed, claiming compensation appears to have a beneficial effect on recovery, ceteris paribus. To avert biased policy and judicial decisions that might inadvertently disadvantage people with compensable injuries, there is an urgent need for researchers to address reverse causality bias in studies on compensation-related factors and health. Copyright © 2012 Elsevier Inc. All rights reserved.

Causal correlations between genes and linguistic features: The mechanism of gradual language evolution

OpenAIRE

Dediu, D.

2008-01-01

The causal correlations between human genetic variants and linguistic (typological) features could represent the mechanism required for gradual, accretionary models of language evolution. The causal link is mediated by the process of cultural transmission of language across generations in a population of genetically biased individuals. The particular case of Tone, ASPM and Microcephalin is discussed as an illustration. It is proposed that this type of genetically-influenced linguistic bias, c...

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

Habitability and Human Factors Contributions to Human Space Flight

Science.gov (United States)

Sumaya, Jennifer Boyer

2011-01-01

This slide presentation reviews the work of the Habitability and Human Factors Branch in support of human space flight in two main areas: Applied support to major space programs, and Space research. The field of Human Factors applies knowledge of human characteristics for the design of safer, more effective, and more efficient systems. This work is in several areas of the human space program: (1) Human-System Integration (HSI), (2) Orion Crew Exploration Vehicle, (3) Extravehicular Activity (EVA), (4) Lunar Surface Systems, (5) International Space Station (ISS), and (6) Human Research Program (HRP). After detailing the work done in these areas, the facilities that are available for human factors work are shown.

Dynamic causal modeling of hippocampal links within the human default mode network: Lateralization and computational stability of effective connections

Directory of Open Access Journals (Sweden)

Vadim Leonidovich Ushakov

2016-10-01

Full Text Available The purpose of this paper was to study causal relationships between left and right hippocampal regions (LHIP and RHIP, respectively within the default mode network (DMN as represented by its key structures: the medial prefrontal cortex (MPFC, posterior cingulate cortex (PCC and the inferior parietal cortex of left (LIPC and right (RIPC hemispheres. Furthermore, we were interested in testing the stability of the connectivity patterns when adding or deleting regions of interest. The functional magnetic resonance imaging (fMRI data from a group of 30 healthy right-handed subjects in the resting state were collected and a connectivity analysis was performed. To model the effective connectivity, we used the spectral Dynamic Causal Modeling (DCM. Three DCM analyses were completed. Two of them modeled interaction between five nodes that included four DMN key structures in addition to either LHIP or RHIP. The last DCM analysis modeled interactions between four nodes whereby one of the main DMN structures, PCC, was excluded from the analysis. The results of all DCM analyses indicated a high level of stability in the computational method: those parts of the winning models that included the key DMN structures demonstrated causal relations known from recent research. However, we discovered new results as well. First of all, we found a pronounced asymmetry in LHIP and RHIP connections. LHIP demonstrated a high involvement of DMN activity with preponderant information outflow to all other DMN regions. Causal interactions of LHIP were bidirectional only in the case of LIPC. On the contrary, RHIP was primarily affected by inputs from LIPC, RIPC and LHIP without influencing these or other DMN key structures. For the first time, an inhibitory link was found from MPFC to LIPC, which may indicate the subjects’ effort to maintain a resting state. Functional connectivity data echoed these results, though they also showed links not reflected in the patterns of

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

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

The role of causal maps in intellectual capital measurement and management

DEFF Research Database (Denmark)

Montemari, Marco; Nielsen, Christian

2013-01-01

Purpose – The purpose of this paper is to investigate the measurement and the management of the dynamic aspects of intellectual capital through the use of causal mapping. Design/methodology/approach – The study details the methods utilized in a single in-depth case study of a network-based business...... of the lag and the persistence of the effects of managerial actions. In addition, it can signal when and how to refine and update the causal map. The combination of these factors supports the dynamic measurement and management of intellectual capital. Research limitations/implications – The paper presented...... the causal mapping approach into practice. Practical implications – The paper highlights the need to build causal maps to enhance the measurement and management of intellectual capital, which is dynamic of nature. As a consequence, this tool can be useful for companies to monitor their intangibles...

Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information

Science.gov (United States)

Li, Songting; Xiao, Yanyang; Zhou, Douglas; Cai, David

2018-05-01

The Granger causality (GC) analysis has been extensively applied to infer causal interactions in dynamical systems arising from economy and finance, physics, bioinformatics, neuroscience, social science, and many other fields. In the presence of potential nonlinearity in these systems, the validity of the GC analysis in general is questionable. To illustrate this, here we first construct minimal nonlinear systems and show that the GC analysis fails to infer causal relations in these systems—it gives rise to all types of incorrect causal directions. In contrast, we show that the time-delayed mutual information (TDMI) analysis is able to successfully identify the direction of interactions underlying these nonlinear systems. We then apply both methods to neuroscience data collected from experiments and demonstrate that the TDMI analysis but not the GC analysis can identify the direction of interactions among neuronal signals. Our work exemplifies inference hazards in the GC analysis in nonlinear systems and suggests that the TDMI analysis can be an appropriate tool in such a case.

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

Science.gov (United States)

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

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 the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.

Human Factors Review Plan

International Nuclear Information System (INIS)

Paramore, B.; Peterson, L.R.

1985-12-01

''Human Factors'' is concerned with the incorporation of human user considerations into a system in order to maximize human reliability and reduce errors. This Review Plan is intended to assist in the assessment of human factors conditions in existing DOE facilities. In addition to specifying assessment methodologies, the plan describes techniques for improving conditions which are found to not adequately support reliable human performance. The following topics are addressed: (1) selection of areas for review describes techniques for needs assessment to assist in selecting and prioritizing areas for review; (2) human factors engineering review is concerned with optimizing the interfaces between people and equipment and people and their work environment; (3) procedures review evaluates completeness and accuracy of procedures, as well as their usability and management; (4) organizational interface review is concerned with communication and coordination between all levels of an organization; and (5) training review evaluates training program criteria such as those involving: trainee selection, qualification of training staff, content and conduct of training, requalification training, and program management

Human Factors Review Plan

Energy Technology Data Exchange (ETDEWEB)

Paramore, B.; Peterson, L.R. (eds.)

1985-12-01

''Human Factors'' is concerned with the incorporation of human user considerations into a system in order to maximize human reliability and reduce errors. This Review Plan is intended to assist in the assessment of human factors conditions in existing DOE facilities. In addition to specifying assessment methodologies, the plan describes techniques for improving conditions which are found to not adequately support reliable human performance. The following topics are addressed: (1) selection of areas for review describes techniques for needs assessment to assist in selecting and prioritizing areas for review; (2) human factors engineering review is concerned with optimizing the interfaces between people and equipment and people and their work environment; (3) procedures review evaluates completeness and accuracy of procedures, as well as their usability and management; (4) organizational interface review is concerned with communication and coordination between all levels of an organization; and (5) training review evaluates training program criteria such as those involving: trainee selection, qualification of training staff, content and conduct of training, requalification training, and program management.

Bilirubin as a potential causal factor in type 2 diabetes risk: a Mendelian randomization study

Science.gov (United States)

Abbasi, Ali; Deetman, Petronella E.; Corpeleijn, Eva; Gansevoort, Ron T.; Gans, Rijk O.B.; Hillege, Hans L.; van der Harst, Pim; Stolk, Ronald P.; Navis, Gerjan; Alizadeh, Behrooz Z.; Bakker, Stephan J.L.

2014-01-01

Circulating bilirubin, a natural antioxidant, is associated with decreased risk of type 2 diabetes (T2D), but the nature of the relationship remains unknown. We performed Mendelian randomization in a prospective cohort of 3,381 participants free of diabetes at baseline (aged 28-75 years; women, 52.6%). We used rs6742078 located in UDP-glucuronosyltransferase (UGT1A1) locus as instrumental variable (IV) to study a potential causal effect of serum total bilirubin on T2D risk. T2D developed in a total of 210 (6.2%) participants during a median follow-up of 7.8 years. In adjusted analyses, rs6742078, which explained 19.5% of bilirubin variation, was strongly associated with total bilirubin (a 0.68-SD increase in bilirubin levels per T allele; Pbilirubin levels, we observed a 25% (OR 0.75 [95%CI, 0.62-0.92]; P=0.004) lower risk of T2D. In Mendelian randomization analysis, the causal risk reduction for T2D was estimated to be 42% (causal ORIVestimation per 1-SD increase in log-transformed bilirubin 0.58 [95%CI, 0.39-0.84]; P=0.005), which was comparable to the observational estimate (Durbin-Wu-Hausman chi-square test Pfor difference =0.19). These novel results provide evidence that elevated bilirubin is causally associated with risk of T2D and support its role as a protective determinant. PMID:25368098

Does Sufficient Evidence Exist to Support a Causal Association between Vitamin D Status and Cardiovascular Disease Risk? An Assessment Using Hill’s Criteria for Causality

Directory of Open Access Journals (Sweden)

Patricia G. Weyland

2014-09-01

Full Text Available Serum 25-hydroxyvitamin D (25(OHD levels have been found to be inversely associated with both prevalent and incident cardiovascular disease (CVD risk factors; dyslipidemia, hypertension and diabetes mellitus. This review looks for evidence of a causal association between low 25(OHD levels and increased CVD risk. We evaluated journal articles in light of Hill’s criteria for causality in a biological system. The results of our assessment are as follows. Strength of association: many randomized controlled trials (RCTs, prospective and cross-sectional studies found statistically significant inverse associations between 25(OHD levels and CVD risk factors. Consistency of observed association: most studies found statistically significant inverse associations between 25(OHD levels and CVD risk factors in various populations, locations and circumstances. Temporality of association: many RCTs and prospective studies found statistically significant inverse associations between 25(OHD levels and CVD risk factors. Biological gradient (dose-response curve: most studies assessing 25(OHD levels and CVD risk found an inverse association exhibiting a linear biological gradient. Plausibility of biology: several plausible cellular-level causative mechanisms and biological pathways may lead from a low 25(OHD level to increased risk for CVD with mediators, such as dyslipidemia, hypertension and diabetes mellitus. Experimental evidence: some well-designed RCTs found increased CVD risk factors with decreasing 25(OHD levels. Analogy: the association between serum 25(OHD levels and CVD risk is analogous to that between 25(OHD levels and the risk of overall cancer, periodontal disease, multiple sclerosis and breast cancer. Conclusion: all relevant Hill criteria for a causal association in a biological system are satisfied to indicate a low 25(OHD level as a CVD risk factor.

Paradoxical Behavior of Granger Causality

Science.gov (United States)

Witt, Annette; Battaglia, Demian; Gail, Alexander

2013-03-01

Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen

The development of human factors technologies -The development of human factors experimental evaluation techniques-

International Nuclear Information System (INIS)

Shim, Bong Sik; Oh, In Suk; Cha, Kyung Hoh; Lee, Hyun Chul

1995-07-01

In this year, we studied the followings: 1) Development of operator mental workload evaluation techniques, 2) Development of a prototype for preliminary human factors experiment, 3) Suitability test of information display on a large scale display panel, 4) Development of guidelines for VDU-based control room design, 5) Development of integrated test facility (ITF). 6) Establishment of an eye tracking system, and we got the following results: 1) Mental workload evaluation techniques for MMI evaluation, 2) PROTOPEX (PROTOtype for preliminary human factors experiment) for preliminary human factors experiments, 3) Usage methods of APTEA (Analysis-Prototyping-Training-Experiment-Analysis) experiment design, 4) Design guidelines for human factors verification, 5) Detail design requirements and development plan of ITF, 6) Eye movement measurement system. 38 figs, 20 tabs, 54 refs. (Author)

A trend analysis of human error events for proactive prevention of accidents. Methodology development and effective utilization

International Nuclear Information System (INIS)

Hirotsu, Yuko; Ebisu, Mitsuhiro; Aikawa, Takeshi; Matsubara, Katsuyuki

2006-01-01

This paper described methods for analyzing human error events that has been accumulated in the individual plant and for utilizing the result to prevent accidents proactively. Firstly, a categorization framework of trigger action and causal factors of human error events were reexamined, and the procedure to analyze human error events was reviewed based on the framework. Secondly, a method for identifying the common characteristics of trigger action data and of causal factor data accumulated by analyzing human error events was clarified. In addition, to utilize the results of trend analysis effectively, methods to develop teaching material for safety education, to develop the checkpoints for the error prevention and to introduce an error management process for strategic error prevention were proposed. (author)

Causal symmetric spaces

CERN Document Server

Olafsson, Gestur; Helgason, Sigurdur

1996-01-01

This book is intended to introduce researchers and graduate students to the concepts of causal symmetric spaces. To date, results of recent studies considered standard by specialists have not been widely published. This book seeks to bring this information to students and researchers in geometry and analysis on causal symmetric spaces.Includes the newest results in harmonic analysis including Spherical functions on ordered symmetric space and the holmorphic discrete series and Hardy spaces on compactly casual symmetric spacesDeals with the infinitesimal situation, coverings of symmetric spaces, classification of causal symmetric pairs and invariant cone fieldsPresents basic geometric properties of semi-simple symmetric spacesIncludes appendices on Lie algebras and Lie groups, Bounded symmetric domains (Cayley transforms), Antiholomorphic Involutions on Bounded Domains and Para-Hermitian Symmetric Spaces

How contrast situations affect the assignment of causality in symmetric physical settings.

Science.gov (United States)

Beller, Sieghard; Bender, Andrea

2014-01-01

In determining the prime cause of a physical event, people often weight one of two entities in a symmetric physical relation as more important for bringing about the causal effect than the other. In a broad survey (Bender and Beller, 2011), we documented such weighting effects for different kinds of physical events and found that their direction and strength depended on a variety of factors. Here, we focus on one of those: adding a contrast situation that-while being formally irrelevant-foregrounds one of the factors and thus frames the task in a specific way. In two experiments, we generalize and validate our previous findings by using different stimulus material (in Experiment 1), by applying a different response format to elicit causal assignments, an analog rating scale instead of a forced-choice decision (in Experiment 2), and by eliciting explanations for the physical events in question (in both Experiments). The results generally confirm the contrast effects for both response formats; however, the effects were more pronounced with the force-choice format than with the rating format. People tended to refer to the given contrast in their explanations, which validates our manipulation. Finally, people's causal assignments are reflected in the type of explanation given in that contrast and property explanations were associated with biased causal assignments, whereas relational explanations were associated with unbiased assignments. In the discussion, we pick up the normative questions of whether or not these contrast effects constitute a bias in causal reasoning.

Human Factors in Marine Casualties

Directory of Open Access Journals (Sweden)

Jelenko Švetak

2002-05-01

Full Text Available Human factors play an important role in the origin of accidents,and it is commonly claimed that between seventy andninety-five percent of industrial and transport accidents involvehuman factors, see Figure 1.Some authorities, however, claim that ultimately, all accidentsinvolve human factors.

An Equivalent Emission Minimization Strategy for Causal Optimal Control of Diesel Engines

Directory of Open Access Journals (Sweden)

Stephan Zentner

2014-02-01

Full Text Available One of the main challenges during the development of operating strategies for modern diesel engines is the reduction of the CO2 emissions, while complying with ever more stringent limits for the pollutant emissions. The inherent trade-off between the emissions of CO2 and pollutants renders a simultaneous reduction difficult. Therefore, an optimal operating strategy is sought that yields minimal CO2 emissions, while holding the cumulative pollutant emissions at the allowed level. Such an operating strategy can be obtained offline by solving a constrained optimal control problem. However, the final-value constraint on the cumulated pollutant emissions prevents this approach from being adopted for causal control. This paper proposes a framework for causal optimal control of diesel engines. The optimization problem can be solved online when the constrained minimization of the CO2 emissions is reformulated as an unconstrained minimization of the CO2 emissions and the weighted pollutant emissions (i.e., equivalent emissions. However, the weighting factors are not known a priori. A method for the online calculation of these weighting factors is proposed. It is based on the Hamilton–Jacobi–Bellman (HJB equation and a physically motivated approximation of the optimal cost-to-go. A case study shows that the causal control strategy defined by the online calculation of the equivalence factor and the minimization of the equivalent emissions is only slightly inferior to the non-causal offline optimization, while being applicable to online control.

Human factors and safe patient care.

Science.gov (United States)

Norris, Beverley

2009-03-01

This paper aims to introduce the topic of human factors to nursing management and to identify areas where it can be applied to patient safety. Human factors is a discipline established in most safety critical industries and uses knowledge about human behaviour in the analysis and design of complex systems, yet it is relatively new to many in healthcare. Most safety critical industries have developed tools and techniques to apply human factors to system design, and these have been reviewed together with those resources already available for use in healthcare. Models of human behaviour such as the nature and patterns of human error, information processing, decision-making and team work have clear applications to healthcare. Human factors focus on a system view of safety, and propose that safety should, where possible, be 'designed in'. Other interventions such as building defences, mitigating hazards and education and training should only be used where design solutions cannot be found. Simple human factors principles such as: designing for standardization; the involvement of users and staff in designing services and procuring equipment; understanding how errors occur; and the workarounds that staff will inevitably take are vital considerations in improving patient safety. Opportunities for the application of human factors to healthcare and improved patient safety are discussed. Some existing tools and techniques for applying human factors in nursing management are also presented.

Investigating the multi-causal and complex nature of the accident causal influence of construction project features.

Science.gov (United States)

Manu, Patrick A; Ankrah, Nii A; Proverbs, David G; Suresh, Subashini

2012-09-01

Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement. Copyright © 2011 Elsevier Ltd. All rights reserved.

The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke.

Science.gov (United States)

Jobe, Thomas H.; Helgason, Cathy M.

1998-04-01

Twentieth century medical science has embraced nineteenth century Boolean probability theory based upon two-valued Aristotelian logic. With the later addition of bit-based, von Neumann structured computational architectures, an epistemology based on randomness has led to a bivalent epidemiological methodology that dominates medical decision making. In contrast, fuzzy logic, based on twentieth century multi-valued logic, and computational structures that are content addressed and adaptively modified, has advanced a new scientific paradigm for the twenty-first century. Diseases such as stroke involve multiple concomitant causal factors that are difficult to represent using conventional statistical methods. We tested which paradigm best represented this complex multi-causal clinical phenomenon-stroke. We show that the fuzzy logic paradigm better represented clinical complexity in cerebrovascular disease than current probability theory based methodology. We believe this finding is generalizable to all of clinical science since multiple concomitant causal factors are involved in nearly all known pathological processes.

Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification

Science.gov (United States)

Faye, Laura L.; Machiela, Mitchell J.; Kraft, Peter; Bull, Shelley B.; Sun, Lei

2013-01-01

Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website. PMID:23950724

Human factors in nuclear safety oversight

International Nuclear Information System (INIS)

Taylor, K.

1989-01-01

The mission of the nuclear safety oversight function at the Savannah River Plant is to enhance the process and nuclear safety of site facilities. One of the major goals surrounding this mission is the reduction of human error. It is for this reason that several human factors engineers are assigned to the Operations assessment Group of the Facility Safety Evaluation Section (FSES). The initial task of the human factors contingent was the design and implementation of a site wide root cause analysis program. The intent of this system is to determine the most prevalent sources of human error in facility operations and to assist in determining where the limited human factors resources should be focused. In this paper the strategy used to educate the organization about the field of human factors is described. Creating an awareness of the importance of human factors engineering in all facets of design, operation, and maintenance is considered to be an important step in reducing the rate of human error

Principal stratification in causal inference.

Science.gov (United States)

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 Set Generator and Action Computer

OpenAIRE

Cunningham, William; Krioukov, Dmitri

2017-01-01

The causal set approach to quantum gravity has gained traction over the past three decades, but numerical experiments involving causal sets have been limited to relatively small scales. The software suite presented here provides a new framework for the generation and study of causal sets. Its efficiency surpasses previous implementations by several orders of magnitude. We highlight several important features of the code, including the compact data structures, the $O(N^2)$ causal set generatio...

Spectral dimension in causal set quantum gravity

International Nuclear Information System (INIS)

Eichhorn, Astrid; Mizera, Sebastian

2014-01-01

We evaluate the spectral dimension in causal set quantum gravity by simulating random walks on causal sets. In contrast to other approaches to quantum gravity, we find an increasing spectral dimension at small scales. This observation can be connected to the nonlocality of causal set theory that is deeply rooted in its fundamentally Lorentzian nature. Based on its large-scale behaviour, we conjecture that the spectral dimension can serve as a tool to distinguish causal sets that approximate manifolds from those that do not. As a new tool to probe quantum spacetime in different quantum gravity approaches, we introduce a novel dimensional estimator, the causal spectral dimension, based on the meeting probability of two random walkers, which respect the causal structure of the quantum spacetime. We discuss a causal-set example, where the spectral dimension and the causal spectral dimension differ, due to the existence of a preferred foliation. (paper)

The causal link between energy and output growth: Evidence from Markov switching Granger causality

International Nuclear Information System (INIS)

Kandemir Kocaaslan, Ozge

2013-01-01

In this paper we empirically investigate the causal link between energy consumption and economic growth employing a Markov switching Granger causality analysis. We carry out our investigation using annual U.S. real GDP, total final energy consumption and total primary energy consumption data which cover the period between 1968 and 2010. We find that there are significant changes in the causal relation between energy consumption and economic growth over the sample period under investigation. Our results show that total final energy consumption and total primary energy consumption have significant predictive content for real economic activity in the U.S. economy. Furthermore, the causality running from energy consumption to output growth seems to be strongly apparent particularly during the periods of economic downturn and energy crisis. We also document that output growth has predictive power in explaining total energy consumption. Furthermore, the power of output growth in predicting total energy consumption is found to diminish after the mid of 1980s. - Highlights: • Total energy consumption has predictive content for real economic activity. • The causality from energy to output growth is apparent in the periods of recession. • The causality from energy to output growth is strong in the periods of energy crisis. • Output growth has predictive power in explaining total energy consumption. • The power of output growth in explaining energy diminishes after the mid of 1980s

Pilot Critical Incident Reports as a Means to Identify Human Factors of Remotely Piloted Aircraft

Science.gov (United States)

Hobbs, Alan; Cardoza, Colleen; Null, Cynthia

2016-01-01

It has been estimated that aviation accidents are typically preceded by numerous minor incidents arising from the same causal factors that ultimately produced the accident. Accident databases provide in-depth information on a relatively small number of occurrences, however incident databases have the potential to provide insights into the human factors of Remotely Piloted Aircraft System (RPAS) operations based on a larger volume of less-detailed reports. Currently, there is a lack of incident data dealing with the human factors of unmanned aircraft systems. An exploratory study is being conducted to examine the feasibility of collecting voluntary critical incident reports from RPAS pilots. Twenty-three experienced RPAS pilots volunteered to participate in focus groups in which they described critical incidents from their own experience. Participants were asked to recall (1) incidents that revealed a system flaw, or (2) highlighted a case where the human operator contributed to system resilience or mission success. Participants were asked to only report incidents that could be included in a public document. During each focus group session, a note taker produced a de-identified written record of the incident narratives. At the end of the session, participants reviewed each written incident report, and made edits and corrections as necessary. The incidents were later analyzed to identify contributing factors, with a focus on design issues that either hindered or assisted the pilot during the events. A total of 90 incidents were reported. Human factor issues included the impact of reduced sensory cues, traffic separation in the absence of an out-the-window view, control latencies, vigilance during monotonous and ultra-long endurance flights, control station design considerations, transfer of control between control stations, the management of lost link procedures, and decision-making during emergencies. Pilots participated willingly and enthusiastically in the study

Study on safety educations against individual causal factors of unsafe acts and specification of target trainees

International Nuclear Information System (INIS)

Hirose, Ayako; Takeda, Daisuke

2016-01-01

Many accidents and incidents are caused by unsafe acts. It is important to reduce these unsafe acts for preventing the accidents. The countermeasures for each causal factor behind unsafe acts are needed, however, comparing with improvement of facilities, workers-oriented measures such as safety educations are not sufficient. Then the purposes of this study are as follows: 1) to investigate the individual factors which have great impact of unsafe acts and the existing safety educations which aim to mitigate the impact of these factors, 2) to specify the target trainees to perform these safety educations. To identify common factors that affect unsafe act significantly, a web survey was conducted to 500 workers who have regularly carried out accident prediction training (i.e. Kiken-Yochi training). They were asked the situation which they were apt to act unsafely by free description. As the result, the following three main factors were extracted: impatience, overconfidence, and bothersome. Also, it was found that there were few existing safety educations which aim to mitigate the impact of these factors except for overconfidence. To specify the target trainees to perform safety educations which aim to mitigate the impact of these three factors, another web survey was conducted to 200 personnel in charge of safety at the workplace. They were asked the features of workers who tended to act unsafely by age group. The relationship between the factor that need to mitigate and the trainee who need to receive the education were clarified from the survey. (author)

Human factors in resuscitation teaching.

Science.gov (United States)

Norris, Elizabeth M; Lockey, Andrew S

2012-04-01

There is an increasing interest in human factors within the healthcare environment reflecting the understanding of their impact on safety. The aim of this paper is to explore how human factors might be taught on resuscitation courses, and improve course outcomes in terms of improved mortality and morbidity for patients. The delivery of human factors training is important and this review explores the work that has been delivered already and areas for future research and teaching. Medline was searched using MESH terms Resuscitation as a Major concept and Patient or Leadership as core terms. The abstracts were read and 25 full length articles reviewed. Critical incident reporting has shown four recurring problems: lack of organisation at an arrest, lack of equipment, non functioning equipment, and obstructions preventing good care. Of these, the first relates directly to the concept of human factors. Team dynamics for both team membership and leadership, management of stress, conflict and the role of debriefing are highlighted. Possible strategies for teaching them are discussed. Four strategies for improving human factors training are discussed: team dynamics (including team membership and leadership behaviour), the influence of stress, debriefing, and conflict within teams. This review illustrates how human factor training might be integrated further into life support training without jeopardising the core content and lengthening the courses. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

¿CONFIEREN PODERES CAUSALES LOS UNIVERSALES TRASCENDENTES?

Directory of Open Access Journals (Sweden)

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.

Re-thinking local causality

NARCIS (Netherlands)

Friederich, Simon

There is widespread belief in a tension between quantum theory and special relativity, motivated by the idea that quantum theory violates J. S. Bell's criterion of local causality, which is meant to implement the causal structure of relativistic space-time. This paper argues that if one takes the

Critical human-factors issues in nuclear-power regulation and a recommended comprehensive human-factors long-range plan. Executive summary

International Nuclear Information System (INIS)

Hopkins, C.O.; Snyder, H.L.; Price, H.E.; Hornick, R.J.; Mackie, R.R.; Smillie, R.J.; Sugarman, R.C.

1982-08-01

This comprehensive long-range human factors plan for nuclear reactor regulation was developed by a Study Group of the Human Factors Society, Inc. This Study Group was selected by the Executive Council of the Society to provide a balanced, experienced human factors perspective to the applications of human factors scientific and engineering knowledge to nuclear power generation. The report is presented in three volumes. Volume 1 contains an Executive Summary of the 18-month effort and its conclusions. Volume 2 summarizes all known nuclear-related human factors activities, evaluates these activities wherever adequate information is available, and describes the recommended long-range (10-year) plan for human factors in regulation. Volume 3 elaborates upon each of the human factors issues and areas of recommended human factors involvement contained in the plan, and discusses the logic that led to the recommendations

Developing a Causal Model of Human and Organizational Culture Factors Affecting the Knowledge Management Maturity Using Meta-Synthesis Approach

Directory of Open Access Journals (Sweden)

Younis Jabarzadeh

2016-03-01

Full Text Available Identifying influential factors which contribute to the knowledge management maturity and studying their interaction over time helps managers to understand the complex behavior of knowledge management system. It also leads them to make right decisions for utilizing these factors in promoting knowledge management and achieve strategic goals of the organization by providing a sound insight and an appropriate mechanism to reach to the optimal maturity level. In this study, all aspects and components of knowledge management with an emphasis on human factors and organizational culture, and relations between them have been identified by using a systematic literature review and meta-synthesis qualitative research approach. Then by using consultation and consensus of experts, all results verified. The results include 64 codes which are classified in 9 dimensions and two categories. Finally, due to the obtained classification and their relations, the dynamic model of knowledge management maturity is presented. The results of this study could be a suitable framework for improving mental models of knowledge management executives and experts. It makes possible Developing dynamic analysis models and appropriate policies in order to improve the knowledge management maturity in organizations.

mediation: R package for causal mediation analysis

OpenAIRE

Tingley, Dustin; Yamamoto, Teppei; Hirose, Kentaro; Keele, Luke; Imai, Kosuke

2012-01-01

In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting su...

Causal knowledge and the development of inductive reasoning.

Science.gov (United States)

Bright, Aimée K; Feeney, Aidan

2014-06-01

We explored the development of sensitivity to causal relations in children's inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey→predator) or diagnostic (predator→prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children's inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning. Copyright © 2013 Elsevier Inc. All rights reserved.

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...

Causal attributions in Brazilian children's reasoning about health and illness

Directory of Open Access Journals (Sweden)

Boruchovitch Evely

2000-01-01

Full Text Available INTRODUCTION: At a time when a great number of diseases can be prevented by changing one's habits and life style, investigations have focused on understanding what adults and children believe to be desirable health practices and uncovering the factors associated with successful adherence to such practices. For these, causal attributions for health and illness were investigated among 96 Brazilian elementary school students. METHODS: Ninety six subjects, aged 6 to 14, were interviewed individually and their causal attributions were assessed through 14 true-false items (e.g. people stay well [healthy] because they are lucky. The relationship between the children's causal attributions and demographic characteristics were also examined. RESULTS: Overall, the results were consistent with previous researches. "Taking care of oneself" was considered the most important cause of good health. "Viruses and germs" and "lack of self-care" were the most selected causes of illness. Analyses revealed significant relationship between subjects' causal attribution and their age, school grade level, socioeconomic status and gender. CONCLUSIONS: The study findings suggest that there may be more cross-cultural similarities than differences in children's causal attributions for health and illness. Finding ways to help individuals engage in appropriate preventive-maintenance health practices without developing an exaggerated notion that the individuals can control their own health and illness is a challenge which remains to be addressed by further research.

Preschool physics: Using the invisible property of weight in causal reasoning tasks.

Science.gov (United States)

Wang, Zhidan; Williamson, Rebecca A; Meltzoff, Andrew N

2018-01-01

Causal reasoning is an important aspect of scientific thinking. Even young human children can use causal reasoning to explain observations, make predictions, and design actions to bring about specific outcomes in the physical world. Weight is an interesting type of cause because it is an invisible property. Here, we tested preschool children with causal problem-solving tasks that assessed their understanding of weight. In an experimental setting, 2- to 5-year-old children completed three different tasks in which they had to use weight to produce physical effects-an object displacement task, a balance-scale task, and a tower-building task. The results showed that the children's understanding of how to use object weight to produce specific object-to-object causal outcomes improved as a function of age, with 4- and 5-year-olds showing above-chance performance on all three tasks. The younger children's performance was more variable. The pattern of results provides theoretical insights into which aspects of weight processing are particularly difficult for preschool children and why they find it difficult.

Probing the Cultural Constitution of Causal Cognition – A Research Program

Science.gov (United States)

Bender, Andrea; Beller, Sieghard

2016-01-01

To what extent is the way people perceive, represent, and reason about causal relationships dependent on culture? While there have been sporadic attempts to explore this question, a systematic investigation is still lacking. Here, we propose that human causal cognition is not only superficially affected by cultural background, but that it is co-constituted by the cultural nature of the human species. To this end, we take stock of on-going research, with a particular focus on the methodological approaches taken: cross-species comparisons, archeological accounts, developmental studies, cross-cultural, and cross-linguistic experiments, as well as in-depth within-culture analyses of cognitive concepts, processes, and changes over time. We argue that only a combination of these approaches will allow us to integrate different components of cognition, levels of analysis, and points of view—the key requirements for a comprehensive, interdisciplinary research program to advance this field. PMID:26941695

An improvement of the applicability of human factors guidelines for coping with human factors issues in nuclear power plants

International Nuclear Information System (INIS)

Lee, Y. H.; Lee, J. Y.

2003-01-01

Human factors have been well known as one of the key factors to the system effectiveness as well as the efficiency and safety of nuclear power plants(NPPs). Human factors engineering(HFE) are included in periodic safety review(PSR) on the existing NPPs and the formal safety assessment for the new ones. However, HFE for NPPs is still neither popular in practice nor concrete in methodology. Especially, the human factors guidelines, which are the most frequent form of human factors engineering in practice, reveal the limitations in their applications. We discuss the limitations and their casual factors found in human factors guidelines in order to lesson the workload of HFE practitioners and to improve the applicability of human factors guidelines. According to the purposes and the phases of HFE for NPPs, more selective items and specified criteria should be prepared carefully in the human factors guidelines for the each HFE applications in practice. These finding on the human factors guidelines can be transferred to the other HFE application field, such as military, aviation, telecommunication, HCI, and product safety

Human factors in nuclear power plants

International Nuclear Information System (INIS)

Swain, A.D.

1981-01-01

This report describes some of the human factors problems in nuclear power plants and the technology that can be employed to reduce those problems. Many of the changes to improve the human factors in existing plants are inexpensive, and the expected gain in human reliability is substantial. The human factors technology is well-established and there are practitioners in most countries that have nuclear power plants. (orig.) [de

How contrast situations affect the assignment of causality in symmetric physical settings

Directory of Open Access Journals (Sweden)

Sieghard eBeller

2015-01-01

Full Text Available In determining the prime cause of a physical event, people often weight one of two entities in a symmetric physical relation as more important for bringing about the causal effect than the other. In a broad survey (Bender and Beller, 2011, we documented such weighting effects for different kinds of physical events and found that their direction and strength depended on a variety of factors. Here, we focus on one of those: adding a contrast situation that—while being formally irrelevant—foregrounds one of the factors and thus frames the task in a specific way. In two experiments, we generalize and validate our previous findings by using different stimulus material (in Experiment 1, by applying a different response format to elicit causal assignments, an analogue rating scale instead of a forced-choice decision (in Experiment 2, and by eliciting explanations for the physical events in question (in both experiments. The results generally confirm the contrast effects for both response formats; however, the effects were more pronounced with the force-choice format than with the rating format. People tended to refer to the given contrast in their explanations, which validates our manipulation. Finally, people’s causal assignments are reflected in the type of explanation given in that contrast and property explanations were associated with biased causal assignments, whereas relational explanations were associated with unbiased assignments. In the discussion, we pick up the normative questions of whether or not these contrast effects constitute a bias in causal reasoning.

The selective power of causality on memory errors.

Science.gov (United States)

Marsh, Jessecae K; Kulkofsky, Sarah

2015-01-01

We tested the influence of causal links on the production of memory errors in a misinformation paradigm. Participants studied a set of statements about a person, which were presented as either individual statements or pairs of causally linked statements. Participants were then provided with causally plausible and causally implausible misinformation. We hypothesised that studying information connected with causal links would promote representing information in a more abstract manner. As such, we predicted that causal information would not provide an overall protection against memory errors, but rather would preferentially help in the rejection of misinformation that was causally implausible, given the learned causal links. In two experiments, we measured whether the causal linkage of information would be generally protective against all memory errors or only selectively protective against certain types of memory errors. Causal links helped participants reject implausible memory lures, but did not protect against plausible lures. Our results suggest that causal information may promote an abstract storage of information that helps prevent only specific types of memory errors.

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

Rate-Agnostic (Causal) Structure Learning.

Science.gov (United States)

Plis, Sergey; Danks, David; Freeman, Cynthia; Calhoun, Vince

2015-12-01

Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a "rate-agnostic" manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling.

Human factors in nuclear power plants

International Nuclear Information System (INIS)

Pack, R.W.

1978-01-01

The Electric Power Research Institute has started research in human factors in nuclear power plants. One project, completed in March 1977, reviewed human factors problems in operating power plants and produced a report evaluating those problems. A second project developed computer programs for evaluating operator performance on training simulators. A third project is developing and evaluating control-room design approaches. A fourth project is reviewing human factors problems associated with power-plant maintainability and instrumentation and control technician activities. Human factors engineering is an interdisciplinary specialty concerned with influencing the design of equipment systems, facilities, and operational environments to promote safe, efficient, and reliable operator performance. The Electric Power Research Institute (EPRI) has undertaken four projects studying the application of human factors engineering principles to nuclear power plants. (author)

Causality and analyticity in optics

International Nuclear Information System (INIS)

Nussenzveig, H.M.

In order to provide an overall picture of the broad range of optical phenomena that are directly linked with the concepts of causality and analyticity, the following topics are briefly reviewed, emphasizing recent developments: 1) Derivation of dispersion relations for the optical constants of general linear media from causality. Application to the theory of natural optical activity. 2) Derivation of sum rules for the optical constants from causality and from the short-time response function (asymptotic high-frequency behavior). Average spectral behavior of optical media. Applications. 3) Role of spectral conditions. Analytic properties of coherence functions in quantum optics. Reconstruction theorem.4) Phase retrieval problems. 5) Inverse scattering problems. 6) Solution of nonlinear evolution equations in optics by inverse scattering methods. Application to self-induced transparency. Causality in nonlinear wave propagation. 7) Analytic continuation in frequency and angular momentum. Complex singularities. Resonances and natural-mode expansions. Regge poles. 8) Wigner's causal inequality. Time delay. Spatial displacements in total reflection. 9) Analyticity in diffraction theory. Complex angular momentum theory of Mie scattering. Diffraction as a barrier tunnelling effect. Complex trajectories in optics. (Author) [pt

mediation: R Package for Causal Mediation Analysis

Directory of Open Access Journals (Sweden)

Dustin Tingley

2014-09-01

Full Text Available In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.

Causal asymmetry across cultures: Assigning causal roles in symmetric physical settings

Directory of Open Access Journals (Sweden)

Andrea eBender

2011-09-01

Full Text Available In the cognitive sciences, causal cognition in the physical domain has featured as a core research topic, but the impact of culture has been rarely ever explored. One case in point for a topic on which this neglect is pronounced is the pervasive tendency of people to consider one of two (equally important entities as more important for bringing about an effect. In order to scrutinize how robust such tendencies are across cultures, we asked German and Tongan participants to assign prime causality in nine symmetric settings. For most settings, strong asymmetries in both cultures were found, but not always in the same direction, depending on the task content. This indicates that causal asymmetries, while indeed being a robust phenomenon across cultures, are also subject to culture-specific concepts. Moreover, the asymmetries were found to be modulated by figure-ground relations, but not by marking agency.

Entanglement, non-Markovianity, and causal non-separability

Science.gov (United States)

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.

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...

The gut microbiota and obesity: from correlation to causality.

Science.gov (United States)

Zhao, Liping

2013-09-01

The gut microbiota has been linked with chronic diseases such as obesity in humans. However, the demonstration of causality between constituents of the microbiota and specific diseases remains an important challenge in the field. In this Opinion article, using Koch's postulates as a conceptual framework, I explore the chain of causation from alterations in the gut microbiota, particularly of the endotoxin-producing members, to the development of obesity in both rodents and humans. I then propose a strategy for identifying the causative agents of obesity in the human microbiota through a combination of microbiome-wide association studies, mechanistic analysis of host responses and the reproduction of diseases in gnotobiotic animals.

Conditional Granger Causality of Diffusion Processes

Czech Academy of Sciences Publication Activity Database

Wahl, B.; Feudel, U.; Hlinka, Jaroslav; Wächter, M.; Peinke, J.; Freund, J.A.

2017-01-01

Roč. 90, č. 10 (2017), č. článku 197. ISSN 1434-6028 R&D Projects: GA ČR GA13-23940S; GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Granger causality * stochastic process * diffusion process * nonlinear dynamical systems Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 1.461, year: 2016

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.

Space-time as a causal set

International Nuclear Information System (INIS)

Bombelli, L.; Lee, J.; Meyer, D.; Sorkin, R.D.

1987-01-01

We propose that space-time at the smallest scales is in reality a causal set: a locally finite set of elements endowed with a partial order corresponding to the macroscopic relation that defines past and future. We explore how a Lorentzian manifold can approximate a causal set, noting in particular that the thereby defined effective dimensionality of a given causal set can vary with length scale. Finally, we speculate briefly on the quantum dynamics of causal sets, indicating why an appropriate choice of action can reproduce general relativity in the classical limit

Epidemiological features and risk factors associated with the spatial and temporal distribution of human brucellosis in China

Science.gov (United States)

2013-01-01

Background Human brucellosis incidence in China has been increasing dramatically since 1999. However, epidemiological features and potential factors underlying the re-emergence of the disease remain less understood. Methods Data on human and animal brucellosis cases at the county scale were collected for the year 2004 to 2010. Also collected were environmental and socioeconomic variables. Epidemiological features including spatial and temporal patterns of the disease were characterized, and the potential factors related to the spatial heterogeneity and the temporal trend of were analysed using Poisson regression analysis, Granger causality analysis, and autoregressive distributed lag (ADL) models, respectively. Results The epidemic showed a significantly higher spatial correlation with the number of sheep and goats than swine and cattle. The disease was most prevalent in grassland areas with elevation between 800–1,600 meters. The ADL models revealed that local epidemics were correlated with comparatively lower temperatures and less sunshine in winter and spring, with a 1–7 month lag before the epidemic peak in May. Conclusions Our findings indicate that human brucellosis tended to occur most commonly in grasslands at moderate elevation where sheep and goats were the predominant livestock, and in years with cooler winter and spring or less sunshine. PMID:24238301

Is the Association Between Education and Fertility Postponement Causal? The Role of Family Background Factors.

Science.gov (United States)

Tropf, Felix C; Mandemakers, Jornt J

2017-02-01

A large body of literature has demonstrated a positive relationship between education and age at first birth. However, this relationship may be partly spurious because of family background factors that cannot be controlled for in most research designs. We investigate the extent to which education is causally related to later age at first birth in a large sample of female twins from the United Kingdom (N = 2,752). We present novel estimates using within-identical twin and biometric models. Our findings show that one year of additional schooling is associated with about one-half year later age at first birth in ordinary least squares (OLS) models. This estimate reduced to only a 1.5-month later age at first birth for the within-identical twin model controlling for all shared family background factors (genetic and family environmental). Biometric analyses reveal that it is mainly influences of the family environment-not genetic factors-that cause spurious associations between education and age at first birth. Last, using data from the Office for National Statistics, we demonstrate that only 1.9 months of the 2.74 years of fertility postponement for birth cohorts 1944-1967 could be attributed to educational expansion based on these estimates. We conclude that the rise in educational attainment alone cannot explain differences in fertility timing between cohorts.

Representing Personal Determinants in Causal Structures.

Science.gov (United States)

Bandura, Albert

1984-01-01

Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…

[Causal analysis approaches in epidemiology].

Science.gov (United States)

Dumas, O; Siroux, V; Le Moual, N; Varraso, R

2014-02-01

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the

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.

Repeated Causal Decision Making

Science.gov (United States)

Hagmayer, York; Meder, Bjorn

2013-01-01

Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in…

Causality in Classical Electrodynamics

Science.gov (United States)

Savage, Craig

2012-01-01

Causality in electrodynamics is a subject of some confusion, especially regarding the application of Faraday's law and the Ampere-Maxwell law. This has led to the suggestion that we should not teach students that electric and magnetic fields can cause each other, but rather focus on charges and currents as the causal agents. In this paper I argue…

Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series

Czech Academy of Sciences Publication Activity Database

Krakovská, A.; Jakubík, J.; Chvosteková, M.; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-01-01

Roč. 97, č. 4 (2018), č. článku 042207. ISSN 2470-0045 R&D Projects: GA MZd(CZ) NV15-33250A Institutional support: RVO:67985807 Keywords : comparative study * causality detection * bivariate models * Granger causality * transfer entropy * convergent cross mappings Impact factor: 2.366, year: 2016 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207

Specifications for human factors guiding documents

Energy Technology Data Exchange (ETDEWEB)

Rhodes, W; Szlapetis, I; MacGregor, C [Rhodes and Associates Inc., Toronto, ON (Canada)

1995-04-01

This report specifies the content, function and appearance of three proposed human factors guiding documents to be used by the Atomic Energy Control board and its licensees. These three guiding documents, to be developed at a later date, are: (a) Human Factors Process Guide; (b) Human Factors Activities Guide; and (c) Human Factors Design Integration Guide. The specifications were developed by examining the best documents as identified in a previous contract with the AECB (Review of Human Factors Guidelines and Methods by W. Rhodes, I. Szlapetis et al. 1992), and a brief literature review. The best features and content were selected from existing documents and used to develop specifications for the guiding documents. The developer of the actual guides would use these specifications to produce comprehensive and consolidated documents at a later date. (author). 128 ref., 7 figs.

Specifications for human factors guiding documents

International Nuclear Information System (INIS)

Rhodes, W.; Szlapetis, I.; MacGregor, C.

1995-04-01

This report specifies the content, function and appearance of three proposed human factors guiding documents to be used by the Atomic Energy Control board and its licensees. These three guiding documents, to be developed at a later date, are: (a) Human Factors Process Guide; (b) Human Factors Activities Guide; and (c) Human Factors Design Integration Guide. The specifications were developed by examining the best documents as identified in a previous contract with the AECB (Review of Human Factors Guidelines and Methods by W. Rhodes, I. Szlapetis et al. 1992), and a brief literature review. The best features and content were selected from existing documents and used to develop specifications for the guiding documents. The developer of the actual guides would use these specifications to produce comprehensive and consolidated documents at a later date. (author). 128 ref., 7 figs

The contributions of human factors on human error in Malaysia aviation maintenance industries

Science.gov (United States)

Padil, H.; Said, M. N.; Azizan, A.

2018-05-01

Aviation maintenance is a multitasking activity in which individuals perform varied tasks under constant pressure to meet deadlines as well as challenging work conditions. These situational characteristics combined with human factors can lead to various types of human related errors. The primary objective of this research is to develop a structural relationship model that incorporates human factors, organizational factors, and their impact on human errors in aviation maintenance. Towards that end, a questionnaire was developed which was administered to Malaysian aviation maintenance professionals. Structural Equation Modelling (SEM) approach was used in this study utilizing AMOS software. Results showed that there were a significant relationship of human factors on human errors and were tested in the model. Human factors had a partial effect on organizational factors while organizational factors had a direct and positive impact on human errors. It was also revealed that organizational factors contributed to human errors when coupled with human factors construct. This study has contributed to the advancement of knowledge on human factors effecting safety and has provided guidelines for improving human factors performance relating to aviation maintenance activities and could be used as a reference for improving safety performance in the Malaysian aviation maintenance companies.

Kant on causal laws and powers.

Science.gov (United States)

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.

[A study of relation between hopelessness and causal attribution in school-aged children].

Science.gov (United States)

Sakurai, S

1989-12-01

This study was conducted to investigate the relation between hopelessness and causal attribution in Japanese school-aged children. In Study 1, the Japanese edition of hopelessness scale for children developed by Kazdin, French, Unis, Esveldt-Dawsan, and Sherick (1983) was constructed. Seventeen original items were translated into Japanese and they were administrated to 405 fifth- and sixth-graders. All of the items could be included to the Japanese edition of hopelessness scale. The reliability and validity was examined. In Study 2, the relation between hopelessness and causal attribution in children were investigated. The causal attribution questionnaire developed by Higuchi, Kambare, and Otsuka (1983) and the hopelessness scale developed by Study 1 were administered to 188 sixth-graders. Children with high scores in hopelessness scale significantly attributed negative events to much more effort factor than children with low scores. It supports neither the reformulated learned helplessness model nor the causal attribution theory of achievement motivation. It was explained mainly from points of self-serving attribution, cultural difference, and social desirability. Some questions were discussed for developing studies on depression and causal attribution in Japan.

A panel Granger-causality test of endogenous vs. exogenous growth

OpenAIRE

Jochen Hartwig

2009-01-01

The paper proposes a new test of endogenous vs. exogenous growth theories based on the Granger-causality methodology and applies it to a panel of 20 OECD countries. The test yields divergent evidence with respect to physical and human capital. For physical capital, the test results favor Solow-type exogenous growth theory over AK-type endogenous growth models. On the other hand, the test results lend support to human capital oriented endogenous growth models - like the Uzawa-Lucas model - rat...

Human Factors in Cabin Accident Investigations

Science.gov (United States)

Chute, Rebecca D.; Rosekind, Mark R. (Technical Monitor)

1996-01-01

Human factors has become an integral part of the accident investigation protocol. However, much of the investigative process remains focussed on the flight deck, airframe, and power plant systems. As a consequence, little data has been collected regarding the human factors issues within and involving the cabin during an accident. Therefore, the possibility exists that contributing factors that lie within that domain may be overlooked. The FAA Office of Accident Investigation is sponsoring a two-day workshop on cabin safety accident investigation. This course, within the workshop, will be of two hours duration and will explore relevant areas of human factors research. Specifically, the three areas of discussion are: Information transfer and resource management, fatigue and other physical stressors, and the human/machine interface. Integration of these areas will be accomplished by providing a suggested checklist of specific cabin-related human factors questions for investigators to probe following an accident.

Implementing human factors in clinical practice

Science.gov (United States)

Timmons, Stephen; Baxendale, Bryn; Buttery, Andrew; Miles, Giulia; Roe, Bridget; Browes, Simon

2015-01-01

Objectives To understand whether aviation-derived human factors training is acceptable and useful to healthcare professionals. To understand whether and how healthcare professionals have been able to implement human factors approaches to patient safety in their own area of clinical practice. Methods Qualitative, longitudinal study using semi-structured interviews and focus groups, of a multiprofessional group of UK NHS staff (from the emergency department and operating theatres) who have received aviation-derived human factors training. Results The human factors training was evaluated positively, and thought to be both acceptable and relevant to practice. However, the staff found it harder to implement what they had learned in their own clinical areas, and this was principally attributed to features of the informal organisational cultures. Conclusions In order to successfully apply human factors approaches in hospital, careful consideration needs to be given to the local context and informal culture of clinical practice. PMID:24631959

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.

Effectiveness of human factors simulator

International Nuclear Information System (INIS)

Moragas, F.

2015-01-01

En 2011, ANAV started the exploitation of the Human Factors Simulator installed in TECNATOM Training Center located in L'Hospital de L'Infant Tarragona. AVAN's Strategic Plan includes the Action Plan for the improvement of human behavior. The plan includes improving the efficiency of the efficiency of the human factors simulator. It is proposed to improve the efficiency into two different terms: winning effectiveness in modeling behaviors, and interweaving the activities in the simulator with the actual strategy of promoting Safety culture and human behaviour. (Author)

Therapists' causal attributions of clients' problems and selection of intervention strategies.

Science.gov (United States)

Royce, W S; Muehlke, C V

1991-04-01

Therapists' choices of intervention strategies are influenced by many factors, including judgments about the bases of clients' problems. To assess the relationships between such causal attributions and the selection of intervention strategies, 196 counselors, psychologists, and social workers responded to the written transcript of a client's interview by answering two questionnaires, a 1982 scale (Causal Dimension Scale by Russell) which measured causal attribution of the client's problem, and another which measured preference for emotional, rational, and active intervention strategies in dealing with the client, based on the 1979 E-R-A taxonomy of Frey and Raming. A significant relationship was found between the two sets of variables, with internal attributions linked to rational intervention strategies and stable attributions linked to active strategies. The results support Halleck's 1978 hypothesis that theories of psychotherapy tie interventions to etiological considerations.

Functional equations with causal operators

CERN Document Server

Corduneanu, C

2003-01-01

Functional equations encompass most of the equations used in applied science and engineering: ordinary differential equations, integral equations of the Volterra type, equations with delayed argument, and integro-differential equations of the Volterra type. The basic theory of functional equations includes functional differential equations with causal operators. Functional Equations with Causal Operators explains the connection between equations with causal operators and the classical types of functional equations encountered by mathematicians and engineers. It details the fundamentals of linear equations and stability theory and provides several applications and examples.

Causal inference in economics and marketing.

Science.gov (United States)

Varian, Hal R

2016-07-05

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

Human Health/Human Factors Considerations in Trans-Lunar Space

Science.gov (United States)

Moore, E. Cherice; Howard, Robert; Mendeck, Gavin

2014-01-01

The human factors insights of how they are incorporated into the vehicle are crucial towards designing and planning the internal designs necessary for future spacecraft and missions. The adjusted mission concept of supporting the Asteroid Redirect Crewed Mission will drive some human factors changes on how the Orion will be used and will be reassessed so as to best contribute to missions success. Recognizing what the human factors and health functional needs are early in the design process and how to integrate them will improve this and future generations of space vehicles to achieve mission success and continue to minimize risks.

Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis

International Nuclear Information System (INIS)

Jahangir Alam, Mohammad; Ara Begum, Ismat; Buysse, Jeroen; Van Huylenbroeck, Guido

2012-01-01

The paper investigates the possible existence of dynamic causality between energy consumption, electricity consumption, carbon emissions and economic growth in Bangladesh. First, we have tested cointegration relationships using the Johansen bi-variate cointegration model. This is complemented with an analysis of an auto-regressive distributed lag model to examine the results' robustness. Then, the Granger short-run, the long-run and strong causality are tested with a vector error correction modelling framework. The results indicate that uni-directional causality exists from energy consumption to economic growth both in the short and the long-run while a bi-directional long-run causality exists between electricity consumption and economic growth but no causal relationship exists in short-run. The strong causality results indicate bi-directional causality for both the cases. A uni-directional causality runs from energy consumption to CO 2 emission for the short-run but feedback causality exists in the long-run. CO 2 Granger causes economic growth both in the short and in the long-run. An important policy implication is that energy (electricity as well) can be considered as an important factor for the economic growth in Bangladesh. Moreover, as higher energy consumption also means higher pollution in the long-run, policy makers should stimulate alternative energy sources for meeting up the increasing energy demand. - Highlights: ► Dynamic causality among energy and electricity consumption, CO 2 and economic growth. ► Uni-directional causality exists from energy consumption to economic growth. ► Bi-directional causality exists between electricity consumption and economic growth. ► Feedback causality exists between CO 2 emission to energy consumption. ► CO 2 Granger causes economic growth both in the short and in the long-run.

Malthusian factors as proximal drivers of human population crisis at Sub-Saharan Africa

Directory of Open Access Journals (Sweden)

Mauricio eLima

2015-11-01

Full Text Available There is a growing interest and concern for understanding the interaction among human population growth and the sustainability of natural resources. In fact, many agrarian societies experienced an increasing frequency of wars, famines and epidemics during the periods of resource depletion. People from Sub-Saharan Africa (SSA have suffered the demographic consequences of famines, civil wars and political instabilities during the last fifty years.. Almost half of the countries of Sub-Saharan Africa have undergone some form of demographic crisis over the past fifty years. Our analysis indicate that despite that environmental conditions were positively correlated with crop production across SSA, Malthusian factors correlated inversely with cultivation intensity, which in turn translated into a higher magnitude of depopulation suffered during the past fifty years. In this paper, we provide empirical evidence that population collapses in SSA during the last fifty years have been multifactorial, although more closely associated with Malthusian factors as proximal drivers. Other proximal drivers such as economic indicators, political stability and environmental determinants did not explain as much variance as Malthusian forces, suggesting that explanations of collapse magnitude in SSA are embedded in a complex multi-causal chain, in which demographic factors may play a modulating role yet to be explored in more depth.

Mimesis: Linking Postmodern Theory to Human Behavior

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Dybicz, Phillip

2010-01-01

This article elaborates mimesis as a theory of causality used to explain human behavior. Drawing parallels to social constructionism's critique of positivism and naturalism, mimesis is offered as a theory of causality explaining human behavior that contests the current dominance of Newton's theory of causality as cause and effect. The contestation…

Interactions of information transfer along separable causal paths

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Jiang, Peishi; Kumar, Praveen

2018-04-01

Complex systems arise as a result of interdependences between multiple variables, whose causal interactions can be visualized in a time-series graph. Transfer entropy and information partitioning approaches have been used to characterize such dependences. However, these approaches capture net information transfer occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within a subgraph of interest through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [Phys. Rev. E 92, 062829 (2015), 10.1103/PhysRevE.92.062829] to develop a framework for quantifying information partitioning along separable causal paths. Momentary information transfer along causal paths captures the amount of information transfer between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique, and redundant information transfer through separable causal paths. Through a graphical model, we analyze the impact of the separable and nonseparable causal paths and the causality structure embedded in the graph as well as the noise effect on information partitioning by using synthetic data generated from two coupled logistic equation models. Our approach can provide a valuable reference for an autonomous information partitioning along separable causal paths which form a causal subgraph influencing a target.

Making sense of (exceptional) causal relations. A cross-cultural and cross-linguistic study.

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Le Guen, Olivier; Samland, Jana; Friedrich, Thomas; Hanus, Daniel; Brown, Penelope

2015-01-01

In order to make sense of the world, humans tend to see causation almost everywhere. Although most causal relations may seem straightforward, they are not always construed in the same way cross-culturally. In this study, we investigate concepts of "chance," "coincidence," or "randomness" that refer to assumed relations between intention, action, and outcome in situations, and we ask how people from different cultures make sense of such non-law-like connections. Based on a framework proposed by Alicke (2000), we administered a task that aims to be a neutral tool for investigating causal construals cross-culturally and cross-linguistically. Members of four different cultural groups, rural Mayan Yucatec and Tseltal speakers from Mexico and urban students from Mexico and Germany, were presented with a set of scenarios involving various types of causal and non-causal relations and were asked to explain the described events. Three links varied as to whether they were present or not in the scenarios: Intention-to-Action, Action-to-Outcome, and Intention-to-Outcome. Our results show that causality is recognized in all four cultural groups. However, how causality and especially non-law-like relations are interpreted depends on the type of links, the cultural background and the language used. In all three groups, Action-to-Outcome is the decisive link for recognizing causality. Despite the fact that the two Mayan groups share similar cultural backgrounds, they display different ideologies regarding concepts of non-law-like relations. The data suggests that the concept of "chance" is not universal, but seems to be an explanation that only some cultural groups draw on to make sense of specific situations. Of particular importance is the existence of linguistic concepts in each language that trigger ideas of causality in the responses from each cultural group.

Making sense of (exceptional) causal relations. A cross-cultural and cross-linguistic study

Science.gov (United States)

Le Guen, Olivier; Samland, Jana; Friedrich, Thomas; Hanus, Daniel; Brown, Penelope

2015-01-01

In order to make sense of the world, humans tend to see causation almost everywhere. Although most causal relations may seem straightforward, they are not always construed in the same way cross-culturally. In this study, we investigate concepts of “chance,” “coincidence,” or “randomness” that refer to assumed relations between intention, action, and outcome in situations, and we ask how people from different cultures make sense of such non-law-like connections. Based on a framework proposed by Alicke (2000), we administered a task that aims to be a neutral tool for investigating causal construals cross-culturally and cross-linguistically. Members of four different cultural groups, rural Mayan Yucatec and Tseltal speakers from Mexico and urban students from Mexico and Germany, were presented with a set of scenarios involving various types of causal and non-causal relations and were asked to explain the described events. Three links varied as to whether they were present or not in the scenarios: Intention-to-Action, Action-to-Outcome, and Intention-to-Outcome. Our results show that causality is recognized in all four cultural groups. However, how causality and especially non-law-like relations are interpreted depends on the type of links, the cultural background and the language used. In all three groups, Action-to-Outcome is the decisive link for recognizing causality. Despite the fact that the two Mayan groups share similar cultural backgrounds, they display different ideologies regarding concepts of non-law-like relations. The data suggests that the concept of “chance” is not universal, but seems to be an explanation that only some cultural groups draw on to make sense of specific situations. Of particular importance is the existence of linguistic concepts in each language that trigger ideas of causality in the responses from each cultural group. PMID:26579028

Does Causality Matter More Now? Increase in the Proportion of Causal Language in English Texts.

Science.gov (United States)

Iliev, Rumen; Axelrod, Robert

2016-05-01

The vast majority of the work on culture and cognition has focused on cross-cultural comparisons, largely ignoring the dynamic aspects of culture. In this article, we provide a diachronic analysis of causal cognition over time. We hypothesized that the increased role of education, science, and technology in Western societies should be accompanied by greater attention to causal connections. To test this hypothesis, we compared word frequencies in English texts from different time periods and found an increase in the use of causal language of about 40% over the past two centuries. The observed increase was not attributable to general language effects or to changing semantics of causal words. We also found that there was a consistent difference between the 19th and the 20th centuries, and that the increase happened mainly in the 20th century. © The Author(s) 2016.

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

Causal inference, probability theory, and graphical insights.

Science.gov (United States)

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.

Causality as a Rigorous Notion and Quantitative Causality Analysis with Time Series

Science.gov (United States)

Liang, X. S.

2017-12-01

Given two time series, can one faithfully tell, in a rigorous and quantitative way, the cause and effect between them? Here we show that this important and challenging question (one of the major challenges in the science of big data), which is of interest in a wide variety of disciplines, has a positive answer. Particularly, for linear systems, the maximal likelihood estimator of the causality from a series X2 to another series X1, written T2→1, turns out to be concise in form: T2→1 = [C11 C12 C2,d1 — C112 C1,d1] / [C112 C22 — C11C122] where Cij (i,j=1,2) is the sample covariance between Xi and Xj, and Ci,dj the covariance between Xi and ΔXj/Δt, the difference approximation of dXj/dt using the Euler forward scheme. An immediate corollary is that causation implies correlation, but not vice versa, resolving the long-standing debate over causation versus correlation. The above formula has been validated with touchstone series purportedly generated with one-way causality that evades the classical approaches such as Granger causality test and transfer entropy analysis. It has also been applied successfully to the investigation of many real problems. Through a simple analysis with the stock series of IBM and GE, an unusually strong one-way causality is identified from the former to the latter in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a "Giant" for the computer market. Another example presented here regards the cause-effect relation between the two climate modes, El Niño and Indian Ocean Dipole (IOD). In general, these modes are mutually causal, but the causality is asymmetric. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean. In the third example, an unambiguous one-way causality is found between CO2 and the global mean temperature anomaly. While it is confirmed that CO2 indeed drives the recent global warming

K-causality and degenerate spacetimes

Science.gov (United States)

Dowker, H. F.; Garcia, R. S.; Surya, S.

2000-11-01

The causal relation K+ was introduced by Sorkin and Woolgar to extend the standard causal analysis of C2 spacetimes to those that are only C0. Most of their results also hold true in the case of metrics with degeneracies which are C0 but vanish at isolated points. In this paper we seek to examine K+ explicitly in the case of topology-changing `Morse histories' which contain degeneracies. We first demonstrate some interesting features of this relation in globally Lorentzian spacetimes. In particular, we show that K+ is robust and the Hawking and Sachs characterization of causal continuity translates into a natural condition in terms of K+. We then examine K+ in topology-changing Morse spacetimes with the degenerate points excised and then for the Morse histories in which the degenerate points are reinstated. We find further characterizations of causal continuity in these cases.

Causal inference in econometrics

CERN Document Server

Kreinovich, Vladik; Sriboonchitta, Songsak

2016-01-01

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Causal localizations in relativistic quantum mechanics

Science.gov (United States)

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.

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

Science.gov (United States)

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

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

Hierarchical organisation of causal graphs

International Nuclear Information System (INIS)

Dziopa, P.

1993-01-01

This paper deals with the design of a supervision system using a hierarchy of models formed by graphs, in which the variables are the nodes and the causal relations between the variables of the arcs. To obtain a representation of the variables evolutions which contains only the relevant features of their real evolutions, the causal relations are completed with qualitative transfer functions (QTFs) which produce roughly the behaviour of the classical transfer functions. Major improvements have been made in the building of the hierarchical organization. First, the basic variables of the uppermost level and the causal relations between them are chosen. The next graph is built by adding intermediary variables to the upper graph. When the undermost graph has been built, the transfer functions parameters corresponding to its causal relations are identified. The second task consists in the upwelling of the information from the undermost graph to the uppermost one. A fusion procedure of the causal relations has been designed to compute the QFTs relevant for each level. This procedure aims to reduce the number of parameters needed to represent an evolution at a high level of abstraction. These techniques have been applied to the hierarchical modelling of nuclear process. (authors). 8 refs., 12 figs

Dynamics and causality constraints

International Nuclear Information System (INIS)

Sousa, Manoelito M. de

2001-04-01

The physical meaning and the geometrical interpretation of causality implementation in classical field theories are discussed. Causality in field theory are kinematical constraints dynamically implemented via solutions of the field equation, but in a limit of zero-distance from the field sources part of these constraints carries a dynamical content that explains old problems of classical electrodynamics away with deep implications to the nature of physicals interactions. (author)

Causal inference regarding infectious aetiology of chronic conditions: a systematic review.

Directory of Open Access Journals (Sweden)

Sofia Orrskog

Full Text Available BACKGROUND: The global burden of disease has shifted from communicable diseases in children to chronic diseases in adults. This epidemiologic shift varies greatly by region, but in Europe, chronic conditions account for 86% of all deaths, 77% of the disease burden, and up to 80% of health care expenditures. A number of risk factors have been implicated in chronic diseases, such as exposure to infectious agents. A number of associations have been well established while others remain uncertain. METHODS AND FINDINGS: We assessed the body of evidence regarding the infectious aetiology of chronic diseases in the peer-reviewed literature over the last decade. Causality was assessed with three different criteria: First, the total number of associations documented in the literature between each infectious agent and chronic condition; second, the epidemiologic study design (quality of the study; third, evidence for the number of Hill's criteria and Koch's postulates that linked the pathogen with the chronic condition. We identified 3136 publications, of which 148 were included in the analysis. There were a total of 75 different infectious agents and 122 chronic conditions. The evidence was strong for five pathogens, based on study type, strength and number of associations; they accounted for 60% of the associations documented in the literature. They were human immunodeficiency virus, hepatitis C virus, Helicobacter pylori, hepatitis B virus, and Chlamydia pneumoniae and were collectively implicated in the aetiology of 37 different chronic conditions. Other pathogens examined were only associated with very few chronic conditions (≤ 3 and when applying the three different criteria of evidence the strength of the causality was weak. CONCLUSIONS: Prevention and treatment of these five pathogens lend themselves as effective public health intervention entry points. By concentrating research efforts on these promising areas, the human, economic, and societal

Energy Consumption and Economic Growth in Algeria: Cointegration and Causality Analysis

Directory of Open Access Journals (Sweden)

Cherfi Souhila

2012-01-01

Full Text Available This study investigates the energy consumption-growth nexus in Algeria. The causal relationship between the logarithm of per capita energy consumption (LPCEC and the logarithm of per capita GDP (LPCGDP during the 1965-2008 period is examined using the threshold cointegration and Granger causality tests. The estimation results indicate that the LPCEC and LPCGDP for Algeria are non cointegrated and that there is a uni-directional causality running from LPCGDP to LPCEC, but not vice versa. The research results strongly support the neoclassical perspective that energy consumption is not a limiting factor to economic growth in Algeria. Accordingly, an important policy implication resulting from this analysis is that government can pursue the conservation energy policies that aim at curtailing energy use for environmental friendly development purposes without creating severe effects on economic growth. The energy should be efficiently allocated into more productive sectors of the economy.

Causal knowledge and reasoning in decision making

NARCIS (Netherlands)

Hagmayer, Y.; Witteman, C.L.M.

2017-01-01

Normative causal decision theories argue that people should use their causal knowledge in decision making. Based on these ideas, we argue that causal knowledge and reasoning may support and thereby potentially improve decision making based on expected outcomes, narratives, and even cues. We will

Expert Causal Reasoning and Explanation.

Science.gov (United States)

Kuipers, Benjamin

The relationship between cognitive psychologists and researchers in artificial intelligence carries substantial benefits for both. An ongoing investigation in causal reasoning in medical problem solving systems illustrates this interaction. This paper traces a dialectic of sorts in which three different types of causal resaoning for medical…

Human Factors Laboratory

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Federal Laboratory Consortium — Purpose: The purpose of the Human Factors Laboratory is to further the understanding of highway user needs so that those needs can be incorporated in roadway design,...

Links between causal effects and causal association for surrogacy evaluation in a gaussian setting.

Science.gov (United States)

Conlon, Anna; Taylor, Jeremy; Li, Yun; Diaz-Ordaz, Karla; Elliott, Michael

2017-11-30

Two paradigms for the evaluation of surrogate markers in randomized clinical trials have been proposed: the causal effects paradigm and the causal association paradigm. Each of these paradigms rely on assumptions that must be made to proceed with estimation and to validate a candidate surrogate marker (S) for the true outcome of interest (T). We consider the setting in which S and T are Gaussian and are generated from structural models that include an unobserved confounder. Under the assumed structural models, we relate the quantities used to evaluate surrogacy within both the causal effects and causal association frameworks. We review some of the common assumptions made to aid in estimating these quantities and show that assumptions made within one framework can imply strong assumptions within the alternative framework. We demonstrate that there is a similarity, but not exact correspondence between the quantities used to evaluate surrogacy within each framework, and show that the conditions for identifiability of the surrogacy parameters are different from the conditions, which lead to a correspondence of these quantities. Copyright © 2017 John Wiley & Sons, Ltd.

Non-Gaussian Methods for Causal Structure Learning.

Science.gov (United States)

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.

Implementing human factors in clinical practice.

Science.gov (United States)

Timmons, Stephen; Baxendale, Bryn; Buttery, Andrew; Miles, Giulia; Roe, Bridget; Browes, Simon

2015-05-01

To understand whether aviation-derived human factors training is acceptable and useful to healthcare professionals. To understand whether and how healthcare professionals have been able to implement human factors approaches to patient safety in their own area of clinical practice. Qualitative, longitudinal study using semi-structured interviews and focus groups, of a multiprofessional group of UK NHS staff (from the emergency department and operating theatres) who have received aviation-derived human factors training. The human factors training was evaluated positively, and thought to be both acceptable and relevant to practice. However, the staff found it harder to implement what they had learned in their own clinical areas, and this was principally attributed to features of the informal organisational cultures. In order to successfully apply human factors approaches in hospital, careful consideration needs to be given to the local context and informal culture of clinical practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

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

OpenAIRE

Hagmayer, York; Engelmann, Neele

2014-01-01

Cognitive psychological research focuses 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 lite...

Causal Relation Analysis Tool of the Case Study in the Engineer Ethics Education

Science.gov (United States)

Suzuki, Yoshio; Morita, Keisuke; Yasui, Mitsukuni; Tanada, Ichirou; Fujiki, Hiroyuki; Aoyagi, Manabu

In engineering ethics education, the virtual experiencing of dilemmas is essential. Learning through the case study method is a particularly effective means. Many case studies are, however, difficult to deal with because they often include many complex causal relationships and social factors. It would thus be convenient if there were a tool that could analyze the factors of a case example and organize them into a hierarchical structure to get a better understanding of the whole picture. The tool that was developed applies a cause-and-effect matrix and simple graph theory. It analyzes the causal relationship between facts in a hierarchical structure and organizes complex phenomena. The effectiveness of this tool is shown by presenting an actual example.

Human factors in nuclear power plant operations

International Nuclear Information System (INIS)

Swain, A.D.

1980-08-01

This report describes some of the human factors problems in nuclear power plants and the technology that can be employed to reduce those problems. Many of the changes to improve the human factors in existing plants are inexpensive, and the expected gain in human reliability is substantial. The human factors technology is well-established and there are practitioners in most countries that have nuclear power plants

Covariation in Natural Causal Induction.

Science.gov (United States)

Cheng, Patricia W.; Novick, Laura R.

1991-01-01

Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)

Supporting inquiry learning by promoting normative understanding of multivariable causality

Science.gov (United States)

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.

Human factors methods in DOE nuclear facilities

International Nuclear Information System (INIS)

Bennett, C.T.; Banks, W.W.; Waters, R.J.

1993-01-01

The US Department of Energy (DOE) is in the process of developing a series of guidelines for the use of human factors standards, procedures, and methods to be used in nuclear facilities. This paper discusses the philosophy and process being used to develop a DOE human factors methods handbook to be used during the design cycle. The following sections will discuss: (1) basic justification for the project; (2) human factors design objectives and goals; and (3) role of human factors engineering (HFE) in the design cycle

Exploring Individual Differences in Preschoolers' Causal Stance

Science.gov (United States)

Alvarez, Aubry; Booth, Amy E.

2016-01-01

Preschoolers, as a group, are highly attuned to causality, and this attunement is known to facilitate memory, learning, and problem solving. However, recent work reveals substantial individual variability in the strength of children's "causal stance," as demonstrated by their curiosity about and preference for new causal information. In…

Integrating human factors into process hazard analysis

International Nuclear Information System (INIS)

Kariuki, S.G.; Loewe, K.

2007-01-01

A comprehensive process hazard analysis (PHA) needs to address human factors. This paper describes an approach that systematically identifies human error in process design and the human factors that influence its production and propagation. It is deductive in nature and therefore considers human error as a top event. The combinations of different factors that may lead to this top event are analysed. It is qualitative in nature and is used in combination with other PHA methods. The method has an advantage because it does not look at the operator error as the sole contributor to the human failure within a system but a combination of all underlying factors

Model-based human reliability analysis: prospects and requirements

International Nuclear Information System (INIS)

Mosleh, A.; Chang, Y.H.

2004-01-01

Major limitations of the conventional methods for human reliability analysis (HRA), particularly those developed for operator response analysis in probabilistic safety assessments (PSA) of nuclear power plants, are summarized as a motivation for the need and a basis for developing requirements for the next generation HRA methods. It is argued that a model-based approach that provides explicit cognitive causal links between operator behaviors and directly or indirectly measurable causal factors should be at the core of the advanced methods. An example of such causal model is briefly reviewed, where due to the model complexity and input requirements can only be currently implemented in a dynamic PSA environment. The computer simulation code developed for this purpose is also described briefly, together with current limitations in the models, data, and the computer implementation

Draft revision of human factors guideline HF-010

International Nuclear Information System (INIS)

Lee, Hyun Chul; Lee, Yong Hee; Oh, In Seok; Lee, Jung Woon; Cha, Woo Chang; Lee, Dhong Ha

2003-05-01

The Application of Human Factors to the design of Man-Machine Interfaces System(MMIS) in the nuclear power plant is essential to the safety and productivity of the nuclear power plants, human factors standards and guidelines as well as human factors analysis methods and experiments are weightily used to the design application. A Korean engineering company has developed a human factors engineering guideline, so-call HF-010, and has used it for human factors design, however the revision of HF-010 is necessary owing to lack of the contents related to the advanced MMI(Man-Machine Interfaces). As the results of the reviews of HF-010, it is found out that the revision of Section 9. Computer Displays of HF-010 is urgent, thus the revision was drafted on the basis of integrated human factors design guidelines for VDT, human factors design guidelines for PMAS SPADES display, human factors design guidelines for PMAS alarm display, and human factors design guidelines for electronic displays developed by the surveillance and operation support project of KOICS. The draft revision of HF-010 Section 9 proposed in this report can be utilized for the human factors design of the advanced MMI, and the high practical usability of the draft can be kept up through the continuous revision according to the advancement of digital technology

Introduction to human factors

International Nuclear Information System (INIS)

Winters, J.M.

1988-03-01

Some background is given on the field of human factors. The nature of problems with current human/computer interfaces is discussed, some costs are identified, ideal attributes of graceful system interfaces are outlined, and some reasons are indicated why it's not easy to fix the problems

Human factors influencing decision making

OpenAIRE

Jacobs, Patricia A.

1998-01-01

This report supplies references and comments on literature that identifies human factors influencing decision making, particularly military decision making. The literature has been classified as follows (the classes are not mutually exclusive): features of human information processing; decision making models which are not mathematical models but rather are descriptive; non- personality factors influencing decision making; national characteristics influencing decision makin...

Maternal age at first birth and offspring criminality: using the children of twins design to test causal hypotheses.

Science.gov (United States)

Coyne, Claire A; Långström, Niklas; Rickert, Martin E; Lichtenstein, Paul; D'Onofrio, Brian M

2013-02-01

Teenage childbirth is a risk factor for poor offspring outcomes, particularly offspring antisocial behavior. It is not clear, however, if maternal age at first birth (MAFB) is causally associated with offspring antisocial behavior or if this association is due to selection factors that influence both the likelihood that a young woman gives birth early and that her offspring engage in antisocial behavior. The current study addresses the limitations of previous research by using longitudinal data from Swedish national registries and children of siblings and children of twins comparisons to identify the extent to which the association between MAFB and offspring criminal convictions is consistent with a causal influence and confounded by genetic or environmental factors that make cousins similar. We found offspring born to mothers who began childbearing earlier were more likely to be convicted of a crime than offspring born to mothers who delayed childbearing. The results from comparisons of differentially exposed cousins, especially born to discordant monozygotic twin sisters, provide support for a causal association between MAFB and offspring criminal convictions. The analyses also found little evidence for genetic confounding due to passive gene-environment correlation. Future studies are needed to replicate these findings and to identify environmental risk factors that mediate this causal association.

Maternal age at first birth and offspring criminality: Using the children-of-twins design to test causal hypotheses

Science.gov (United States)

Coyne, Claire A; Långström, Niklas; Rickert, Martin E; Lichtenstein, Paul; D’Onofrio, Brian M

2013-01-01

Teenage childbirth is a risk factor for poor offspring outcomes, particularly offspring antisocial behaviour. It is not clear if maternal age at first birth (MAFB) is causally associated with offspring antisocial behavior or if this association is due to selection factors that influence both the likelihood that a young woman gives birth early and that her offspring engage in antisocial behavior. The current study addresses the limitations of previous research by using longitudinal data from Swedish national registries and children-of-siblings and children-of-twins comparisons to identify the extent to which the association between MAFB and offspring criminal convictions is consistent with a causal influence and confounded by genetic or environmental factors that make cousins similar. We found offspring born to mothers who began childbearing earlier were more likely to be convicted of a crime than offspring born to mothers who delayed childbearing. The results from comparisons of differentially exposed cousins, especially born to discordant MZ twin sisters, provide support for a causal association between MAFB and offspring criminal convictions. The analyses also found little evidence for genetic confounding due to passive gene-environment correlation. Future studies are needed to replicate these findings and to identify environmental risk factors that mediate this causal association. PMID:23398750

Eastern minds in western cockpits: meta-analysis of human factors in mishaps from three nations.

Science.gov (United States)

Li, Wen-Chin; Harris, Don; Chen, Aurora

2007-04-01

Aviation accident rates vary in different regions; Asia and Africa have higher rates than Europe and America. There has been a great deal of discussion about the role of culture in aviation mishaps; however, culture is rarely mentioned as a contributory factor in accidents. It is hypothesized that different cultures will show different patterns in the underlying causal factors in aircraft accidents. Using a meta-analysis of previously published results, this research examined statistical differences in the 18 categories of the Human Factors Analysis and Classification System (HFACS) across accidents in the Republic of China (Taiwan), India, and the United States. Seven HFACS categories exhibited significant differences between these three regions. These were mostly concerned with contributory factors at the higher organizational levels. The differences were related to organizational processes, organizational climate, resource management, inadequate supervision, physical/mental limitations, adverse mental states, and decision errors. Overall, the evidence from this research supports the observation that national cultures have an impact on aviation safety and adds further explanatory power with regards to why this should be so. The majority of the cultural issues identified seem to be associated with the style of management of the organizations rather than the operation of the aircraft per se.

The impact of school leadership on school level factors: validation of a causal model

NARCIS (Netherlands)

Krüger, M.L.; Witziers, B.; Sleegers, P.

2007-01-01

This study aims to contribute to a better understanding of the antecedents and effects of educational leadership, and of the influence of the principal's leadership on intervening and outcome variables. A path analysis was conducted to test and validate a causal model. The results show no direct or

Human factors in safety and business management.

Science.gov (United States)

Vogt, Joachim; Leonhardt, Jorg; Koper, Birgit; Pennig, Stefan

2010-02-01

Human factors in safety is concerned with all those factors that influence people and their behaviour in safety-critical situations. In aviation these are, for example, environmental factors in the cockpit, organisational factors such as shift work, human characteristics such as ability and motivation of staff. Careful consideration of human factors is necessary to improve health and safety at work by optimising the interaction of humans with their technical and social (team, supervisor) work environment. This provides considerable benefits for business by increasing efficiency and by preventing incidents/accidents. The aim of this paper is to suggest management tools for this purpose. Management tools such as balanced scorecards (BSC) are widespread instruments and also well known in aviation organisations. Only a few aviation organisations utilise management tools for human factors although they are the most important conditions in the safety management systems of aviation organisations. One reason for this is that human factors are difficult to measure and therefore also difficult to manage. Studies in other domains, such as workplace health promotion, indicate that BSC-based tools are useful for human factor management. Their mission is to develop a set of indicators that are sensitive to organisational performance and help identify driving forces as well as bottlenecks. Another tool presented in this paper is the Human Resources Performance Model (HPM). HPM facilitates the integrative assessment of human factors programmes on the basis of a systematic performance analysis of the whole system. Cause-effect relationships between system elements are defined in process models in a first step and validated empirically in a second step. Thus, a specific representation of the performance processes is developed, which ranges from individual behaviour to system performance. HPM is more analytic than BSC-based tools because HPM also asks why a certain factor is

Behavioural Pattern of Causality Parameter of Autoregressive ...

African Journals Online (AJOL)

In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...

Tachyons and causal paradoxes

International Nuclear Information System (INIS)

Maund, J.B.

1979-01-01

Although the existence of tachyons is not ruled out by special relativity, it appears that causal paradoxes will arise if there are tachyons. The usual solutions to these paradoxes employ some form of the reinterpretation principle. In this paper it is argued first that, the principle is incoherent, second, that even if it is not, some causal paradoxes remain, and third, the most plausible ''solution,'' which appeals to boundary conditions of the universe, will conflict with special relativity

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.

Toxicology and Epidemiology: Improving the Science with a Framework for Combining Toxicological and Epidemiological Evidence to Establish Causal Inference

Science.gov (United States)

Adami, Hans-Olov; Berry, Sir Colin L.; Breckenridge, Charles B.; Smith, Lewis L.; Swenberg, James A.; Trichopoulos, Dimitrios; Weiss, Noel S.; Pastoor, Timothy P.

2011-01-01

Historically, toxicology has played a significant role in verifying conclusions drawn on the basis of epidemiological findings. Agents that were suggested to have a role in human diseases have been tested in animals to firmly establish a causative link. Bacterial pathogens are perhaps the oldest examples, and tobacco smoke and lung cancer and asbestos and mesothelioma provide two more recent examples. With the advent of toxicity testing guidelines and protocols, toxicology took on a role that was intended to anticipate or predict potential adverse effects in humans, and epidemiology, in many cases, served a role in verifying or negating these toxicological predictions. The coupled role of epidemiology and toxicology in discerning human health effects by environmental agents is obvious, but there is currently no systematic and transparent way to bring the data and analysis of the two disciplines together in a way that provides a unified view on an adverse causal relationship between an agent and a disease. In working to advance the interaction between the fields of toxicology and epidemiology, we propose here a five-step “Epid-Tox” process that would focus on: (1) collection of all relevant studies, (2) assessment of their quality, (3) evaluation of the weight of evidence, (4) assignment of a scalable conclusion, and (5) placement on a causal relationship grid. The causal relationship grid provides a clear view of how epidemiological and toxicological data intersect, permits straightforward conclusions with regard to a causal relationship between agent and effect, and can show how additional data can influence conclusions of causality. PMID:21561883

Renormalization group approach to causal bulk viscous cosmological models

International Nuclear Information System (INIS)

Belinchon, J A; Harko, T; Mak, M K

2002-01-01

The renormalization group method is applied to the study of homogeneous and flat Friedmann-Robertson-Walker type universes, filled with a causal bulk viscous cosmological fluid. The starting point of the study is the consideration of the scaling properties of the gravitational field equations, the causal evolution equation of the bulk viscous pressure and the equations of state. The requirement of scale invariance imposes strong constraints on the temporal evolution of the bulk viscosity coefficient, temperature and relaxation time, thus leading to the possibility of obtaining the bulk viscosity coefficient-energy density dependence. For a cosmological model with bulk viscosity coefficient proportional to the Hubble parameter, we perform the analysis of the renormalization group flow around the scale-invariant fixed point, thereby obtaining the long-time behaviour of the scale factor

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

Science.gov (United States)

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.

Linear causal modeling with structural equations

CERN Document Server

Mulaik, Stanley A

2009-01-01

Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal

Human factor problem in nuclear power generation

International Nuclear Information System (INIS)

Yoshino, Kenji; Fujimoto, Junzo

1999-01-01

Since a nuclear power plant accident at Threemile Island in U.S.A. occurred in March, 1979, twenty years have passed. After the accident, the human factor problem became focussed in nuclear power, to succeed its research at present. For direct reason of human error, most of factors at individual level or work operation level are often listed at their center. Then, it is natural that studies on design of a machine or apparatus suitable for various human functions and abilities and on improvement of relationship between 'human being and machine' and 'human being and working environment' are important in future. Here was, as first, described on outlines of the human factor problem in a nuclear power plant developed at a chance of past important accident, and then was described on educational training for its countermeasure. At last, some concrete researching results obtained by human factor research were introduced. (G.K.)

Identity, causality, and pronoun ambiguity.

Science.gov (United States)

Sagi, Eyal; Rips, Lance J

2014-10-01

This article looks at the way people determine the antecedent of a pronoun in sentence pairs, such as: Albert invited Ron to dinner. He spent hours cleaning the house. The experiment reported here is motivated by the idea that such judgments depend on reasoning about identity (e.g., the identity of the he who cleaned the house). Because the identity of an individual over time depends on the causal-historical path connecting the stages of the individual, the correct antecedent will also depend on causal connections. The experiment varied how likely it is that the event of the first sentence (e.g., the invitation) would cause the event of the second (the house cleaning) for each of the two individuals (the likelihood that if Albert invited Ron to dinner, this would cause Albert to clean the house, versus cause Ron to clean the house). Decisions about the antecedent followed causal likelihood. A mathematical model of causal identity accounted for most of the key aspects of the data from the individual sentence pairs. Copyright © 2014 Cognitive Science Society, Inc.

Research on disaster prevention by human factor

Energy Technology Data Exchange (ETDEWEB)

Kim, Bok Youn; Kang, Chang Hee; Kang, Sun Duck; Jo, Young Do [Korea Institute of Geology Mining and Materials, Taejon (Korea)

1998-12-01

Mining, by its very nature, requires workers and technology to function in an unpredictable environment that can not easily be engineered to accommodate human factors. Miners' physical and cognitive capabilities are sometimes stretched to the point that 'human error' in performance result. Mine safety researchers estimate that 50-85% of all mining injuries are due, in large part, to human error. Further research suggests that the primary causes of these errors in performance lie outside the individual and can be minimized by improvements in equipment design, work environments, work procedures and training. The human factors research is providing the science needed to determine which aspects of the mining environment can be made more worker-friendly and how miners can work more safely in environments that can not be improved. Underground mines have long been recognized as an innately hazardous and physically demanding work environment. Recently, mining is becoming a more complicated process as more sophisticated technologies are introduced. The more complicated or difficult the tasks to be performed, the more critical it is to have a systematic understanding of the humans, the technology, the environments, and how they interact. Human factors is a key component in solving most of today's mine safety and health problems. Human factors research primarily centered around solving problems in the following four areas: 1) How mining methods and equipment affect safety, 2) Evaluating the fit between miner's physical capabilities and the demands of their job, 3) Improving miner's ability to perceive and react to hazards, 4) Understanding how organizational and managerial variables influence safety. Human factor research was begun during the World war II. National Coal Board (British Coal) of Great Britain commenced ergonomics in 1969, and Bureau of Mine of United States started human factor researches in same year. Japan has very short history

The science of human factors: separating fact from fiction.

Science.gov (United States)

Russ, Alissa L; Fairbanks, Rollin J; Karsh, Ben-Tzion; Militello, Laura G; Saleem, Jason J; Wears, Robert L

2013-10-01

Interest in human factors has increased across healthcare communities and institutions as the value of human centred design in healthcare becomes increasingly clear. However, as human factors is becoming more prominent, there is growing evidence of confusion about human factors science, both anecdotally and in scientific literature. Some of the misconceptions about human factors may inadvertently create missed opportunities for healthcare improvement. The objective of this article is to describe the scientific discipline of human factors and provide common ground for partnerships between healthcare and human factors communities. The primary goal of human factors science is to promote efficiency, safety and effectiveness by improving the design of technologies, processes and work systems. As described in this article, human factors also provides insight on when training is likely (or unlikely) to be effective for improving patient safety. Finally, we outline human factors specialty areas that may be particularly relevant for improving healthcare delivery and provide examples to demonstrate their value. The human factors concepts presented in this article may foster interdisciplinary collaborations to yield new, sustainable solutions for healthcare quality and patient safety.

A Causal Theory of Modality

Directory of Open Access Journals (Sweden)

José Tomás Alvarado

2009-08-01

Full Text Available This work presents a causal conception of metaphysical modality in which a state of affairs is metaphysically possible if and only if it can be caused (in the past, the present or the future by current entities. The conception is contrasted with what is called the “combinatorial” conception of modality, in which everything can co-exist with anything else. This work explains how the notion of ‘causality’ should be construed in the causal theory, what difference exists between modalities thus defined from nomological modality, how accessibility relations between possible worlds should be interpreted, and what is the relation between the causal conception and the necessity of origin.

Causal reasoning with mental models

Science.gov (United States)

Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398

Causal reasoning with mental models.

Science.gov (United States)

Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Causal reasoning with mental models

Directory of Open Access Journals (Sweden)

Sangeet eKhemlani

2014-10-01

Full Text Available This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Foundational perspectives on causality in large-scale brain networks

Science.gov (United States)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

Human factors in atomic power plant

International Nuclear Information System (INIS)

Kawano, Ryutaro

1997-01-01

To ensure safety should have priority over all other things in atomic power plants. In Chernobyl accident, however, various human factors including the systems for bulb check after inspection and communication, troubles in the interface between hardwares such as warning speakers and instruments, and their operators, those in education and training for operators and those in the general management of the plant have been pointed out. Therefore, the principles and the practical measures from the aspect of human factors in atomic power plants were discussed here. The word, ''human factor'' was given a definition in terms of the direct cause and the intellectual system. An explanatory model for human factors, model SHEL constructed by The Tokyo Electric Power Co., Ltd., Inc. was presented; the four letter mean software(S), hardware(H), environment(E) and liveware(L). In the plants of the company, systemic measures for human error factors are taken now in all steps not only for design, operation and repairing but also the step for safety culture. Further, the level required for the safety against atomic power is higher in the company than those in other fields. Thus, the central principle in atomic power plants is changing from the previous views that technology is paid greater importance to a view regarding human as most importance. (M.N.)

Expert explanations of honeybee losses in areas of extensive agriculture in France: Gaucho® compared with other supposed causal factors

NARCIS (Netherlands)

Maxim, L.; van der Sluijs, J.P.

2010-01-01

Debates on causality are at the core of controversies as regards environmental changes. The present paper presents a new method for analyzing controversies on causality in a context of social debate and the results of its empirical testing. The case study used is the controversy as regards the role

Human factors issues for interstellar spacecraft

Science.gov (United States)

Cohen, Marc M.; Brody, Adam R.

1991-01-01

Developments in research on space human factors are reviewed in the context of a self-sustaining interstellar spacecraft based on the notion of traveling space settlements. Assumptions about interstellar travel are set forth addressing costs, mission durations, and the need for multigenerational space colonies. The model of human motivation by Maslow (1970) is examined and directly related to the design of space habitat architecture. Human-factors technology issues encompass the human-machine interface, crew selection and training, and the development of spaceship infrastructure during transtellar flight. A scenario for feasible instellar travel is based on a speed of 0.5c, a timeframe of about 100 yr, and an expandable multigenerational crew of about 100 members. Crew training is identified as a critical human-factors issue requiring the development of perceptual and cognitive aids such as expert systems and virtual reality.

Human Factors in Accidents Involving Remotely Piloted Aircraft

Science.gov (United States)

Merlin, Peter William

2013-01-01

This presentation examines human factors that contribute to RPA mishaps and provides analysis of lessons learned. RPA accident data from U.S. military and government agencies were reviewed and analyzed to identify human factors issues. Common contributors to RPA mishaps fell into several major categories: cognitive factors (pilot workload), physiological factors (fatigue and stress), environmental factors (situational awareness), staffing factors (training and crew coordination), and design factors (human machine interface).

Heterogeneous Causal Effects and Sample Selection Bias

DEFF Research Database (Denmark)

Breen, Richard; Choi, Seongsoo; Holm, Anders

2015-01-01

The role of education in the process of socioeconomic attainment is a topic of long standing interest to sociologists and economists. Recently there has been growing interest not only in estimating the average causal effect of education on outcomes such as earnings, but also in estimating how...... causal effects might vary over individuals or groups. In this paper we point out one of the under-appreciated hazards of seeking to estimate heterogeneous causal effects: conventional selection bias (that is, selection on baseline differences) can easily be mistaken for heterogeneity of causal effects....... This might lead us to find heterogeneous effects when the true effect is homogenous, or to wrongly estimate not only the magnitude but also the sign of heterogeneous effects. We apply a test for the robustness of heterogeneous causal effects in the face of varying degrees and patterns of selection bias...

Human and Organizational Factors

International Nuclear Information System (INIS)

Eshiett, P.B.S.

2016-01-01

The Human and Organizational Factors Approach to Industrial Safety (HOFS) consists of identifying and putting in place conditions which encourage a positive contribution from operators (individually and in a team) with regards to industrial safety. The knowledge offered by the HOFS approach makes it possible better to understand what conditions human activity and to act on the design of occupational situations and the organization, in the aim of creating the conditions for safe work. Efforts made in this area can also lead to an improvement in results in terms of the quality of production or occupational safety (incidence and seriousness rates) (Daniellou, F., et al., 2011). Research on industrial accidents shows that they rarely happen as a result of a single event, but rather emerge from the accumulation of several, often seemingly trivial, malfunctions, misunderstandings, incorrect assumptions and other issues. The nuclear community has established rigorous international safety standards and concepts to ensure the protection of people and the environment from harmful effects of ionizing radiation (IAEA, 2014). A review of major human induced disasters in a number of countries and in different industries yields insights into several of the human and organizational factors involved in their occurrence. Some of these factors relate to failures in: • Design or technology; • Training; • Decision making; • Communication; • Preparation for the unexpected; • Understanding of organizational interdependencies

Human Factors Throughout the Life Cycle: Lessons Learned from the Shuttle Program. [Human Factors in Ground Processing

Science.gov (United States)

Kanki, Barbara G.

2011-01-01

With the ending of the Space Shuttle Program, it is critical that we not forget the Human Factors lessons we have learned over the years. At every phase of the life cycle, from manufacturing, processing and integrating vehicle and payload, to launch, flight operations, mission control and landing, hundreds of teams have worked together to achieve mission success in one of the most complex, high-risk socio-technical enterprises ever designed. Just as there was great diversity in the types of operations performed at every stage, there was a myriad of human factors that could further complicate these human systems. A single mishap or close call could point to issues at the individual level (perceptual or workload limitations, training, fatigue, human error susceptibilities), the task level (design of tools, procedures and aspects of the workplace), as well as the organizational level (appropriate resources, safety policies, information access and communication channels). While we have often had to learn through human mistakes and technological failures, we have also begun to understand how to design human systems in which individuals can excel, where tasks and procedures are not only safe but efficient, and how organizations can foster a proactive approach to managing risk and supporting human enterprises. Panelists will talk about their experiences as they relate human factors to a particular phase of the shuttle life cycle. They will conclude with a framework for tying together human factors lessons-learned into system-level risk management strategies.

Causal diagrams in systems epidemiology

Directory of Open Access Journals (Sweden)

Joffe Michael

2012-03-01

Full Text Available Abstract Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s. Transmitted causes ("causes of causes" tend not to be systematically analysed. The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties. The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets. Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.

Causal diagrams in systems epidemiology.

Science.gov (United States)

Joffe, Michael; Gambhir, Manoj; Chadeau-Hyam, Marc; Vineis, Paolo

2012-03-19

Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed.The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties.The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets.Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.

Entanglement entropy in causal set theory

Science.gov (United States)

Sorkin, Rafael D.; Yazdi, Yasaman K.

2018-04-01

Entanglement entropy is now widely accepted as having deep connections with quantum gravity. It is therefore desirable to understand it in the context of causal sets, especially since they provide in a natural manner the UV cutoff needed to render entanglement entropy finite. Formulating a notion of entanglement entropy in a causal set is not straightforward because the type of canonical hypersurface-data on which its definition typically relies is not available. Instead, we appeal to the more global expression given in Sorkin (2012 (arXiv:1205.2953)) which, for a Gaussian scalar field, expresses the entropy of a spacetime region in terms of the field’s correlation function within that region (its ‘Wightman function’ W(x, x') ). Carrying this formula over to the causal set, one obtains an entropy which is both finite and of a Lorentz invariant nature. We evaluate this global entropy-expression numerically for certain regions (primarily order-intervals or ‘causal diamonds’) within causal sets of 1  +  1 dimensions. For the causal-set counterpart of the entanglement entropy, we obtain, in the first instance, a result that follows a (spacetime) volume law instead of the expected (spatial) area law. We find, however, that one obtains an area law if one truncates the commutator function (‘Pauli–Jordan operator’) and the Wightman function by projecting out the eigenmodes of the Pauli–Jordan operator whose eigenvalues are too close to zero according to a geometrical criterion which we describe more fully below. In connection with these results and the questions they raise, we also study the ‘entropy of coarse-graining’ generated by thinning out the causal set, and we compare it with what one obtains by similarly thinning out a chain of harmonic oscillators, finding the same, ‘universal’ behaviour in both cases.

Development of human factors design review guidelines

Energy Technology Data Exchange (ETDEWEB)

Lee, Jung Woon; Oh, In Suk; Suh, Sang Moon; Lee, Hyun Chul [Korea Atomic Energy Research Institute, Taejon (Korea)

1997-10-01

The objective of this study is to develop human factors engineering program review guidelines and alarm system review guidelines in order to resolve the two major technical issues: 25. Human Factors Engineering Program Review Model and 26. Review Criteria for Human Factors Aspects of Advanced Controls and Instrumentation, which are related to the development of human factors safety regulation guides being performed by KINS. For the development of human factors program review guidelines, we made a Korean version of NUREG-0711 and added our comments by considering Korean regulatory situation and reviewing the reference documents of NUREG-0711. We also computerized the Korean version of NUREG-0711, additional comments, and selected portion of the reference documents for the developer of safety regulation guides in KINS to see the contents comparatively at a glance and use them easily. For the development of alarm system review guidelines, we made a Korean version of NUREG/CR-6105, which was published by NRC in 1994 as a guideline document for the human factors review of alarm systems. Then we will update the guidelines by reviewing the literature related to alarm design published after 1994. (author). 12 refs., 5 figs., 2 tabs.

Acute appendicitis following blunt abdominal trauma. Coincidence or causality?

Directory of Open Access Journals (Sweden)

Sergio Iván Latorre

2017-01-01

Full Text Available Acute appendicitis is a common disease in clinical practice; some well-defined causes include luminal obstruction by fecoliths, lymphoid hyperplasia, foreign bodies and intestinal parasites. Closed abdominal trauma has been associated as an etiological factor, although, their causal relationship is still unclear. This paper presents the case of a patient with appendicitis after a closed abdominal trauma.

Preschoolers prefer to learn causal information

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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

In defense of causal-formative indicators: A minority report.

Science.gov (United States)

Bollen, Kenneth A; Diamantopoulos, Adamantios

2017-09-01

Causal-formative indicators directly affect their corresponding latent variable. They run counter to the predominant view that indicators depend on latent variables and are thus often controversial. If present, such indicators have serious implications for factor analysis, reliability theory, item response theory, structural equation models, and most measurement approaches that are based on reflective or effect indicators. Psychological Methods has published a number of influential articles on causal and formative indicators as well as launching the first major backlash against them. This article examines 7 common criticisms of these indicators distilled from the literature: (a) A construct measured with "formative" indicators does not exist independently of its indicators; (b) Such indicators are causes rather than measures; (c) They imply multiple dimensions to a construct and this is a liability; (d) They are assumed to be error-free, which is unrealistic; (e) They are inherently subject to interpretational confounding; (f) They fail proportionality constraints; and (g) Their coefficients should be set in advance and not estimated. We summarize each of these criticisms and point out the flaws in the logic and evidence marshaled in their support. The most common problems are not distinguishing between what we call causal-formative and composite-formative indicators, tautological fallacies, and highlighting issues that are common to all indicators, but presenting them as special problems of causal-formative indicators. We conclude that measurement theory needs (a) to incorporate these types of indicators, and (b) to better understand their similarities to and differences from traditional indicators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Mathematical implications of Einstein-Weyl causality

International Nuclear Information System (INIS)

Borchers, H.J.; Sen, R.N.

2006-01-01

The present work is the first systematic attempt at answering the following fundamental question: what mathematical structures does Einstein-Weyl causality impose on a point-set that has no other previous structure defined on it? The authors propose an axiomatization of Einstein-Weyl causality (inspired by physics), and investigate the topological and uniform structures that it implies. Their final result is that a causal space is densely embedded in one that is locally a differentiable manifold. The mathematical level required of the reader is that of the graduate student in mathematical physics. (orig.)

Causality violation, gravitational shockwaves and UV completion

Energy Technology Data Exchange (ETDEWEB)

Hollowood, Timothy J.; Shore, Graham M. [Department of Physics, Swansea University,Swansea, SA2 8PP (United Kingdom)

2016-03-18

The effective actions describing the low-energy dynamics of QFTs involving gravity generically exhibit causality violations. These may take the form of superluminal propagation or Shapiro time advances and allow the construction of “time machines”, i.e. spacetimes admitting closed non-spacelike curves. Here, we discuss critically whether such causality violations may be used as a criterion to identify unphysical effective actions or whether, and how, causality problems may be resolved by embedding the action in a fundamental, UV complete QFT. We study in detail the case of photon scattering in an Aichelburg-Sexl gravitational shockwave background and calculate the phase shifts in QED for all energies, demonstrating their smooth interpolation from the causality-violating effective action values at low-energy to their manifestly causal high-energy limits. At low energies, these phase shifts may be interpreted as backwards-in-time coordinate jumps as the photon encounters the shock wavefront, and we illustrate how the resulting causality problems emerge and are resolved in a two-shockwave time machine scenario. The implications of our results for ultra-high (Planck) energy scattering, in which graviton exchange is modelled by the shockwave background, are highlighted.

Introductive remarks on causal inference

Directory of Open Access Journals (Sweden)

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.

Exploring Torus Universes in Causal Dynamical Triangulations

DEFF Research Database (Denmark)

Budd, Timothy George; Loll, R.

2013-01-01

Motivated by the search for new observables in nonperturbative quantum gravity, we consider Causal Dynamical Triangulations (CDT) in 2+1 dimensions with the spatial topology of a torus. This system is of particular interest, because one can study not only the global scale factor, but also global...... shape variables in the presence of arbitrary quantum fluctuations of the geometry. Our initial investigation focusses on the dynamics of the scale factor and uncovers a qualitatively new behaviour, which leads us to investigate a novel type of boundary conditions for the path integral. Comparing large....... Apart from setting the stage for the analysis of shape dynamics on the torus, the new set-up highlights the role of nontrivial boundaries and topology....

Human Factors in Space Exploration

Science.gov (United States)

Jones, Patricia M.; Fiedler, Edna

2010-01-01

The exploration of space is one of the most fascinating domains to study from a human factors perspective. Like other complex work domains such as aviation (Pritchett and Kim, 2008), air traffic management (Durso and Manning, 2008), health care (Morrow, North, and Wickens, 2006), homeland security (Cooke and Winner, 2008), and vehicle control (Lee, 2006), space exploration is a large-scale sociotechnical work domain characterized by complexity, dynamism, uncertainty, and risk in real-time operational contexts (Perrow, 1999; Woods et ai, 1994). Nearly the entire gamut of human factors issues - for example, human-automation interaction (Sheridan and Parasuraman, 2006), telerobotics, display and control design (Smith, Bennett, and Stone, 2006), usability, anthropometry (Chaffin, 2008), biomechanics (Marras and Radwin, 2006), safety engineering, emergency operations, maintenance human factors, situation awareness (Tenney and Pew, 2006), crew resource management (Salas et aI., 2006), methods for cognitive work analysis (Bisantz and Roth, 2008) and the like -- are applicable to astronauts, mission control, operational medicine, Space Shuttle manufacturing and assembly operations, and space suit designers as they are in other work domains (e.g., Bloomberg, 2003; Bos et al, 2006; Brooks and Ince, 1992; Casler and Cook, 1999; Jones, 1994; McCurdy et ai, 2006; Neerincx et aI., 2006; Olofinboba and Dorneich, 2005; Patterson, Watts-Perotti and Woods, 1999; Patterson and Woods, 2001; Seagull et ai, 2007; Sierhuis, Clancey and Sims, 2002). The human exploration of space also has unique challenges of particular interest to human factors research and practice. This chapter provides an overview of those issues and reports on sorne of the latest research results as well as the latest challenges still facing the field.

Accidents and human factors

International Nuclear Information System (INIS)

Nishiwaki, Y.; Kawai, H.; Morishima, H.; Terano, T.; Sugeno, M.

1984-01-01

When the TMI accident occurred it was 4 a.m., an hour when the error potential of the operators would have been very high. The frequency of car and train accidents in Japan is also highest between 4 a.m. and 6 a.m. The error potential may be classified into five phases corresponding to the electroencephalogramic pattern (EEG). At phase 0, when the delta wave appears, a person is unconscious and in deep sleep; at phase I, when the theta wave appears, he is very tired, sleepy and subnormal; at phase II, when the alpha wave appears, he is normal, relaxed and passive; at phase III, when the beta wave appears, he is normal, clear-minded and active; at phase IV, when the strong beta or epileptic wave appears, he is hypernormal, excited and incapable of normal judgement. Should an accident occur at phase II, the brain condition may jump to phase IV. At this phase the error or accident potential is maximum. The response of the human brain to different types of noises and signals may vary somewhat for different individuals and for different groups of people. Therefore, the possibility that such differences in brain functions may influence the mental structure would be worthy of consideration in human factors and in the design of man-machine systems. Human reliability and performance would be affected by many factors: medical, physiological and psychological, etc. The uncertainty involved in human factors may not necessarily be probabilistic, but fuzzy. Therefore, it would be important to develop a theory by which both non-probabilistic uncertainties, or fuzziness, of human factors and the probabilistic properties of machines can be treated consistently. From the mathematical point of view, probabilistic measure is considered a special case of fuzzy measure. Therefore, fuzzy set theory seems to be an effective tool for analysing man-machine systems. To minimize human error and the possibility of accidents, new safety systems should not only back up man and make up for his

Human Modeling for Ground Processing Human Factors Engineering Analysis

Science.gov (United States)

Stambolian, Damon B.; Lawrence, Brad A.; Stelges, Katrine S.; Steady, Marie-Jeanne O.; Ridgwell, Lora C.; Mills, Robert E.; Henderson, Gena; Tran, Donald; Barth, Tim

2011-01-01

There have been many advancements and accomplishments over the last few years using human modeling for human factors engineering analysis for design of spacecraft. The key methods used for this are motion capture and computer generated human models. The focus of this paper is to explain the human modeling currently used at Kennedy Space Center (KSC), and to explain the future plans for human modeling for future spacecraft designs

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.

Human Leptospirosis and risk factors.

Directory of Open Access Journals (Sweden)

Yanelis Emilia Tabío Henry

2010-09-01

Full Text Available The human leptospirosis is a zoonosis of world distribution, were risk factors exist that have favored the wild and domestic animal propagation and so man. A descpitive investigation was made with the objective of determining the behavior of risk factors in outpatients by human leptospirosis in “Camilo Cienfuegos“ University General Hospital from Sncti Spíritus In the comprised time period betwen december 1 st and 3 st , 2008.The sample of this study was conformed by 54 risk persons that keep inclusion criteria. Some variables were used:age, sex, risk factors and number of ill persons, according to the month. Some patients of masculine sex prevailed (61,9%, group of ages between 15-29 and 45-59 years (27,7%, patients treated since october to december (53,7%, the direct and indirect contact with animals (46,2 %. The risk factors cassually associated to human leptospirosis turned to be: the masculine sex, the contac with animals, the occupational exposition and the inmersion on sources of sweet water.

Human factors of safety: a few landmarks

International Nuclear Information System (INIS)

Mosneron Dupin, F.

1992-06-01

This paper discusses factors to be taken into account, and methods to be used. It concludes that more realistic and positive conceptions of Human Factors should be developed, and that Human Factors should be addressed at the very beginning of any technical project

The causal effect of institutional quality on outsourcing

OpenAIRE

H.J. Roelfsema; Zhang Yi

2009-01-01

This paper empirically investigates the relationship between institutional quality and outsourcing to developing economies. In contrast to cross-sectional studies on institutions, this paper uses panel data for 76 countries over 25 years (1980-2004). Employing panel data helps to show the causal relationship by controlling for the fixed effects and dynamic factors. Using within and IV estimations, we find that there is a positive effect of institutional quality on outsourcing in the lower-mid...

Quasi-Experimental Designs for Causal Inference

Science.gov (United States)

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Linear and Nonlinear Causality between Energy Consumption and Economic Growth: The Case of Mexico 1965–2014

Directory of Open Access Journals (Sweden)

Mario Gómez

2018-03-01

Full Text Available This paper analyzes the causal link between aggregated and disaggregated levels of energy consumption and economic growth in Mexico between 1965 and 2014, with the presence of structural breaks stemming from the series. To that end, unit root with structural breaks, cointegration, and linear and nonlinear causality tests are employed. The results show that there is a long-run relationship between production, capital, labor, and energy, and linear causal links from total and disaggregated energy consumption to economic growth. A nonlinear causality also exists from energy consumption, the transport sector, capital, and labor to output. These results support the growth hypothesis, which maintains that energy is an important input factor for economic activity and that energy conservation policies impact the economic growth in Mexico.

ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework.

Science.gov (United States)

Zhang, Kunlin; Chang, Suhua; Cui, Sijia; Guo, Liyuan; Zhang, Liuyan; Wang, Jing

2011-07-01

Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no web server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap between GWAS and disease mechanism study by generating hypothesis of SNP → gene → pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.

ACSNI study group on human factors

International Nuclear Information System (INIS)

1993-01-01

Organisational failures are now recognised as being as important as mechanical failures or individual human errors in causing major accidents such as the capsize of the Herald of Free Enterprise or the Pipa Alpha disaster. The Human Factors Study Group of the Advisory Committee on the Safety of Nuclear Installations was set up to look at the part played by human factors in nuclear risk and its reduction. The third report of the Study Group considers the role played by organisational factors and management in promoting nuclear safety. Actions to review and promote a safety culture are suggested. Three main conclusions are drawn and several recommendations made. (UK)

Gravity and matter in causal set theory

International Nuclear Information System (INIS)

Sverdlov, Roman; Bombelli, Luca

2009-01-01

The goal of this paper is to propose an approach to the formulation of dynamics for causal sets and coupled matter fields. We start from the continuum version of the action for a Klein-Gordon field coupled to gravity, and rewrite it first using quantities that have a direct correspondent in the case of a causal set, namely volumes, causal relations and timelike lengths, as variables to describe the geometry. In this step, the local Lagrangian density L(f;x) for a set of fields f is recast into a quasilocal expression L 0 (f;p,q) that depends on pairs of causally related points pprq and is a function of the values of f in the Alexandrov set defined by those points, and whose limit as p and q approach a common point x is L(f;x). We then describe how to discretize L 0 (f;p,q) and use it to define a causal-set-based action.

The influence of causal attribution of parents on developing the child enuresis

Directory of Open Access Journals (Sweden)

Jerković Ivan

2003-01-01

Full Text Available Causal attributions are affirmed as a cognitive element able to explain emotional and motivational aspects of behaviour of some categories of adult psychiatric patients, primarily depressive ones. Theoretical and practical success of cognitive ideas in explaining the origination of depressive disorders, and in the monitoring of depressive patient treatment has led to further development of theory, but also to the attempt to apply the learning about causal attributions to various problems. Characteristic attempts are those that the problems of child abuse, children’s depression, upbringing problems, school failure, hyperactivity, enuresis, and long-term effects of different child treatment, too, are analysed from the point of view of causal attributions. By assessing parent causal attributions regarding child night urination, we wanted to establish to what extent specific attributions for child behaviour differentiate the parents of children having this problem from those parents whose children have established control. Parents were assessed in terms of four dimensions of causal attributions for child’s problem. Those are the dimensions of globality, counter-lability, internality, and the stability of the cause of child’s problem. The analysis of parent causal attributions show that mothers and fathers in both assessed groups similarly experience the cause of enuretic problems of their children. Enuresis is seen as caused by specific, internal, and instable causes. Such a system of dimensions could correspond to the belief that the main etiological factor of the enuresis is maturing. For more reliable verification of this attitude, longitudinal strategy in research is necessary, especially to comprehend whether parental attributions have been developed as an effect of persistent enuresis, or whether the enuresis is developed as an effect of parental attributions.

Causal theory in (2+1)-dimensional Qed

International Nuclear Information System (INIS)

Scharf, G.; Wreszinski, W.F.

1994-01-01

The program of constructing the S-matrix by means of causality in quantum field theory goes back to Stueckelberg and Bogoliubov. Epstein and Glaser proposed an axiomatic construct where ultraviolet divergences do not appear, leading directly to the renormalized perturbation series. They have shown that in the causal theory the UV problem is a consequence of incorrect distribution splitting. This paper studies the causal theory in (2+1)D Qed

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.

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 .

Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation

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Paolo Vineis

2017-06-01

Full Text Available Abstract In the last decades, Systems Biology (including cancer research has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a causality in epidemiology and in philosophical theorizing—notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social—are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.

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.

Human Factor in Therapeutic Relationship

Directory of Open Access Journals (Sweden)

Ramazan Akdogan

2011-03-01

Full Text Available herapeutic relationship is a professional relationship that has been structured based on theoretical props. This relationship is a complicated, wide and unique relationship which develops between two people, where both sides' personality and attitudes inevitably interfere. Therapist-client relationship experienced through transference and counter transference, especially in psychodynamic approaches, is accepted as the main aspect of therapeutic process. However, the approaches without dynamic/deterministic tendency also take therapist-client relationship into account seriously and stress uniqueness of interaction between two people. Being a person and a human naturally sometimes may negatively influence the relationship between the therapist and client and result in a relationship going out of the theoretical frame at times. As effective components of a therapeutic process, the factors that stem from being human include the unique personalities of the therapist and the client, their values and their attitude either made consciously or subconsciously. Literature has shown that the human-related factors are too effective to be denied in therapeutic relationship process. Ethical and theoretical knowledge can be inefficient to prevent the negative effects of these factors in therapeutic process at which point a deep insight and supervision would have a critical role in continuing an acceptable therapeutic relationship. This review is focused on the reflection of some therapeutic factors resulting from being human and development of counter transference onto the therapeutic process.

Development of human factors design review guidelines

International Nuclear Information System (INIS)

Lee, Jung Woon; Oh, In Suk; Suh, Sang Moon; Lee, Hyun Chul

1997-10-01

The Objective of this study is to develop human factors engineering program review guidelines and alarm system review guidelines in order to resolve the two major technical issues: '25, Human factors engineering program review model' and '26, Review criteria for human actors aspects of advanced controls and instrumentation', which are related to the development of human factors safety regulation guides be ing performed by KINS. For the development of human factors program review guidelines, we made a Korean version of NUREG-0711 and added our comments by considering Korean regulatory situation and reviewing the reference documents of NUREG-0711. We also computerized the Korean version of NUREG-0711, additional comments, and selected portion of the reference documents for the developer of safety regulation guides in KINS to see the contents comparatively at a glance and use them easily. For the development of alarm system review guidelines, we made a Korean version of NUREG/CR-6105, which was published by NRC in 1994 as a guideline document for the human factors review of alarm systems. Then we well update the guidelines by reviewing the literature related to alarm design published after 1994

Human Research Program: Space Human Factors and Habitability Element

Science.gov (United States)

Russo, Dane M.

2007-01-01

The three project areas of the Space Human Factors and Habitability Element work together to achieve a working and living environment that will keep crews healthy, safe, and productive throughout all missions -- from Earth orbit to Mars expeditions. The Advanced Environmental Health (AEH) Project develops and evaluates advanced habitability systems and establishes requirements and health standards for exploration missions. The Space Human Factors Engineering (SHFE) Project s goal is to ensure a safe and productive environment for humans in space. With missions using new technologies at an ever-increasing rate, it is imperative that these advances enhance crew performance without increasing stress or risk. The ultimate goal of Advanced Food Technology (AFT) Project is to develop and deliver technologies for human centered spacecraft that will support crews on missions to the moon, Mars, and beyond.

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.

Human Factors in Nuclear Reactor Accidents

International Nuclear Information System (INIS)

Mustafa, M.E.

2016-01-01

While many people would blame nature for the disaster of the “Fukushima Daiichi” accident, experts considered this accident to be also a human-induced disaster. This confirmed the importance of human errors which have been getting a growing interest in the nuclear field after the Three Mile Island accident. Personnel play an important role in design, operation, maintenance, planning, and management. The interface between machine and man is known as a human factor. In the present work, the human factors that have to be considered were discussed. The effect of the control room configuration and equipment design effect on the human behavior was also discussed. Precise reviewing of person’s qualifications and experience was focused. Insufficient training has been a major cause of human error in the nuclear field. The effective training issues were introduced. Avoiding complicated operational processes and non responsive management systems was stressed. Distinguishing between the procedures for normal and emergency operations was emphasised. It was stated that human error during maintenance and testing activities could cause a serious accident. This is because safety systems do not cover much more risk probabilities in the maintenance and testing activities like they do in the normal operation. In nuclear industry, the need for a classification and identification of human errors has been well recognised. As a result of this, human reliability must be assessed. These errors are analyzed by a probabilistic safety assessment which deals with errors in reading, listening and implementing procedures but not with cognitive errors. Much efforts must be accomplished to consider cognitive errors in the probabilistic safety assessment. The ways of collecting human factor data were surveyed. The methods for identifying safe designs, helping decision makers to predict how proposed or current policies will affect safety, and comprehensive understanding of the relationship

Development of a Field Management Standard for Improving Human Factors

International Nuclear Information System (INIS)

Yun, Young Su; Son, Il Moon; Son, Byung Chang; Kwak, Hyo Yean

2009-07-01

This project is to develop a management guideline for improving human performances as a part of the Human Factors Management System of Kori unit 1 which is managing all of human factors items such as man-machine system interfaces, work procedures, work environments, and human reliabilities in nuclear power plants. Human factors engineering includes an human factors suitability analysis and improvement of human works, an analysis of accidents by human error, an improvement of work environment, an establishment of human factors management rules and a development of human resources to manage and perform those things consistently. For assisting these human factors engineering tasks, we developed human factors management guidelines, checklists and work procedures to be used in staffing, qualification, training, and human information requirements and workload. We also provided a software tool for managing the above items. Additionally, contents and an item pool for a human factors qualifying examination and training programs were developed. A procedures improvement and a human factors V and V on the Kori unit 1 have been completed as a part of this project, too

On minimizers of causal variational principles

International Nuclear Information System (INIS)

Schiefeneder, Daniela

2011-01-01

Causal variational principles are a class of nonlinear minimization problems which arise in a formulation of relativistic quantum theory referred to as the fermionic projector approach. This thesis is devoted to a numerical and analytic study of the minimizers of a general class of causal variational principles. We begin with a numerical investigation of variational principles for the fermionic projector in discrete space-time. It is shown that for sufficiently many space-time points, the minimizing fermionic projector induces non-trivial causal relations on the space-time points. We then generalize the setting by introducing a class of causal variational principles for measures on a compact manifold. In our main result we prove under general assumptions that the support of a minimizing measure is either completely timelike, or it is singular in the sense that its interior is empty. In the examples of the circle, the sphere and certain flag manifolds, the general results are supplemented by a more detailed analysis of the minimizers. (orig.)

Human factoring administrative procedures

International Nuclear Information System (INIS)

Grider, D.A.; Sturdivant, M.H.

1991-01-01

In nonnuclear business, administrative procedures bring to mind such mundane topics as filing correspondence and scheduling vacation time. In the nuclear industry, on the other hand, administrative procedures play a vital role in assuring the safe operation of a facility. For some time now, industry focus has been on improving technical procedures. Significant efforts are under way to produce technical procedure requires that a validated technical, regulatory, and administrative basis be developed and that the technical process be established for each procedure. Producing usable technical procedures requires that procedure presentation be engineered to the same human factors principles used in control room design. The vital safety role of administrative procedures requires that they be just as sound, just a rigorously formulated, and documented as technical procedures. Procedure programs at the Tennessee Valley Authority and at Boston Edison's Pilgrim Station demonstrate that human factors engineering techniques can be applied effectively to technical procedures. With a few modifications, those same techniques can be used to produce more effective administrative procedures. Efforts are under way at the US Department of Energy Nuclear Weapons Complex and at some utilities (Boston Edison, for instance) to apply human factors engineering to administrative procedures: The techniques being adapted include the following

Are bruxism and the bite causally related?

Science.gov (United States)

Lobbezoo, F; Ahlberg, J; Manfredini, D; Winocur, E

2012-07-01

In the dental profession, the belief that bruxism and dental (mal-)occlusion ('the bite') are causally related is widespread. The aim of this review was to critically assess the available literature on this topic. A PubMed search of the English-language literature, using the query 'Bruxism [Majr] AND (Dental Occlusion [Majr] OR Malocclusion [Majr])', yielded 93 articles, of which 46 papers were finally included in the present review*. Part of the included publications dealt with the possible associations between bruxism and aspects of occlusion, from which it was concluded that neither for occlusal interferences nor for factors related to the anatomy of the oro-facial skeleton, there is any evidence available that they are involved in the aetiology of bruxism. Instead, there is a growing awareness of other factors (viz. psychosocial and behavioural ones) being important in the aetiology of bruxism. Another part of the included papers assessed the possible mediating role of occlusion between bruxism and its purported consequences (e.g. tooth wear, loss of periodontal tissues, and temporomandibular pain and dysfunction). Even though most dentists agree that bruxism may have several adverse effects on the masticatory system, for none of these purported adverse effects, evidence for a mediating role of occlusion and articulation has been found to date. Hence, based on this review, it should be concluded that to date, there is no evidence whatsoever for a causal relationship between bruxism and the bite. © 2012 Blackwell Publishing Ltd.

Warranty claim analysis considering human factors

International Nuclear Information System (INIS)

Wu Shaomin

2011-01-01

Warranty claims are not always due to product failures. They can also be caused by two types of human factors. On the one hand, consumers might claim warranty due to misuse and/or failures caused by various human factors. Such claims might account for more than 10% of all reported claims. On the other hand, consumers might not be bothered to claim warranty for failed items that are still under warranty, or they may claim warranty after they have experienced several intermittent failures. These two types of human factors can affect warranty claim costs. However, research in this area has received rather little attention. In this paper, we propose three models to estimate the expected warranty cost when the two types of human factors are included. We consider two types of failures: intermittent and fatal failures, which might result in different claim patterns. Consumers might report claims after a fatal failure has occurred, and upon intermittent failures they might report claims after a number of failures have occurred. Numerical examples are given to validate the results derived.

Therapeutic Targets of Triglyceride Metabolism as Informed by Human Genetics.

Science.gov (United States)

Bauer, Robert C; Khetarpal, Sumeet A; Hand, Nicholas J; Rader, Daniel J

2016-04-01

Human genetics has contributed to the development of multiple drugs to treat hyperlipidemia and coronary artery disease (CAD), most recently including antibodies targeting PCSK9 to reduce LDL cholesterol. Despite these successes, a large burden of CAD remains. Genetic and epidemiological studies have suggested that circulating triglyceride (TG)-rich lipoproteins (TRLs) are a causal risk factor for CAD, presenting an opportunity for novel therapeutic strategies. We discuss recent unbiased human genetics testing, including genome-wide association studies (GWAS) and whole-genome or -exome sequencing, that have identified the lipoprotein lipase (LPL) and hepatic lipogenesis pathways as important mechanisms in the regulation of circulating TRLs. Further strengthening the causal relationship between TRLs and CAD, findings such as these may provide novel targets for much-needed potential therapeutic interventions. Copyright © 2016. Published by Elsevier Ltd.

Human factors in RBNK plants

International Nuclear Information System (INIS)

Demitrack, T.

1995-01-01

The Safety of RBMK nuclear power plants in the Russian Federation, The Ukraine and Lithuanian is a topic of concern to the European Union and other Western European countries. The European Commission, Sweden, Finland and Canada financed the project Safety Design Solutions and Operation of NPP with RBMK Reactors. The project examined nine issues and recommended safety improvements which will form the basis of future European Commission spending on these power plants. During its year of work, the project examined these issues: 1. Systems Engineering and progression of accidents 2. Protection System 3. Core Physics 4. External Events 5. Engineering Quality 6. Operating Experience 7. Human Factors 8. Regulatory Interface 9. Probabilistic Safety analysis Empresarios Agrupados, in collaboration with other western European firms, the Russian Federation and Lithuanian took part in two of these groups, Human Factors and Probabilistic Safety Analysis. This presentation gives a brief description of the most important aspects of human factors in RBMK plants, focusing on operations organization, training and education

The psychophysics of comic: Effects of incongruity in causality and animacy.

Science.gov (United States)

Parovel, Giulia; Guidi, Stefano

2015-07-01

According to several theories of humour (see Berger, 2012; Martin, 2007), incongruity - i.e., the presence of two incompatible meanings in the same situation - is a crucial condition for an event being evaluated as comical. The aim of this research was to test with psychophysical methods the role of incongruity in visual perception by manipulating the causal paradigm (Michotte, 1946/1963) to get a comic effect. We ran three experiments. In Experiment 1, we tested the role of speed ratio between the first and the second movement, and the effect of animacy cues (i.e. frog-like and jumping-like trajectories) in the second movement; in Experiment 2, we manipulated the temporal delay between the movements to explore the relationship between perceptual causal contingencies and comic impressions; in Experiment 3, we compared the strength of the comic impressions arising from incongruent trajectories based on animacy cues with those arising from incongruent trajectories not based on animacy cues (bouncing and rotating) in the second part of the causal event. General findings showed that the paradoxical juxtaposition of a living behaviour in the perceptual causal paradigm is a powerful factor in eliciting comic appreciations, coherently with the Bergsonian perspective in particular (Bergson, 2003), and with incongruity theories in general. Copyright © 2015 Elsevier B.V. All rights reserved.

Human factors with nonhumans - Factors that affect computer-task performance

Science.gov (United States)

Washburn, David A.

1992-01-01

There are two general strategies that may be employed for 'doing human factors research with nonhuman animals'. First, one may use the methods of traditional human factors investigations to examine the nonhuman animal-to-machine interface. Alternatively, one might use performance by nonhuman animals as a surrogate for or model of performance by a human operator. Each of these approaches is illustrated with data in the present review. Chronic ambient noise was found to have a significant but inconsequential effect on computer-task performance by rhesus monkeys (Macaca mulatta). Additional data supported the generality of findings such as these to humans, showing that rhesus monkeys are appropriate models of human psychomotor performance. It is argued that ultimately the interface between comparative psychology and technology will depend on the coordinated use of both strategies of investigation.

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.

Contending Claims to Causality: A Critical Review of Mediation Research in HRD

Science.gov (United States)

Ghosh, Rajashi; Jacobson, Seth

2016-01-01

Purpose: The purpose of this paper is to conduct a critical review of the mediation studies published in the field of Human Resource Development (HRD) to discern if the study designs, the nature of data collection and the choice of statistical methods justify the causal claims made in those studies. Design/methodology/approach: This paper conducts…

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.

Applications of human factors engineering in the digital HMI

International Nuclear Information System (INIS)

Zhou Bingjian

2014-01-01

In order to prevent and minimize human errors in the digital main control room, the principles of human factors engineering must be complied strictly in the design process of digital human-machine interface. This paper briefly describes the basic human factors engineering principles of designing main control room, introduces the main steps to implement the human factors engineering verification and validation of main control room, including HSI task support verification, human factors engineering design verification and integrated system validation. Meanwhile, according to the new digital human-machine interface characteristics, the development models of human error are analyzed. (author)

Effectiveness of human factors simulator; Eficiencia del simulador de factores humanos

Energy Technology Data Exchange (ETDEWEB)

Moragas, F.

2015-07-01

En 2011, ANAV started the exploitation of the Human Factors Simulator installed in TECNATOM Training Center located in L'Hospital de L'Infant Tarragona. AVAN's Strategic Plan includes the Action Plan for the improvement of human behavior. The plan includes improving the efficiency of the efficiency of the human factors simulator. It is proposed to improve the efficiency into two different terms: winning effectiveness in modeling behaviors, and interweaving the activities in the simulator with the actual strategy of promoting Safety culture and human behaviour. (Author)

Monetary policy and the causality between inflation and money supply in Indonesia

Directory of Open Access Journals (Sweden)

Gatot Sasongko

2018-05-01

Full Text Available Conceptually and empirically, inflation volatility in Indonesia is a monetary and fiscal phenomenon. This study focuses on the macroeconomic policy and public policy especially causality between two variables namely inflation and money supply in Indonesia. This study uses Indonesian macroeconomic data of inflation and money supply from the Bank of Indonesia publication during 2007.1–2017.7. Inflation is measured by the consumer price index, reflects the annual percentage change in costs of acquiring a basket of goods and services to the average consumers that may change at specified intervals. Meanwhile, money supply is measured by the currency, demand deposits, time deposits, and saving deposits. Methodically, this study uses the Granger Causality model to determine the causality between inflation and money supply. The results show that there is a one-way causality between inflation and money supply in Indonesia. These findings imply that money supply causes inflation, but not vice versa. This condition implies that the role of Indonesian Government and Bank of Indonesia were very crucial in managing and controlling macroeconomic policy and public policy. Then, analysis of money supply and inflation also related to impacting factors such as money laundering, role of banks, taxation, tax evasion, and corruption.

Causal inference in biology networks with integrated belief propagation.

Science.gov (United States)

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

A general, multivariate definition of causal effects in epidemiology.

Science.gov (United States)

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.

Human Reliability Analysis for Design: Using Reliability Methods for Human Factors Issues

Energy Technology Data Exchange (ETDEWEB)

Ronald Laurids Boring

2010-11-01

This paper reviews the application of human reliability analysis methods to human factors design issues. An application framework is sketched in which aspects of modeling typically found in human reliability analysis are used in a complementary fashion to the existing human factors phases of design and testing. The paper provides best achievable practices for design, testing, and modeling. Such best achievable practices may be used to evaluate and human system interface in the context of design safety certifications.

Human Reliability Analysis for Design: Using Reliability Methods for Human Factors Issues

International Nuclear Information System (INIS)

Boring, Ronald Laurids

2010-01-01

This paper reviews the application of human reliability analysis methods to human factors design issues. An application framework is sketched in which aspects of modeling typically found in human reliability analysis are used in a complementary fashion to the existing human factors phases of design and testing. The paper provides best achievable practices for design, testing, and modeling. Such best achievable practices may be used to evaluate and human system interface in the context of design safety certifications.

US Nuclear Regulatory Commission human-factors program plan

International Nuclear Information System (INIS)

1983-08-01

The purpose of the NRC Human Factors Program Plan is to ensure that proper consideration is given to human factors in the design, operation, and maintenance of nuclear facilities. This initial plan addresses nuclear power plants (NPP) and describes (1) the technical assistance and research activities planned to provide the technical bases for the resolution of the remaining human factors related tasks described in NUREG-0660, The NRC Action Plan Developed as a Result of the TMI-2 Accident, and NUREG-0737, Clarification of TMI Action Plan Requirements, and (2) the additional human factors efforts identified during implementation of the Action Plan that should receive NRC attention. The plan represents a systematic and comprehensive approach for addressing human factors concerns important to NPP safety in the FY-83 through FY-85 time frame

Causal assessment of dietary acid load and bone disease: a systematic review & meta-analysis applying Hill's epidemiologic criteria for causality

Directory of Open Access Journals (Sweden)

Lyon Andrew W

2011-04-01

Full Text Available Abstract Background Modern diets have been suggested to increase systemic acid load and net acid excretion. In response, alkaline diets and products are marketed to avoid or counteract this acid, help the body regulate its pH to prevent and cure disease. The objective of this systematic review was to evaluate causal relationships between dietary acid load and osteoporosis using Hill's criteria. Methods Systematic review and meta-analysis. We systematically searched published literature for randomized intervention trials, prospective cohort studies, and meta-analyses of the acid-ash or acid-base diet hypothesis with bone-related outcomes, in which the diet acid load was altered, or an alkaline diet or alkaline salts were provided, to healthy human adults. Cellular mechanism studies were also systematically examined. Results Fifty-five of 238 studies met the inclusion criteria: 22 randomized interventions, 2 meta-analyses, and 11 prospective observational studies of bone health outcomes including: urine calcium excretion, calcium balance or retention, changes of bone mineral density, or fractures, among healthy adults in which acid and/or alkaline intakes were manipulated or observed through foods or supplements; and 19 in vitro cell studies which examined the hypothesized mechanism. Urine calcium excretion rates were consistent with osteoporosis development; however calcium balance studies did not demonstrate loss of whole body calcium with higher net acid excretion. Several weaknesses regarding the acid-ash hypothesis were uncovered: No intervention studies provided direct evidence of osteoporosis progression (fragility fractures, or bone strength as measured using biopsy. The supporting prospective cohort studies were not controlled regarding important osteoporosis risk factors including: weight loss during follow-up, family history of osteoporosis, baseline bone mineral density, and estrogen status. No study revealed a biologic mechanism

Human factors in aviation

National Research Council Canada - National Science Library

Salas, Eduardo; Maurino, Daniel E

2010-01-01

.... HFA offers a comprehensive overview of the topic, taking readers from the general to the specific, first covering broad issues, then the more specific topics of pilot performance, human factors...

Human factors methods for nuclear control room design. Volume 2. Human factors survey of control room design practices

International Nuclear Information System (INIS)

Seminara, J.L.; Parsons, S.O.

1979-11-01

An earlier review of the control rooms of operating nuclear power plants identified many design problems having potential for degrading operator performance. As a result, the formal application of human factors principles was found to be needed. This report demonstrates the use of human factors in the design of power plant control rooms. The approaches shown in the report can be applied to operating power plants, as well as to those in the design stage. This study documents human factors techniques required to provide a sustained concern for the man-machine interface from control room concept definition to system implementation

Information causality from an entropic and a probabilistic perspective

International Nuclear Information System (INIS)

Al-Safi, Sabri W.; Short, Anthony J.

2011-01-01

The information causality principle is a generalization of the no-signaling principle which implies some of the known restrictions on quantum correlations. But despite its clear physical motivation, information causality is formulated in terms of a rather specialized game and figure of merit. We explore different perspectives on information causality, discussing the probability of success as the figure of merit, a relation between information causality and the nonlocal ''inner-product game,'' and the derivation of a quadratic bound for these games. We then examine an entropic formulation of information causality with which one can obtain the same results, arguably in a simpler fashion.

The importance of residues 195-206 of human blood clotting factor VII in the interaction of factor VII with tissue factor

International Nuclear Information System (INIS)

Wildgoose, P.; Kisiel, W.; Kazim, A.L.

1990-01-01

Previous studies indicated that human and bovine factor VII exhibit 71% amino acid sequence identity. In the present study, competition binding experiments revealed that the interaction of human factor VII with cell-surface human tissue factor was not inhibited by 100-fold molar excess of bovine factor VII. This finding indicated that bovine and human factor VII are not structurally homologous in the region(s) where human factor VII interacts with human tissue factor. On this premise, the authors synthesized three peptides corresponding to regions of human factor VII that exhibited marked structural dissimilarity to bovine factor VII; these regions of dissimilarity included residues 195-206, 263-274, and 314-326. Peptide 195-206 inhibited the interaction of factor VII with cell-surface tissue factor and the activation of factor X by a complex of factor VIIa and tissue factor half-maximally at concentrations of 1-2 mM. A structurally rearranged form of peptide 195-206 containing an aspartimide residue inhibited these reactions half-maximally at concentrations of 250-300 μM. In contrast, neither peptide 263-274 nor peptide 314-326, at 2 mM concentration, significantly affected either factor VIIa interaction with tissue factor or factor VIIa-mediated activation of factor X. The data provide presumptive evidence that residues 195-206 of human factor VII are involved in the interaction of human factor VII with the extracellular domain of human tissue factor apoprotein

Causal Mediation Analysis: Warning! Assumptions Ahead

Science.gov (United States)

Keele, Luke

2015-01-01

In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…

Meeting Human Reliability Requirements through Human Factors Design, Testing, and Modeling

Energy Technology Data Exchange (ETDEWEB)

R. L. Boring

2007-06-01

In the design of novel systems, it is important for the human factors engineer to work in parallel with the human reliability analyst to arrive at the safest achievable design that meets design team safety goals and certification or regulatory requirements. This paper introduces the System Development Safety Triptych, a checklist of considerations for the interplay of human factors and human reliability through design, testing, and modeling in product development. This paper also explores three phases of safe system development, corresponding to the conception, design, and implementation of a system.

Importance of human factors on nuclear installations safety

International Nuclear Information System (INIS)

Caruso, G.J.

1990-01-01

Actually, installations safety and, in particular the nuclear installations infer a strong incidence in human factors related to the design and operation of such installations. In general, the experience aims to that the most important accidents have happened as result of the components' failures combination and human failures in the operation of safety systems. Human factors in the nuclear installations may be divided into two areas: economy and human reliability. Human factors treatments for the safety evaluation of the nuclear installations allow to diagnose the weak points of man-machine interaction. (Author) [es

How to Be Causal: Time, Spacetime and Spectra

Science.gov (United States)

Kinsler, Paul

2011-01-01

I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…

On the road toward formal reasoning: reasoning with factual causal and contrary-to-fact causal premises during early adolescence.

Science.gov (United States)

Markovits, Henry

2014-12-01

Understanding the development of conditional (if-then) reasoning is critical for theoretical and educational reasons. Here we examined the hypothesis that there is a developmental transition between reasoning with true and contrary-to-fact (CF) causal conditionals. A total of 535 students between 11 and 14 years of age received priming conditions designed to encourage use of either a true or CF alternatives generation strategy and reasoning problems with true causal and CF causal premises (with counterbalanced order). Results show that priming had no effect on reasoning with true causal premises. By contrast, priming with CF alternatives significantly improved logical reasoning with CF premises. Analysis of the effect of order showed that reasoning with CF premises reduced logical responding among younger students but had no effect among older students. Results support the idea that there is a transition in the reasoning processes in this age range associated with the nature of the alternatives generation process required for logical reasoning with true and CF causal conditionals. Copyright © 2014 Elsevier Inc. All rights reserved.

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...

Human factors methods for nuclear control room design. Volume I. Human factors enhancement of existing nuclear control rooms. Final report

International Nuclear Information System (INIS)

Seminara, J.L.; Seidenstein, S.; Eckert, S.K.; Smith, D.L.

1979-11-01

Human factors engineering is an interdisciplinary specialty concerned with influencing the design of equipment systems, facilities, and operational environments to promote safe, efficient, and reliable operator performance. Human factors approaches were applied in the design of representative nuclear power plant control panels. First, methods for upgrading existing operational control panels were examined. Then, based on detailed human factors analyses of operator information and control requirements, designs of reactor, feedwater, and turbine-generator control panels were developed to improve the operator-control board interface, thereby reducing the potential for operator errors. In addition to examining present-generation concepts, human factors aspects of advanced systems and of hybrid combinations of advanced and conventional designs were investigated. Special attention was given to warning system designs. Also, a survey was conducted among control board designers to (1) develop an overview of design practices in the industry, and (2) establish appropriate measures leading to a more systematic concern for human factors in control board design

Human Factors Interface with Systems Engineering for NASA Human Spaceflights

Science.gov (United States)

Wong, Douglas T.

2009-01-01

This paper summarizes the past and present successes of the Habitability and Human Factors Branch (HHFB) at NASA Johnson Space Center s Space Life Sciences Directorate (SLSD) in including the Human-As-A-System (HAAS) model in many NASA programs and what steps to be taken to integrate the Human-Centered Design Philosophy (HCDP) into NASA s Systems Engineering (SE) process. The HAAS model stresses systems are ultimately designed for the humans; the humans should therefore be considered as a system within the systems. Therefore, the model places strong emphasis on human factors engineering. Since 1987, the HHFB has been engaging with many major NASA programs with much success. The HHFB helped create the NASA Standard 3000 (a human factors engineering practice guide) and the Human Systems Integration Requirements document. These efforts resulted in the HAAS model being included in many NASA programs. As an example, the HAAS model has been successfully introduced into the programmatic and systems engineering structures of the International Space Station Program (ISSP). Success in the ISSP caused other NASA programs to recognize the importance of the HAAS concept. Also due to this success, the HHFB helped update NASA s Systems Engineering Handbook in December 2007 to include HAAS as a recommended practice. Nonetheless, the HAAS model has yet to become an integral part of the NASA SE process. Besides continuing in integrating HAAS into current and future NASA programs, the HHFB will investigate incorporating the Human-Centered Design Philosophy (HCDP) into the NASA SE Handbook. The HCDP goes further than the HAAS model by emphasizing a holistic and iterative human-centered systems design concept.

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.

Entanglement, holography and causal diamonds

Science.gov (United States)

de Boer, Jan; Haehl, Felix M.; Heller, Michal P.; Myers, Robert C.

2016-08-01

We argue that the degrees of freedom in a d-dimensional CFT can be reorganized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2 d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglemententropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

Entanglement, holography and causal diamonds

Energy Technology Data Exchange (ETDEWEB)

Boer, Jan de [Institute of Physics, Universiteit van Amsterdam,Science Park 904, 1090 GL Amsterdam (Netherlands); Haehl, Felix M. [Centre for Particle Theory & Department of Mathematical Sciences, Durham University,South Road, Durham DH1 3LE (United Kingdom); Heller, Michal P.; Myers, Robert C. [Perimeter Institute for Theoretical Physics,31 Caroline Street North, Waterloo, Ontario N2L 2Y5 (Canada)

2016-08-29

We argue that the degrees of freedom in a d-dimensional CFT can be re-organized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglement entropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

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

Science.gov (United States)

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

Human Capital, Social Classes, and the Earnings Determination Process in Brazilian Agriculture.

Science.gov (United States)

Neves, Jorge A.; Haller, Archibald O.; Fernandes, Danielle C.

This paper examines the process of earnings determination in the agricultural sector of Brazil. Among the main causal factors analyzed are human capital (education and work experience), labor market segmentation, gender, social class position, level of development/modernization, and concentration of land ownership. Data on individuals employed in…

Dual Causality and the Autonomy of Biology.

Science.gov (United States)

Bock, Walter J

2017-03-01

Ernst Mayr's concept of dual causality in biology with the two forms of causes (proximate and ultimate) continues to provide an essential foundation for the philosophy of biology. They are equivalent to functional (=proximate) and evolutionary (=ultimate) causes with both required for full biological explanations. The natural sciences can be classified into nomological, historical nomological and historical dual causality, the last including only biology. Because evolutionary causality is unique to biology and must be included for all complete biological explanations, biology is autonomous from the physical sciences.

Can chance cause cancer? A causal consideration.

Science.gov (United States)

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.

Human factors in the Canadian nuclear industry: future needs

International Nuclear Information System (INIS)

Harrison, F.

2008-01-01

Currently the industry is facing refurbishment and new builds. At present most licensees in Canada do not have sufficient numbers of Human Factors staff. As a result, the activities of the CNSC are too often focused on providing guidance regarding the application of Human Factors, in addition to reviewing work submitted by the licensee. Greater efficiencies for both the licensee and the CNSC could be realized if licensee staff had greater Human Factors expertise. Strategies for developing Human Factors expertise should be explored through cooperative partnerships with universities, which could be encouraged to include Human Factors courses specific to nuclear. (author)

Determining Directional Dependency in Causal Associations

Science.gov (United States)

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…