Addressing Omitted Prior Achievement Bias in International Assessments: An Applied Example Using PIRLS-NPD Matched Data

Science.gov (United States)

Caro, Daniel H.; Kyriakides, Leonidas; Televantou, Ioulia

2018-01-01

Omitted prior achievement bias is pervasive in international assessment studies and precludes causal inference. For example, reported negative associations between student-oriented teaching strategies and student performance are against expectations and might actually reflect omitted prior achievement bias. Namely, that these teaching strategies…

Social anxiety is characterized by biased learning about performance and the self.

Science.gov (United States)

Koban, Leonie; Schneider, Rebecca; Ashar, Yoni K; Andrews-Hanna, Jessica R; Landy, Lauren; Moscovitch, David A; Wager, Tor D; Arch, Joanna J

2017-12-01

People learn about their self from social information, and recent work suggests that healthy adults show a positive bias for learning self-related information. In contrast, social anxiety disorder (SAD) is characterized by a negative view of the self, yet what causes and maintains this negative self-view is not well understood. Here the authors use a novel experimental paradigm and computational model to test the hypothesis that biased social learning regarding self-evaluation and self-feelings represents a core feature that distinguishes adults with SAD from healthy controls. Twenty-one adults with SAD and 35 healthy controls (HCs) performed a speech in front of 3 judges. They subsequently evaluated themselves and received performance feedback from the judges and then rated how they felt about themselves and the judges. Affective updating (i.e., change in feelings about the self over time, in response to feedback from the judges) was modeled using an adapted Rescorla-Wagner learning model. HCs demonstrated a positivity bias in affective updating, which was absent in SAD. Further, self-performance ratings revealed group differences in learning from positive feedback-a difference that endured at an average of 1 year follow up. These findings demonstrate the presence and long-term endurance of positively biased social learning about the self among healthy adults, a bias that is absent or reversed among socially anxious adults. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Toward an Instructionally Oriented Theory of Example-Based Learning

Science.gov (United States)

Renkl, Alexander

2014-01-01

Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from…

Using Machine Learning to Predict MCNP Bias

Energy Technology Data Exchange (ETDEWEB)

Grechanuk, Pavel Aleksandrovi [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-01-09

For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental k_eff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.

Disorders of compulsivity: a common bias towards learning habits.

Science.gov (United States)

Voon, V; Derbyshire, K; Rück, C; Irvine, M A; Worbe, Y; Enander, J; Schreiber, L R N; Gillan, C; Fineberg, N A; Sahakian, B J; Robbins, T W; Harrison, N A; Wood, J; Daw, N D; Dayan, P; Grant, J E; Bullmore, E T

2015-03-01

Why do we repeat choices that we know are bad for us? Decision making is characterized by the parallel engagement of two distinct systems, goal-directed and habitual, thought to arise from two computational learning mechanisms, model-based and model-free. The habitual system is a candidate source of pathological fixedness. Using a decision task that measures the contribution to learning of either mechanism, we show a bias towards model-free (habit) acquisition in disorders involving both natural (binge eating) and artificial (methamphetamine) rewards, and obsessive-compulsive disorder. This favoring of model-free learning may underlie the repetitive behaviors that ultimately dominate in these disorders. Further, we show that the habit formation bias is associated with lower gray matter volumes in caudate and medial orbitofrontal cortex. Our findings suggest that the dysfunction in a common neurocomputational mechanism may underlie diverse disorders involving compulsion.

Greek Physical Education Teachers' Gender Biases in Learning and Teaching

Science.gov (United States)

Mouratidou, Katerina; Barkoukis, Vassilis

2018-01-01

Gender biases have often been observed in physical education (PE) classes, as many teachers adopt a male-biased perspective in teaching and learning. This might affect their evaluation of students' behavior and may lead students to accept and reproduce gender biases in other social contexts. The aim of this study was to examine whether PE teachers…

Adult learners in a novel environment use prestige-biased social learning.

Science.gov (United States)

Atkisson, Curtis; O'Brien, Michael J; Mesoudi, Alex

2012-08-13

Social learning (learning from others) is evolutionarily adaptive under a wide range of conditions and is a long-standing area of interest across the social and biological sciences. One social-learning mechanism derived from cultural evolutionary theory is prestige bias, which allows a learner in a novel environment to quickly and inexpensively gather information as to the potentially best teachers, thus maximizing his or her chances of acquiring adaptive behavior. Learners provide deference to high-status individuals in order to ingratiate themselves with, and gain extended exposure to, that individual. We examined prestige-biased social transmission in a laboratory experiment in which participants designed arrowheads and attempted to maximize hunting success, measured in caloric return. Our main findings are that (1) participants preferentially learned from prestigious models (defined as those models at whom others spent longer times looking), and (2) prestige information and success-related information were used to the same degree, even though the former was less useful in this experiment than the latter. We also found that (3) participants were most likely to use social learning over individual (asocial) learning when they were performing poorly, in line with previous experiments, and (4) prestige information was not used more often following environmental shifts, contrary to predictions.  These results support previous discussions of the key role that prestige-biased transmission plays in social learning.

Semi-supervised Eigenvectors for Locally-biased Learning

DEFF Research Database (Denmark)

Hansen, Toke Jansen; Mahoney, Michael W.

2012-01-01

In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that pre-specified target region. Locally-biased problems of t...

Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction.

Science.gov (United States)

Carpenter, Gail A; Gaddam, Sai Chaitanya

2010-04-01

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Two-dimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/. Copyright 2009 Elsevier Ltd. All rights reserved.

Active learning in the presence of unlabelable examples

Science.gov (United States)

Mazzoni, Dominic; Wagstaff, Kiri

2004-01-01

We propose a new active learning framework where the expert labeler is allowed to decline to label any example. This may be necessary because the true label is unknown or because the example belongs to a class that is not part of the real training problem. We show that within this framework, popular active learning algorithms (such as Simple) may perform worse than random selection because they make so many queries to the unlabelable class. We present a method by which any active learning algorithm can be modified to avoid unlabelable examples by training a second classifier to distinguish between the labelable and unlabelable classes. We also demonstrate the effectiveness of the method on two benchmark data sets and a real-world problem.

Radar Rainfall Bias Correction based on Deep Learning Approach

Science.gov (United States)

Song, Yang; Han, Dawei; Rico-Ramirez, Miguel A.

2017-04-01

Radar rainfall measurement errors can be considerably attributed to various sources including intricate synoptic regimes. Temperature, humidity and wind are typically acknowledged as critical meteorological factors in inducing the precipitation discrepancies aloft and on the ground. The conventional practices mainly use the radar-gauge or geostatistical techniques by direct weighted interpolation algorithms as bias correction schemes whereas rarely consider the atmospheric effects. This study aims to comprehensively quantify those meteorological elements' impacts on radar-gauge rainfall bias correction based on a deep learning approach. The deep learning approach employs deep convolutional neural networks to automatically extract three-dimensional meteorological features for target recognition based on high range resolution profiles. The complex nonlinear relationships between input and target variables can be implicitly detected by such a scheme, which is validated on the test dataset. The proposed bias correction scheme is expected to be a promising improvement in systematically minimizing the synthesized atmospheric effects on rainfall discrepancies between radar and rain gauges, which can be useful in many meteorological and hydrological applications (e.g., real-time flood forecasting) especially for regions with complex atmospheric conditions.

Active learning techniques for librarians practical examples

CERN Document Server

Walsh, Andrew

2010-01-01

A practical work outlining the theory and practice of using active learning techniques in library settings. It explains the theory of active learning and argues for its importance in our teaching and is illustrated using a large number of examples of techniques that can be easily transferred and used in teaching library and information skills to a range of learners within all library sectors. These practical examples recognise that for most of us involved in teaching library and information skills the one off session is the norm, so we need techniques that allow us to quickly grab and hold our

Test Framing Generates a Stability Bias for Predictions of Learning by Causing People to Discount their Learning Beliefs

Science.gov (United States)

Ariel, Robert; Hines, Jarrod C.; Hertzog, Christopher

2014-01-01

People estimate minimal changes in learning when making predictions of learning (POLs) for future study opportunities despite later showing increased performance and an awareness of that increase (Kornell & Bjork, 2009). This phenomenon is conceptualized as a stability bias in judgments about learning. We investigated the malleability of this effect, and whether it reflected people’s underlying beliefs about learning. We manipulated prediction framing to emphasize the role of testing vs. studying on memory and directly measured beliefs about multi-trial study effects on learning by having participants construct predicted learning curves before and after the experiment. Mean POLs were more sensitive to the number of study-test opportunities when performance was framed in terms of study benefits rather than testing benefits and POLs reflected pre-existing beliefs about learning. The stability bias is partially due to framing and reflects discounted beliefs about learning benefits rather than inherent belief in the stability of performance. PMID:25067885

Collaborative Learning in Practice : Examples from Natural ...

International Development Research Centre (IDRC) Digital Library (Canada)

1 déc. 2010 ... Couverture du livre Collaborative Learning in Practice: Examples from Natural Resource Management in Asia ... Collaborative Learning in Practice saura intéresser les universitaires, les chercheurs et les étudiants des cycles supérieurs en études du développement, ... Strategic leverage on value chains.

The Use of Learning Study in Designing Examples for Teaching Physics

Science.gov (United States)

Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

2017-07-01

Researchers have consistently demonstrated that studying multiple examples is more effective than studying one example because comparing multiple examples can promote schema construction and facilitate discernment of critical aspects. Teachers, however, are usually absent from those self-led text-based studies. In this experimental study, a learning study approach based on variation theory was adopted to examine the effectiveness of teachers' different ways of designing multiple examples in helping students learn a physics principle. Three hundred and fifty-one tenth-grade students learned to distinguish action-reaction from equilibrium (a) by comparing examples that varied critical aspects first separately and then simultaneously, or (b) by comparing examples that separately varied critical aspects only. Results showed that students with average academic attainment benefited more from comparing examples in the first condition. Students with higher academic attainment learned equally within both conditions. This finding supports the advantage of simultaneous variation. The characteristics of students and instructional support should be taken into account when considering the effectiveness of patterns of variation.

Learning from video modeling examples: Does gender matter?

NARCIS (Netherlands)

Hoogerheide, V.; Loyens, S.M.M.; van Gog, T.

2016-01-01

Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer

Learning from video modeling examples: does gender matter?

NARCIS (Netherlands)

V. Hoogerheide (Vincent); S.M.M. Loyens (Sofie); T.A.J.M. van Gog (Tamara)

2016-01-01

textabstractOnline learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the

Example-based learning: comparing the effects of additionally providing three different integrative learning activities on physiotherapy intervention knowledge.

Science.gov (United States)

Dyer, Joseph-Omer; Hudon, Anne; Montpetit-Tourangeau, Katherine; Charlin, Bernard; Mamede, Sílvia; van Gog, Tamara

2015-03-07

Example-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of problem-solving skills more than studying worked examples alone. Completion examples are worked examples in which some of the solution steps remain unsolved for learners to complete. Providing learners engaged in example-based learning with self-explanation prompts has been shown to foster increased meaningful learning compared to providing no self-explanation prompts. Concept mapping and concept map study are other instructional activities known to promote meaningful learning. This study compares the effects of self-explaining, completing a concept map and studying a concept map on conceptual knowledge and problem-solving skills among novice learners engaged in example-based learning. Ninety-one physiotherapy students were randomized into three conditions. They performed a pre-test and a post-test to evaluate their gains in conceptual knowledge and problem-solving skills (transfer performance) in intervention selection. They studied three pairs of worked/completion examples in a digital learning environment. Worked examples consisted of a written reasoning process for selecting an optimal physiotherapy intervention for a patient. The completion examples were partially worked out, with the last few problem-solving steps left blank for students to complete. The students then had to engage in additional self-explanation, concept map completion or model concept map study in order to synthesize and deepen their knowledge of the key concepts and problem-solving steps. Pre-test performance did not differ among conditions. Post-test conceptual knowledge was higher (P example and completion example strategies to foster intervention selection.

Effect of vicarious fear learning on children's heart rate responses and attentional bias for novel animals.

Science.gov (United States)

Reynolds, Gemma; Field, Andy P; Askew, Chris

2014-10-01

Research with children has shown that vicarious learning can result in changes to 2 of Lang's (1968) 3 anxiety response systems: subjective report and behavioral avoidance. The current study extended this research by exploring the effect of vicarious learning on physiological responses (Lang's final response system) and attentional bias. The study used Askew and Field's (2007) vicarious learning procedure and demonstrated fear-related increases in children's cognitive, behavioral, and physiological responses. Cognitive and behavioral changes were retested 1 week and 1 month later, and remained elevated. In addition, a visual search task demonstrated that fear-related vicarious learning creates an attentional bias for novel animals, which is moderated by increases in fear beliefs during learning. The findings demonstrate that vicarious learning leads to lasting changes in all 3 of Lang's anxiety response systems and is sufficient to create attentional bias to threat in children. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Effects of Example Variability and Prior Knowledge in How Students Learn to Solve Equations

Science.gov (United States)

Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

2014-01-01

Researchers have consistently demonstrated that multiple examples are better than one example in facilitating learning because the comparison evoked by multiple examples supports learning and transfer. However, research outcomes are unclear regarding the effects of example variability and prior knowledge on learning from comparing multiple…

Learning from Video Modeling Examples: Does Gender Matter?

Science.gov (United States)

Hoogerheide, Vincent; Loyens, Sofie M. M.; van Gog, Tamara

2016-01-01

Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer similarity hypothesis suggests that such…

Erratum to: The blocking effect in associative learning involves learned biases in rapid attentional capture.

Science.gov (United States)

2018-04-01

Luque, D., Vadillo, M, A., Gutiérrez-Cobo, M, J., Le Pelley, M, E. (2018). The blocking effect in associative learning involves learned biases in rapid attentional capture. Quarterly Journal of Experimental Psychology, 71, 522-544. doi: 10.1080/17470218.2016.1262435. The above article is part of the Special Issue on Associative Learning (in honour of Nick Mackintosh) and was inadvertently published in the February 2018 issue of Quarterly Journal of Experimental Psychology. After publication of the Special Issue, an online collection on Associative Learning will be created on SAGE Journals and this paper will be included in that collection. The Publisher apologises for this error.

Can Collaborative Learning Improve the Effectiveness of Worked Examples in Learning Mathematics?

Science.gov (United States)

Retnowati, Endah; Ayres, Paul; Sweller, John

2017-01-01

Worked examples and collaborative learning have both been shown to facilitate learning. However, the testing of both strategies almost exclusively has been conducted independently of each other. The main aim of the current study was to examine interactions between these 2 strategies. Two experiments (N = 182 and N = 122) were conducted with…

Effect of Vicarious Fear Learning on Children’s Heart Rate Responses and Attentional Bias for Novel Animals

Science.gov (United States)

2014-01-01

Research with children has shown that vicarious learning can result in changes to 2 of Lang’s (1968) 3 anxiety response systems: subjective report and behavioral avoidance. The current study extended this research by exploring the effect of vicarious learning on physiological responses (Lang’s final response system) and attentional bias. The study used Askew and Field’s (2007) vicarious learning procedure and demonstrated fear-related increases in children’s cognitive, behavioral, and physiological responses. Cognitive and behavioral changes were retested 1 week and 1 month later, and remained elevated. In addition, a visual search task demonstrated that fear-related vicarious learning creates an attentional bias for novel animals, which is moderated by increases in fear beliefs during learning. The findings demonstrate that vicarious learning leads to lasting changes in all 3 of Lang’s anxiety response systems and is sufficient to create attentional bias to threat in children. PMID:25151521

Noise Induces Biased Estimation of the Correction Gain.

Directory of Open Access Journals (Sweden)

Jooeun Ahn

Full Text Available The detection of an error in the motor output and the correction in the next movement are critical components of any form of motor learning. Accordingly, a variety of iterative learning models have assumed that a fraction of the error is adjusted in the next trial. This critical fraction, the correction gain, learning rate, or feedback gain, has been frequently estimated via least-square regression of the obtained data set. Such data contain not only the inevitable noise from motor execution, but also noise from measurement. It is generally assumed that this noise averages out with large data sets and does not affect the parameter estimation. This study demonstrates that this is not the case and that in the presence of noise the conventional estimate of the correction gain has a significant bias, even with the simplest model. Furthermore, this bias does not decrease with increasing length of the data set. This study reveals this limitation of current system identification methods and proposes a new method that overcomes this limitation. We derive an analytical form of the bias from a simple regression method (Yule-Walker and develop an improved identification method. This bias is discussed as one of other examples for how the dynamics of noise can introduce significant distortions in data analysis.

How Effective Is Example Generation for Learning Declarative Concepts?

Science.gov (United States)

Rawson, Katherine A.; Dunlosky, John

2016-01-01

Declarative concepts (i.e., key terms and corresponding definitions for abstract concepts) represent foundational knowledge that students learn in many content domains. Thus, investigating techniques to enhance concept learning is of critical importance. Various theoretical accounts support the expectation that example generation will serve this…

Worked examples are more efficient for learning than high-assistance instructional software

NARCIS (Netherlands)

McLaren, Bruce M.; van Gog, Tamara; Ganoe, Craig; Yaron, David; Karabinos, Michael

2015-01-01

The ‘assistance dilemma’, an important issue in the Learning Sciences, is concerned with how much guidance or assistance should be provided to help students learn. A recent study comparing three high-assistance approaches (worked examples, tutored problems, and erroneous examples) and one

Cognitive Bias for Learning Speech Sounds From a Continuous Signal Space Seems Nonlinguistic

Directory of Open Access Journals (Sweden)

Sabine van der Ham

2015-10-01

Full Text Available When learning language, humans have a tendency to produce more extreme distributions of speech sounds than those observed most frequently: In rapid, casual speech, vowel sounds are centralized, yet cross-linguistically, peripheral vowels occur almost universally. We investigate whether adults’ generalization behavior reveals selective pressure for communication when they learn skewed distributions of speech-like sounds from a continuous signal space. The domain-specific hypothesis predicts that the emergence of sound categories is driven by a cognitive bias to make these categories maximally distinct, resulting in more skewed distributions in participants’ reproductions. However, our participants showed more centered distributions, which goes against this hypothesis, indicating that there are no strong innate linguistic biases that affect learning these speech-like sounds. The centralization behavior can be explained by a lack of communicative pressure to maintain categories.

Reinforcement function design and bias for efficient learning in mobile robots

International Nuclear Information System (INIS)

Touzet, C.; Santos, J.M.

1998-01-01

The main paradigm in sub-symbolic learning robot domain is the reinforcement learning method. Various techniques have been developed to deal with the memorization/generalization problem, demonstrating the superior ability of artificial neural network implementations. In this paper, the authors address the issue of designing the reinforcement so as to optimize the exploration part of the learning. They also present and summarize works relative to the use of bias intended to achieve the effective synthesis of the desired behavior. Demonstrative experiments involving a self-organizing map implementation of the Q-learning and real mobile robots (Nomad 200 and Khepera) in a task of obstacle avoidance behavior synthesis are described. 3 figs., 5 tabs

Example-based learning: Integrating cognitive and social-cognitive research perspectives

NARCIS (Netherlands)

T.A.J.M. van Gog (Tamara); N. Rummel (Nikol)

2010-01-01

textabstractExample-based learning has been studied from different perspectives. Cognitive research has mainly focused on worked examples, which typically provide students with a written worked-out didactical solution to a problem to study. Social-cognitive research has mostly focused on modeling

On-line learning from clustered input examples

NARCIS (Netherlands)

Riegler, Peter; Biehl, Michael; Solla, Sara A.; Marangi, Carmela; Marinaro, Maria; Tagliaferri, Roberto

1996-01-01

We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs are taken from two overlapping clusters of data and the rule is defined through a teacher vector which is in general not aligned with the connection line of the cluster centers. We find that the Hebb

Example-based learning: comparing the effects of additionally providing three different integrative learning activities on physiotherapy intervention knowledge

NARCIS (Netherlands)

J.-O. Dyer (Joseph-Omer); A. Hudon (Anne); K. Montpetit-Tourangeau (Katherine); B. Charlin (Bernard); S. Mamede (Silvia); T.A.J.M. van Gog (Tamara)

2015-01-01

textabstractBACKGROUND: Example-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of

Pay-off-biased social learning underlies the diffusion of novel extractive foraging traditions in a wild primate

Science.gov (United States)

2017-01-01

The type and variety of learning strategies used by individuals to acquire behaviours in the wild are poorly understood, despite the presence of behavioural traditions in diverse taxa. Social learning strategies such as conformity can be broadly adaptive, but may also retard the spread of adaptive innovations. Strategies like pay-off-biased learning, by contrast, are effective at diffusing new behaviour but may perform poorly when adaptive behaviour is common. We present a field experiment in a wild primate, Cebus capucinus, that introduced a novel food item and documented the innovation and diffusion of successful extraction techniques. We develop a multilevel, Bayesian statistical analysis that allows us to quantify individual-level evidence for different social and individual learning strategies. We find that pay-off-biased and age-biased social learning are primarily responsible for the diffusion of new techniques. We find no evidence of conformity; instead rare techniques receive slightly increased attention. We also find substantial and important variation in individual learning strategies that is patterned by age, with younger individuals being more influenced by both social information and their own individual experience. The aggregate cultural dynamics in turn depend upon the variation in learning strategies and the age structure of the wild population. PMID:28592681

Social biases modulate the loss of redundant forms in the cultural evolution of language.

Science.gov (United States)

Roberts, Gareth; Fedzechkina, Maryia

2018-02-01

According to the competitive exclusion principle (Gause, 1934), competition for the same niche must eventually lead one competitor to extinction or the occupation of a new niche. This principle applies in both biology and the cultural evolution of language, where different words and structures compete for the same function or meaning (Aronoff, 2016). Across languages, for example, word order trades off with case marking as a means of indicating who did what to whom in a sentence. Previous experimental work has shed light on how such trade-offs come about as languages adapt to human biases through learning and production, with biases becoming amplified through iterated learning over generations. At the same time, a large body of work has documented the impact of social biases on language change. However, little work has investigated how social biases interact with learning and production biases. In particular, the social dimension of language may provide alternative niches for otherwise redundant forms, preventing or slowing their extinction. We tested this hypothesis in an iterated-learning experiment in which participants were exposed to a language with two dialects, both of which had fixed word order, but differed in whether they employed case markers. In one condition, we biased participants socially towards speakers of the dialect that employed case; in other conditions we provided no bias, or biased participants for or against the dialect without case. As expected under our hypothesis, the use of case markers declined over time in all conditions, but the social bias in favor of case-dialect speakers slowed the decline. Copyright © 2017 Elsevier B.V. All rights reserved.

Example-based learning: Comparing the effects of additionally providing three different integrative learning activities on physiotherapy intervention knowledge Approaches to teaching and learning

NARCIS (Netherlands)

J.-O. Dyer (Joseph-Omer); A. Hudon (Anne); K. Montpetit-Tourangeau (Katherine); B. Charlin (Bernard); S. Mamede (Silvia); T.A.J.M. van Gog (Tamara)

2015-01-01

textabstractBackground: Example-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of

Moral empiricism and the bias for act-based rules.

Science.gov (United States)

Ayars, Alisabeth; Nichols, Shaun

2017-10-01

Previous studies on rule learning show a bias in favor of act-based rules, which prohibit intentionally producing an outcome but not merely allowing the outcome. Nichols, Kumar, Lopez, Ayars, and Chan (2016) found that exposure to a single sample violation in which an agent intentionally causes the outcome was sufficient for participants to infer that the rule was act-based. One explanation is that people have an innate bias to think rules are act-based. We suggest an alternative empiricist account: since most rules that people learn are act-based, people form an overhypothesis (Goodman, 1955) that rules are typically act-based. We report three studies that indicate that people can use information about violations to form overhypotheses about rules. In study 1, participants learned either three "consequence-based" rules that prohibited allowing an outcome or three "act-based" rules that prohibiting producing the outcome; in a subsequent learning task, we found that participants who had learned three consequence-based rules were more likely to think that the new rule prohibited allowing an outcome. In study 2, we presented participants with either 1 consequence-based rule or 3 consequence-based rules, and we found that those exposed to 3 such rules were more likely to think that a new rule was also consequence based. Thus, in both studies, it seems that learning 3 consequence-based rules generates an overhypothesis to expect new rules to be consequence-based. In a final study, we used a more subtle manipulation. We exposed participants to examples act-based or accident-based (strict liability) laws and then had them learn a novel rule. We found that participants who were exposed to the accident-based laws were more likely to think a new rule was accident-based. The fact that participants' bias for act-based rules can be shaped by evidence from other rules supports the idea that the bias for act-based rules might be acquired as an overhypothesis from the

Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples

DEFF Research Database (Denmark)

Thorson, James T.; Kristensen, Kasper

2016-01-01

Statistical models play an important role in fisheries science when reconciling ecological theory with available data for wild populations or experimental studies. Ecological models increasingly include both fixed and random effects, and are often estimated using maximum likelihood techniques...... configurations of an age-structured population dynamics model. This simulation experiment shows that the epsilon-method and the existing bias-correction method perform equally well in data-rich contexts, but the epsilon-method is slightly less biased in data-poor contexts. We then apply the epsilon......-method to a spatial regression model when estimating an index of population abundance, and compare results with an alternative bias-correction algorithm that involves Markov-chain Monte Carlo sampling. This example shows that the epsilon-method leads to a biologically significant difference in estimates of average...

Imitation and Education: A Philosophical Inquiry into Learning by Example

Science.gov (United States)

Warnick, Bryan R.

2008-01-01

"Imitation and Education" provides an in-depth reassessment of learning by example that places imitation in a larger social context. It is the first book to bring together ancient educational thought and startling breakthroughs in the fields of cognitive science, psychology, and philosophy to reconsider how we learn from the lives of…

Personalized Resource Recommendations using Learning from Positive and Unlabeled Examples

Directory of Open Access Journals (Sweden)

Priyank Thakkar

2016-08-01

Full Text Available This paper proposes a novel approach for recommending social resources using learning from positive and unlabeled examples. Bookmarks submitted on social bookmarking system delicious1 and artists on online music system last.fm2 are considered as social resources. The foremost feature of this problem is that there are no labeled negative resources/examples available for learning a recommender/classifier. The memory based collaborative filtering has served as the most widely used algorithm for social resource recommendation. However, its predictions are based on some ad hoc heuristic rules and its success depends on the availability of a critical mass of users. This paper proposes model based two-step techniques to learn a classifier using positive and unlabeled examples to address personalized resource recommendations. In the first step of these techniques, naïve Bayes classifier is employed to identify reliable negative resources. In the second step, to generate effective resource recommender, classification and regression tree and least square support vector machine (LS-SVM are exercised. A direct method based on LS-SVM is also put forward to realize the recommendation task. LS-SVM is customized for learning from positive and unlabeled data. Furthermore, the impact of feature selection on our proposed techniques is also studied. Memory based collaborative filtering as well as our proposed techniques exploit usage data to generate personalized recommendations. Experimental results show that the proposed techniques outperform existing method appreciably.

Bias correction for selecting the minimal-error classifier from many machine learning models.

Science.gov (United States)

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Statics learning from engineering examples

CERN Document Server

Emri, Igor

2016-01-01

This textbook introduces and explains the basic concepts on which statics is based utilizing real engineering examples. The authors emphasize the learning process by showing a real problem, analyzing it, simplifying it, and developing a way to solve it. This feature teaches students intuitive thinking in solving real engineering problems using the fundamentals of Newton’s laws. This book also: · Stresses representation of physical reality in ways that allow students to solve problems and obtain meaningful results · Emphasizes identification of important features of the structure that should be included in a model and which features may be omitted · Facilitates students' understanding and mastery of the "flow of thinking" practiced by professional engineers.

Toward a clarification of the taxonomy of "bias" in epidemiology textbooks.

Science.gov (United States)

Schwartz, Sharon; Campbell, Ulka B; Gatto, Nicolle M; Gordon, Kirsha

2015-03-01

Epidemiology textbooks typically divide biases into 3 general categories-confounding, selection bias, and information bias. Despite the ubiquity of this categorization, authors often use these terms to mean different things. This hinders communication among epidemiologists and confuses students who are just learning about the field. To understand the sources of this problem, we reviewed current general epidemiology textbooks to examine how the authors defined and categorized biases. We found that much of the confusion arises from different definitions of "validity" and from a mixing of 3 overlapping organizational features in defining and differentiating among confounding, selection bias, and information bias: consequence, the result of the problem; cause, the processes that give rise to the problem; and cure, how these biases can be addressed once they occur. By contrast, a consistent taxonomy would provide (1) a clear and consistent definition of what unites confounding, selection bias, and information bias and (2) a clear articulation and consistent application of the feature that distinguishes these categories. Based on a distillation of these textbook discussions, we provide an example of a taxonomy that we think meets these criteria.

Design-related bias in estimates of accuracy when comparing imaging tests: examples from breast imaging research

International Nuclear Information System (INIS)

Houssami, Nehmat; Ciatto, Stefano

2010-01-01

This work highlights concepts on the potential for design-related factors to bias estimates of test accuracy in comparative imaging research. We chose two design factors, selection of eligible subjects and the reference standard, to examine the effect of design limitations on estimates of accuracy. Estimates of sensitivity in a study of the comparative accuracy of mammography and ultrasound differed according to how subjects were selected. Comparison of a new imaging test with an existing test should distinguish whether the new test is to be used as a replacement for, or as an adjunct to, the conventional test, to guide the method for subject selection. Quality of the reference standard, examined in a meta-analysis of preoperative breast MRI, varied across studies and was associated with estimates of incremental accuracy. Potential solutions to deal with the reference standard are outlined where an ideal reference standard may not be available in all subjects. These examples of breast imaging research demonstrate that design-related bias, when comparing a new imaging test with a conventional imaging test, may bias accuracy in a direction that favours the new test by overestimating the accuracy of the new test or by underestimating that of the conventional test. (orig.)

Example-Based Learning in Heuristic Domains: A Cognitive Load Theory Account

Science.gov (United States)

Renkl, Alexander; Hilbert, Tatjana; Schworm, Silke

2009-01-01

One classical instructional effect of cognitive load theory (CLT) is the worked-example effect. Although the vast majority of studies have focused on well-structured and algorithmic sub-domains of mathematics or physics, more recent studies have also analyzed learning with examples from complex domains in which only heuristic solution strategies…

Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design)

NARCIS (Netherlands)

Burgos, Daniel; Hummel, Hans; Tattersall, Colin; Brouns, Francis; Koper, Rob

2007-01-01

Burgos, D., Hummel, H. G. K., Tattersall, C., Brouns, F., & Koper, R. (2009). Design Guidelines for Collaboration and Participation with Examples from the LN4LD (Learning Network for Learning Design). In L. Lockyer, S. Bennett, S. Agostinho & B. Harper (Eds.), Handbook of Research on Learning Design

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

Science.gov (United States)

Cox, Louis Anthony Tony

2015-10-01

Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low-regret learning strategies using regulation of air pollutants with uncertain health effects as an example. © 2015 Society for Risk Analysis.

An introduction to Deep learning on biological sequence data - Examples and solutions

DEFF Research Database (Denmark)

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten

2017-01-01

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use....... Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively...

Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

Science.gov (United States)

Panda, Priyadarshini; Srinivasa, Narayan

2018-01-01

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competitive accuracy with respect to state-of-the-art non-spiking neural models. PMID:29551962

Inductive learning of thyroid functional states using the ID3 algorithm. The effect of poor examples on the learning result.

Science.gov (United States)

Forsström, J

1992-01-01

The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.

Effect of vicarious fear learning on children's heart rate responses and attentional bias for novel animals

OpenAIRE

Reynolds, G; Field, AP; Askew, C

2014-01-01

Research with children has shown that vicarious learning can result in changes to 2 of Lang's (1968) 3 anxiety response systems: subjective report and behavioral avoidance. The current study extended this research by exploring the effect of vicarious learning on physiological responses (Lang's final response system) and attentional bias. The study used Askew and Field's (2007) vicarious learning procedure and demonstrated fear-related increases in children's cognitive, behavioral, and physiol...

Adaptive enhanced sampling by force-biasing using neural networks

Science.gov (United States)

Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Learning from video modeling examples : Effects of seeing the human model's face

NARCIS (Netherlands)

Van Gog, Tamara; Verveer, Ilse; Verveer, Lise

2014-01-01

Video modeling examples in which a human(-like) model shows learners how to perform a task are increasingly used in education, as they have become very easy to create and distribute in e-learning environments. However, little is known about design guidelines to optimize learning from video modeling

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

Energy Technology Data Exchange (ETDEWEB)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brink, Henrik [Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O (Denmark); Long, James P.; Rice, John, E-mail: jwrichar@stat.berkeley.edu [Statistics Department, University of California, Berkeley, CA 94720-7450 (United States)

2012-01-10

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

International Nuclear Information System (INIS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Science.gov (United States)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Learning Argumentation Skills through the Use of Prompts for Self-Explaining Examples

Science.gov (United States)

Schworm, Silke; Renkl, Alexander

2007-01-01

Learning with self-explaining examples is an effective method in well-structured domains. The authors analyzed this method in teaching the complex skill of argumentation, experimentally comparing 4 conditions (N = 71 student teachers) that differed with respect to whether and how the processing of the examples was supported by self-explanation…

How trait anxiety, interpretation bias and memory affect acquired fear in children learning about new animals.

Science.gov (United States)

Field, Zoë C; Field, Andy P

2013-06-01

Cognitive models of vulnerability to anxiety propose that information processing biases such as interpretation bias play a part in the etiology and maintenance of anxiety disorders. However, at present little is known about the role of memory in information processing accounts of child anxiety. The current study investigates the relationships between interpretation biases, memory and fear responses when learning about new stimuli. Children (aged 8-11 years) were presented with ambiguous information regarding a novel animal, and their fear, interpretation bias, and memory for the information was measured. The main findings were: (1) trait anxiety and interpretation bias significantly predicted acquired fear; (2) interpretation bias did not significantly mediate the relationship between trait anxiety and acquired fear; (3) interpretation bias appeared to be a more important predictor of acquired fear than trait anxiety per se; and (4) the relationship between interpretation bias and acquired fear was not mediated by the number of negative memories but was mediated by the number of positive and false-positive memories. The findings suggest that information processing models of child anxiety need to explain the role of positive memory in the formation of fear responses.

Fear acquisition and liking of out-group and in-group members: Learning bias or attention?

Science.gov (United States)

Koenig, Stephan; Nauroth, Peter; Lucke, Sara; Lachnit, Harald; Gollwitzer, Mario; Uengoer, Metin

2017-10-01

The present study explores the notion of an out-group fear learning bias that is characterized by facilitated fear acquisition toward harm-doing out-group members. Participants were conditioned with two in-group and two out-group faces as conditioned stimuli. During acquisition, one in-group and one out-group face was paired with an aversive shock whereas the other in-group and out-group face was presented without shock. Psychophysiological measures of fear conditioning (skin conductance and pupil size) and explicit and implicit liking exhibited increased differential responding to out-group faces compared to in-group faces. However, the results did not clearly indicate that harm-doing out-group members were more readily associated with fear than harm-doing in-group members. In contrast, the out-group face not paired with shock decreased conditioned fear and disliking at least to the same extent that the shock-associated out-group face increased these measures. Based on these results, we suggest an account of the out-group fear learning bias that relates to an attentional bias to process in-group information. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Transition-transversion bias is not universal: a counter example from grasshopper pseudogenes.

Directory of Open Access Journals (Sweden)

Irene Keller

2007-02-01

Full Text Available Comparisons of the DNA sequences of metazoa show an excess of transitional over transversional substitutions. Part of this bias is due to the relatively high rate of mutation of methylated cytosines to thymine. Postmutation processes also introduce a bias, particularly selection for codon-usage bias in coding regions. It is generally assumed, however, that there is a universal bias in favour of transitions over transversions, possibly as a result of the underlying chemistry of mutation. Surprisingly, this underlying trend has been evaluated only in two types of metazoan, namely Drosophila and the Mammalia. Here, we investigate a third group, and find no such bias. We characterize the point substitution spectrum in Podisma pedestris, a grasshopper species with a very large genome. The accumulation of mutations was surveyed in two pseudogene families, nuclear mitochondrial and ribosomal DNA sequences. The cytosine-guanine (CpG dinucleotides exhibit the high transition frequencies expected of methylated sites. The transition rate at other cytosine residues is significantly lower. After accounting for this methylation effect, there is no significant difference between transition and transversion rates. These results contrast with reports from other taxa and lead us to reject the hypothesis of a universal transition/transversion bias. Instead we suggest fundamental interspecific differences in point substitution processes.

Investigating d-cycloserine as a potential pharmacological enhancer of an emotional bias learning procedure.

Science.gov (United States)

Woud, Marcella L; Blackwell, Simon E; Steudte-Schmiedgen, Susann; Browning, Michael; Holmes, Emily A; Harmer, Catherine J; Margraf, Jürgen; Reinecke, Andrea

2018-05-01

The partial N-methyl-D-aspartate receptor agonist d-cycloserine may enhance psychological therapies. However, its exact mechanism of action is still being investigated. Cognitive bias modification techniques allow isolation of cognitive processes and thus investigation of how they may be affected by d-cycloserine. We used a cognitive bias modification paradigm targeting appraisals of a stressful event, Cognitive Bias Modification-Appraisal, to investigate whether d-cycloserine enhanced the modification of appraisal, and whether it caused greater reduction in indices of psychopathology. Participants received either 250 mg of d-cycloserine ( n=19) or placebo ( n=19). As a stressor task, participants recalled a negative life event, followed by positive Cognitive Bias Modification-Appraisal training. Before and after Cognitive Bias Modification-Appraisal, appraisals and indices of psychopathology related to the stressor were assessed. Cognitive Bias Modification-Appraisal successfully modified appraisals, but d-cycloserine did not affect appraisals post-training. There were no post-training group differences in frequency of intrusions. Interestingly, d-cycloserine led to a greater reduction in distress and impact on state mood from recalling the event, and lower distress post-training was associated with fewer intrusions. Therefore, d-cycloserine may affect emotional reactivity to recalling a negative event when combined with induction of a positive appraisal style, but via a mechanism other than enhanced learning of the appraisal style.

Differential-associative processing or example elaboration: Which strategy is best for learning the definitions of related and unrelated concepts?

Science.gov (United States)

Hannon, Brenda

2012-10-01

Definitions of related concepts (e.g., genotype - phenotype ) are prevalent in introductory classes. Consequently, it is important that educators and students know which strategy(s) work best for learning them. This study showed that a new comparative elaboration strategy, called differential-associative processing, was better for learning definitions of related concepts than was an integrative elaborative strategy, called example elaboration. This outcome occurred even though example elaboration was administered in a naturalistic way (Experiment 1) and students spent more time in the example elaboration condition learning (Experiments 1, 2, 3), and generating pieces of information about the concepts (Experiments 2 and 3). Further, with unrelated concepts ( morpheme-fluid intelligence ), performance was similar regardless if students used differential-associative processing or example elaboration (Experiment 3). Taken as a whole, these results suggest that differential-associative processing is better than example elaboration for learning definitions of related concepts and is as good as example elaboration for learning definitions of unrelated concepts.

Learning and Understanding System Stability Using Illustrative Dynamic Texture Examples

Science.gov (United States)

Liu, Huaping; Xiao, Wei; Zhao, Hongyan; Sun, Fuchun

2014-01-01

System stability is a basic concept in courses on dynamic system analysis and control for undergraduate students with computer science backgrounds. Typically, this was taught using a simple simulation example of an inverted pendulum. Unfortunately, many difficult issues arise in the learning and understanding of the concepts of stability,…

Cognitive advantage in bilingualism: an example of publication bias?

Science.gov (United States)

de Bruin, Angela; Treccani, Barbara; Della Sala, Sergio

2015-01-01

It is a widely held belief that bilinguals have an advantage over monolinguals in executive-control tasks, but is this what all studies actually demonstrate? The idea of a bilingual advantage may result from a publication bias favoring studies with positive results over studies with null or negative effects. To test this hypothesis, we looked at conference abstracts from 1999 to 2012 on the topic of bilingualism and executive control. We then determined which of the studies they reported were subsequently published. Studies with results fully supporting the bilingual-advantage theory were most likely to be published, followed by studies with mixed results. Studies challenging the bilingual advantage were published the least. This discrepancy was not due to differences in sample size, tests used, or statistical power. A test for funnel-plot asymmetry provided further evidence for the existence of a publication bias. © The Author(s) 2014.

An Investigation of the Learning Strategies as Bias Factors in Second Language Cloze Tests

Science.gov (United States)

Ajideh, Parviz; Yaghoubi-Notash, Massoud; Khalili, Abdolreza

2017-01-01

The present study investigated the contribution of the EFL students' learning strategies to the explanation of the variance in their results on language tests. More specifically, it examined the role of these strategies as bias factors in the results of English cloze tests. Based on this aim, first, 158 intermediate EFL learners were selected from…

STEM-related, Student-led Service Learning / Community Engagement Projects: Examples and Benefits

Science.gov (United States)

Swap, R. J.; Wayland, K.

2015-12-01

Field-based, STEM-related service learning / community engagement projects present an opportunity for undergraduate students to demonstrate proficiencies related to the process of inquiry. These proficiencies include: appreciation of the larger project context, articulation of an informed question/hypothesis, project proposal development, interdisciplinary collaboration, project management (including planning, implementation reconfiguration and synthesis) and lastly the generation and handing off of acquired knowledge. Calls for these types of proficiencies have been expressed by governmental, non-governmental as well as the private sector. Accordingly, institutions of higher learning have viewed such activities as opportunities for enriching the learning experience for undergraduate students and for making such students more marketable, especially those from STEM-related fields. This institutional interest has provided an opportunity to support and expand field-based learning. Here we present examples of student-led/faculty-mentored international service learning and community engagement projects along the arc of preparation, implementation and post-field process. Representative examples that draw upon environmental science and engineering knowledge have been selected from more than 20 international undergraduate student projects over past decade and include: slow-sand water filtration, rainwater harvesting, methane biodigesters, water reticulation schemes and development and implementation of rocket stoves for communal cooking. We discuss these efforts in terms of the development of the aforementioned proficiencies, the utility of such proficiencies to the larger enterprise of STEM and the potential for transformative student learning outcomes. We share these experiences and lessons learned with the hope that others may intelligently borrow from our approach in a manner appropriate for their particular context.

Do horses with poor welfare show `pessimistic' cognitive biases?

Science.gov (United States)

Henry, S.; Fureix, C.; Rowberry, R.; Bateson, M.; Hausberger, M.

2017-02-01

This field study tested the hypothesis that domestic horses living under putatively challenging-to-welfare conditions (for example involving social, spatial, feeding constraints) would present signs of poor welfare and co-occurring pessimistic judgement biases. Our subjects were 34 horses who had been housed for over 3 years in either restricted riding school situations ( e.g. kept in single boxes, with limited roughage, ridden by inexperienced riders; N = 25) or under more naturalistic conditions ( e.g. access to free-range, kept in stable social groups, leisure riding; N = 9). The horses' welfare was assessed by recording health-related, behavioural and postural indicators. Additionally, after learning a location task to discriminate a bucket containing either edible food (`positive' location) or unpalatable food (`negative' location), the horses were presented with a bucket located near the positive position, near the negative position and halfway between the positive and negative positions to assess their judgement biases. The riding school horses displayed the highest levels of behavioural and health-related problems and a pessimistic judgment bias, whereas the horses living under more naturalistic conditions displayed indications of good welfare and an optimistic bias. Moreover, pessimistic bias data strongly correlated with poor welfare data. This suggests that a lowered mood impacts a non-human species' perception of its environment and highlights cognitive biases as an appropriate tool to assess the impact of chronic living conditions on horse welfare.

Do horses with poor welfare show 'pessimistic' cognitive biases?

Science.gov (United States)

Henry, S; Fureix, C; Rowberry, R; Bateson, M; Hausberger, M

2017-02-01

This field study tested the hypothesis that domestic horses living under putatively challenging-to-welfare conditions (for example involving social, spatial, feeding constraints) would present signs of poor welfare and co-occurring pessimistic judgement biases. Our subjects were 34 horses who had been housed for over 3 years in either restricted riding school situations (e.g. kept in single boxes, with limited roughage, ridden by inexperienced riders; N = 25) or under more naturalistic conditions (e.g. access to free-range, kept in stable social groups, leisure riding; N = 9). The horses' welfare was assessed by recording health-related, behavioural and postural indicators. Additionally, after learning a location task to discriminate a bucket containing either edible food ('positive' location) or unpalatable food ('negative' location), the horses were presented with a bucket located near the positive position, near the negative position and halfway between the positive and negative positions to assess their judgement biases. The riding school horses displayed the highest levels of behavioural and health-related problems and a pessimistic judgment bias, whereas the horses living under more naturalistic conditions displayed indications of good welfare and an optimistic bias. Moreover, pessimistic bias data strongly correlated with poor welfare data. This suggests that a lowered mood impacts a non-human species' perception of its environment and highlights cognitive biases as an appropriate tool to assess the impact of chronic living conditions on horse welfare.

An introduction to deep learning on biological sequence data: examples and solutions.

Science.gov (United States)

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

2017-11-15

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Zen of cloud learning cloud computing by examples on Microsoft Azure

CERN Document Server

Bai, Haishi

2014-01-01

Zen of Cloud: Learning Cloud Computing by Examples on Microsoft Azure provides comprehensive coverage of the essential theories behind cloud computing and the Windows Azure cloud platform. Sharing the author's insights gained while working at Microsoft's headquarters, it presents nearly 70 end-to-end examples with step-by-step guidance on implementing typical cloud-based scenarios.The book is organized into four sections: cloud service fundamentals, cloud solutions, devices and cloud, and system integration and project management. Each chapter contains detailed exercises that provide readers w

Brief learning induces a memory bias for arousing-negative words: an fMRI study in high and low trait anxious persons.

Science.gov (United States)

Eden, Annuschka S; Dehmelt, Vera; Bischoff, Matthias; Zwitserlood, Pienie; Kugel, Harald; Keuper, Kati; Zwanzger, Peter; Dobel, Christian

2015-01-01

Persons suffering from anxiety disorders display facilitated processing of arousing and negative stimuli, such as negative words. This memory bias is reflected in better recall and increased amygdala activity in response to such stimuli. However, individual learning histories were not considered in most studies, a concern that we meet here. Thirty-four female persons (half with high-, half with low trait anxiety) participated in a criterion-based associative word-learning paradigm, in which neutral pseudowords were paired with aversive or neutral pictures, which should lead to a valence change for the negatively paired pseudowords. After learning, pseudowords were tested with fMRI to investigate differential brain activation of the amygdala evoked by the newly acquired valence. Explicit and implicit memory was assessed directly after training and in three follow-ups at 4-day intervals. The behavioral results demonstrate that associative word-learning leads to an explicit (but no implicit) memory bias for negatively linked pseudowords, relative to neutral ones, which confirms earlier studies. Bilateral amygdala activation underlines the behavioral effect: Higher trait anxiety is correlated with stronger amygdala activation for negatively linked pseudowords than for neutrally linked ones. Most interestingly, this effect is also present for negatively paired pseudowords that participants could not remember well. Moreover, neutrally paired pseudowords evoked higher amygdala reactivity than completely novel ones in highly anxious persons, which can be taken as evidence for generalization. These findings demonstrate that few word-learning trials generate a memory bias for emotional stimuli, indexed both behaviorally and neurophysiologically. Importantly, the typical memory bias for emotional stimuli and the generalization to neutral ones is larger in high anxious persons.

Brief learning induces a memory bias for arousing-negative words: An fMRI study in high and low trait anxious persons

Directory of Open Access Journals (Sweden)

Annuschka Salima Eden

2015-08-01

Full Text Available Persons suffering from anxiety disorders display facilitated processing of arousing and negative stimuli, such as negative words. This memory bias is reflected in better recall and increased amygdala activity in response to such stimuli. However, individual learning histories were not considered in most studies, a concern that we meet here. Thirty-four female persons (half with high-, half with low trait anxiety participated in a criterion-based associative word-learning paradigm, in which neutral pseudowords were paired with aversive or neutral pictures, which should lead to a valence change for the negatively paired pseudowords. After learning, pseudowords were tested with fMRI to investigate differential brain activation of the amygdala evoked by the newly acquired valence. Explicit and implicit memory was assessed directly after training and in three follow-ups at four-day intervals. The behavioral results demonstrate that associative word-learning leads to an explicit (but no implicit memory bias for negatively linked pseudowords, relative to neutral ones, which confirms earlier studies. Bilateral amygdala activation underlines the behavioral effect: Higher trait anxiety is correlated with stronger amygdala activation for negatively linked pseudowords than for neutrally linked ones. Most interestingly, this effect is also present for negatively paired pseudowords that participants could not remember well. Moreover, neutrally paired pseudowords evoked higher amygdala reactivity than completely novel ones in highly anxious persons, which can be taken as evidence for generalization. These findings demonstrate that few word-learning trials generate a memory bias for emotional stimuli, indexed both behaviorally and neurophysiologically. Importantly, the typical memory bias for emotional stimuli and the generalization to neutral ones is larger in high anxious persons.

Python for probability, statistics, and machine learning

CERN Document Server

Unpingco, José

2016-01-01

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowl...

Example and Non-Example Pada Pembelajaran Matematika

OpenAIRE

Yunarto, Wanda Nugroho

2016-01-01

Abstrak Example and Non-Example Learning Model merupakan model pembelajaran yang menggunakan gambar sebagai media pembelajaran yang bertujuan mendorong mahasiswa untuk belajar berfikir kritis dengan jalan memecahkan permasalahan-permasalahan yang terkandung dalam contoh-contoh permasalahan/ konsep yang disajikan. Tujuan dari penelitian ini adalah mendapatkan gambaran mengenai bagaimana penerapan model pembelajaran Example and non-Example pada mahasiswa program studi Pendidikan Matematika Univ...

Statistical physics of learning from examples: a brief introduction

International Nuclear Information System (INIS)

Broeck, C. van den

1994-01-01

The problem of how one can learn from examples is illustrated on the case of a student perception trained by the Hebb rule on examples generated by a teacher perception. Two basic quantities are calculated: the training error and the generalization error. The obtained results are found to be typical. Other training rules are discussed. For the case of an Ising student with an Ising teacher, the existence of a first order phase transition is shown. Special effects such as dilution, queries, rejection, etc. are discussed and some results for multilayer networks are reviewed. In particular, the properties of a self-similar committee machine are derived. Finally, we discuss the statistic of generalization, with a review of the Hoeffding inequality, the Dvoretzky Kiefer Wolfowitz theorem and the Vapnik Chervonenkis theorem. (author). 29 refs, 6 figs

An experimental examination of catastrophizing-related interpretation bias for ambiguous facial expressions of pain using an incidental learning task

Directory of Open Access Journals (Sweden)

Ali eKHATIBI

2014-09-01

Full Text Available Individuals with pain-related concerns are likely to interpret ambiguous pain-related information in a threatening manner. It is unknown whether this interpretation bias also occurs for ambiguous pain-related facial expressions. This study examined whether individuals who habitually attach a catastrophic meaning to pain are characterized by negative interpretation bias for ambiguous pain-related facial expressions. Sixty-four female undergraduates completed an incidental learning task during which pictures of faces were presented, each followed by a visual target at one of two locations. Participants indicated target location by pressing one of two response keys. During the learning phase, happy and painful facial expressions predicted target location. During two test phases, morphed facial expressions of pain and happiness were added, equally often followed by a target at either location. Faster responses following morphs to targets at the location predicted by painful expressions compared to targets at the location predicted by happy expressions were taken to reflect pain-related interpretation bias. During one test phase, faces were preceded by either a safe or threatening context cue. High, but not low, pain-catastrophizers responded faster following morphs to targets at the location predicted by painful expressions than to targets at the other location (when participants were aware of the contingency between expression type and target location. When context cues were presented, there was no indication of interpretation bias. Participants were also asked to directly classify the facial expressions that were presented during the incidental learning task. Participants classified morphs more often as happy than as painful, independent of their level of pain catastrophizing. This observation is discussed in terms of differences between indirect and direct measures of interpretation bias.

Effects of creating video-based modeling examples on learning and transfer

NARCIS (Netherlands)

Hoogerheide, Vincent; Loyens, Sofie M M; van Gog, Tamara

2014-01-01

Two experiments investigated whether acting as a peer model for a video-based modeling example, which entails studying a text with the intention to explain it to others and then actually explaining it on video, would foster learning and transfer. In both experiments, novices were instructed to study

Issues in Institutional Benchmarking of Student Learning Outcomes Using Case Examples

Science.gov (United States)

Judd, Thomas P.; Pondish, Christopher; Secolsky, Charles

2013-01-01

Benchmarking is a process that can take place at both the inter-institutional and intra-institutional level. This paper focuses on benchmarking intra-institutional student learning outcomes using case examples. The findings of the study illustrate the point that when the outcomes statements associated with the mission of the institution are…

COOPERATIVE LEARNING AS A MEANS OF STIMULATING LIFE SKILLS IN PROFESSIONALLY-BIASED FOREIGN LANGUAGE TEACHING

Directory of Open Access Journals (Sweden)

Alexander Komarov

2016-02-01

Full Text Available The article is devoted to the contribution of foreign language learning to stimulating students’ life skills at non-linguistic higher institutions. In the article, the author considers the possibilities of motivating students to exercise life skills in the process of foreign language instruction. The author analyses the cooperative learning technology as a means that enables students to be involved into interaction with one another as well as it develops their team-building skills to successfully cooperate and communicate with each other. The author describes different forms of cooperative leaning, which give students an opportunity to mutually enrich and complement each other’s skills in foreign language learning. The author argues that cooperative learning technology stimulates the students’ existent life skills and makes them work in the process of professionally-biased instruction of a foreign language.

Effects of Worked Examples, Example-Problem Pairs, and Problem-Example Pairs Compared to Problem Solving

NARCIS (Netherlands)

Van Gog, Tamara; Kester, Liesbeth; Paas, Fred

2010-01-01

Van Gog, T., Kester, L., & Paas, F. (2010, August). Effects of worked examples, example-problem pairs, and problem-example pairs compared to problem solving. Paper presented at the Biannual EARLI SIG meeting of Instructional design and Learning and instruction with computers, Ulm, Germany.

Bounding the bias of contrastive divergence learning

DEFF Research Database (Denmark)

Fischer, Anja; Igel, Christian

2011-01-01

Optimization based on k-step contrastive divergence (CD) has become a common way to train restricted Boltzmann machines (RBMs). The k-step CD is a biased estimator of the log-likelihood gradient relying on Gibbs sampling. We derive a new upper bound for this bias. Its magnitude depends on k...

Gender bias in Islamic textbooks for Muslim children in Indonesia

Directory of Open Access Journals (Sweden)

Suwardi Suwardi

2018-01-01

Full Text Available Textbook has a strong influence on the formation of children’s attitudes and value system. Therefore, Islamic textbooks as the main learning source for Muslim children in Indonesia need to consider the gender equality. This is very important to note, because feminists often view that Islam contains teachings of gender inequality. Islam places men in the higher position, while women are placed in the lower position. For example, men can be imam for women in prayer, but women cannot be imam for men. It is easier for children to learn textbook material presented in pictures. Therefore, the pictures presented in Islamic textbooks ideally do not contain gender bias. So, a research is needed to know if there is gender bias in the pictures presented in Islamic textbooks taught to Muslim children in Indonesia. To prove it, a literary research is conducted on the Islamic textbooks taught to the first grade Muslim student of Islamic Elementary School/ Madrasah Ibtidaiyah (MI in Indonesia which includes pictures in their teaching materials. Islamic textbooks studied in the research include Fikih, Akidah Akhlak, and Arabic textbooks. The results of this study conclude that the pictures presented in Islamic textbooks taught in Muslim children in Indonesia contain gender bias. The man favor pictures are more than those of woman favor. Based on the conclusion, this study recommends an improvement of pictures presented in Islamic textbooks taught to Muslim children in Indonesia.

Harmonic biases in child learners: In support of language universals

Science.gov (United States)

Culbertson, Jennifer; Newport, Elissa L.

2015-01-01

A fundamental question for cognitive science concerns the ways in which languages are shaped by the biases of language learners. Recent research using laboratory language learning paradigms, primarily with adults, has shown that structures or rules that are common in the languages of the world are learned or processed more easily than patterns that are rare or unattested. Here we target child learners, investigating a set of biases for word order learning in the noun phrase studied by Culbertson, Smolensky & Legendre (2012) in college-age adults. We provide the first evidence that child learners exhibit a preference for typologically common harmonic word order patterns—those which preserve the order of the head with respect to its complements—validating the psychological reality of a principle formalized in many different linguistic theories. We also discuss important differences between child and adult learners in terms of both the strength and content of the biases at play during language learning. In particular, the bias favoring harmonic patterns is markedly stronger in children than adults, and children (unlike adults) acquire adjective ordering more readily than numeral ordering. The results point to the importance of investigating learning biases across development in order to understand how these biases may shape the history and structure of natural languages. PMID:25800352

Adaptive social learning strategies in temporally and spatially varying environments : how temporal vs. spatial variation, number of cultural traits, and costs of learning influence the evolution of conformist-biased transmission, payoff-biased transmission, and individual learning.

Science.gov (United States)

Nakahashi, Wataru; Wakano, Joe Yuichiro; Henrich, Joseph

2012-12-01

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial-but not temporal-variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing.

Is racial bias malleable? Whites' lay theories of racial bias predict divergent strategies for interracial interactions.

Science.gov (United States)

Neel, Rebecca; Shapiro, Jenessa R

2012-07-01

How do Whites approach interracial interactions? We argue that a previously unexamined factor-beliefs about the malleability of racial bias-guides Whites' strategies for difficult interracial interactions. We predicted and found that those who believe racial bias is malleable favor learning-oriented strategies such as taking the other person's perspective and trying to learn why an interaction is challenging, whereas those who believe racial bias is fixed favor performance-oriented strategies such as overcompensating in the interaction and trying to end the interaction as quickly as possible. Four studies support these predictions. Whether measured (Studies 1, 3, and 4) or manipulated (Study 2), beliefs that racial bias is fixed versus malleable yielded these divergent strategies for difficult interracial interactions. Furthermore, beliefs about the malleability of racial bias are distinct from related constructs (e.g., prejudice and motivations to respond without prejudice; Studies 1, 3, and 4) and influence self-reported (Studies 1-3) and actual (Study 4) strategies in imagined (Studies 1-2) and real (Studies 3-4) interracial interactions. Together, these findings demonstrate that beliefs about the malleability of racial bias influence Whites' approaches to and strategies within interracial interactions. PsycINFO Database Record (c) 2012 APA, all rights reserved

Harmonic biases in child learners: in support of language universals.

Science.gov (United States)

Culbertson, Jennifer; Newport, Elissa L

2015-06-01

A fundamental question for cognitive science concerns the ways in which languages are shaped by the biases of language learners. Recent research using laboratory language learning paradigms, primarily with adults, has shown that structures or rules that are common in the languages of the world are learned or processed more easily than patterns that are rare or unattested. Here we target child learners, investigating a set of biases for word order learning in the noun phrase studied by Culbertson, Smolensky, and Legendre (2012) in college-age adults. We provide the first evidence that child learners exhibit a preference for typologically common harmonic word order patterns-those which preserve the order of the head with respect to its complements-validating the psychological reality of a principle formalized in many different linguistic theories. We also discuss important differences between child and adult learners in terms of both the strength and content of the biases at play during language learning. In particular, the bias favoring harmonic patterns is markedly stronger in children than adults, and children (unlike adults) acquire adjective ordering more readily than numeral ordering. The results point to the importance of investigating learning biases across development in order to understand how these biases may shape the history and structure of natural languages. Copyright © 2015 Elsevier B.V. All rights reserved.

Workplace Learning by Action Learning: A Practical Example.

Science.gov (United States)

Miller, Peter

2003-01-01

An action learning approach to help managers enhance learning capacity involved a performance management seminar, work by action learning sets, implementation of a new performance management instrument with mentoring by action learning facilitators, and evaluation. Survey responses from 392 participants revealed satisfaction with managerial…

Beyond assembly bias: exploring secondary halo biases for cluster-size haloes

Science.gov (United States)

Mao, Yao-Yuan; Zentner, Andrew R.; Wechsler, Risa H.

2018-03-01

Secondary halo bias, commonly known as `assembly bias', is the dependence of halo clustering on a halo property other than mass. This prediction of the Λ Cold Dark Matter cosmology is essential to modelling the galaxy distribution to high precision and interpreting clustering measurements. As the name suggests, different manifestations of secondary halo bias have been thought to originate from halo assembly histories. We show conclusively that this is incorrect for cluster-size haloes. We present an up-to-date summary of secondary halo biases of high-mass haloes due to various halo properties including concentration, spin, several proxies of assembly history, and subhalo properties. While concentration, spin, and the abundance and radial distribution of subhaloes exhibit significant secondary biases, properties that directly quantify halo assembly history do not. In fact, the entire assembly histories of haloes in pairs are nearly identical to those of isolated haloes. In general, a global correlation between two halo properties does not predict whether or not these two properties exhibit similar secondary biases. For example, assembly history and concentration (or subhalo abundance) are correlated for both paired and isolated haloes, but follow slightly different conditional distributions in these two cases. This results in a secondary halo bias due to concentration (or subhalo abundance), despite the lack of assembly bias in the strict sense for cluster-size haloes. Due to this complexity, caution must be exercised in using any one halo property as a proxy to study the secondary bias due to another property.

Revisiting the Pink Triangle Exercise: An Exploration of Experiential Learning in Graduate Social Work Education

Science.gov (United States)

Pugh, Greg L.

2014-01-01

The pink triangle exercise is an example of an experiential learning exercise that creates cognitive dissonance and deep learning of unrealized internalized biases among social work students. Students wear a button with a pink triangle on it for 1 day and write a reflection paper. The exercise increases self-awareness, cultural competence, and the…

Learning with multiple representations: an example of a revision lesson in mechanics

Science.gov (United States)

Wong, Darren; Poo, Sng Peng; Eng Hock, Ng; Loo Kang, Wee

2011-03-01

We describe an example of learning with multiple representations in an A-level revision lesson on mechanics. The context of the problem involved the motion of a ball thrown vertically upwards in air and studying how the associated physical quantities changed during its flight. Different groups of students were assigned to look at the ball's motion using various representations: motion diagrams, vector diagrams, free-body diagrams, verbal description, equations and graphs, drawn against time as well as against displacement. Overall, feedback from students about the lesson was positive. We further discuss the benefits of using computer simulation to support and extend student learning.

Reflecting Equity and Diversity. Part I: Guidelines and Procedure for Evaluating Bias in Instructional Materials. Part II: Bias Awareness Training Worksheets. Part III: Bias Awareness and Procedure Training Course.

Science.gov (United States)

Bebermeyer, Jim; Edmond, Mary, Ed.

Reflecting a need to prepare students for working in diverse organizations, this document was developed to increase school officials' awareness of bias in instructional materials and help them select bias-free materials. A number of the examples illustrate situations dealing with diversity in the workplace. The guide is divided into three parts:…

Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory

Science.gov (United States)

Koenig, Stephan; Uengoer, Metin; Lachnit, Harald

2017-01-01

We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L), partial (P), and high (H) shock expectancy, respectively. The cues differed with respect to the strength of their shock association (L H). During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues. After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically. Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention. PMID:28588466

Social inference and social anxiety: evidence of a fear-congruent self-referential learning bias.

Science.gov (United States)

Button, Katherine S; Browning, Michael; Munafò, Marcus R; Lewis, Glyn

2012-12-01

Fears of negative evaluation characterise social anxiety, and preferential processing of fear-relevant information is implicated in maintaining symptoms. Little is known, however, about the relationship between social anxiety and the process of inferring negative evaluation. The ability to use social information to learn what others think about one, referred to here as self-referential learning, is fundamental for effective social interaction. The aim of this research was to examine whether social anxiety is associated with self-referential learning. 102 Females with either high (n = 52) or low (n = 50) self-reported social anxiety completed a novel probabilistic social learning task. Using trial and error, the task required participants to learn two self-referential rules, 'I am liked' and 'I am disliked'. Participants across the sample were better at learning the positive rule 'I am liked' than the negative rule 'I am disliked', β = -6.4, 95% CI [-8.0, -4.7], p learning positive self-referential information was strongest in the lowest socially anxious and was abolished in the most symptomatic participants. Relative to the low group, the high anxiety group were better at learning they were disliked and worse at learning they were liked, social anxiety by rule interaction β = 3.6; 95% CI [+0.3, +7.0], p = 0.03. The specificity of the results to self-referential processing requires further research. Healthy individuals show a robust preference for learning that they are liked relative to disliked. This positive self-referential bias is reduced in social anxiety in a way that would be expected to exacerbate anxiety symptoms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Worked Examples with Errors: When Self-Explanation Prompts Hinder Learning of Teachers Diagnostic Competences on Problem-Based Learning

Science.gov (United States)

Heitzmann, Nicole; Fischer, Frank; Fischer, Martin R.

2018-01-01

To diagnose classroom situations is crucial for teachers' everyday practice. The approach of worked examples with errors seems promising to support the diagnosis of classroom situations in student teachers (Stark et al. in "Learn Instr" 21(1):22-33, 2011). To enhance that approach, error-explanation prompts and an adaptable feedback…

Learning with Multiple Representations: An Example of a Revision Lesson in Mathematics

Science.gov (United States)

Wong, Darren; Poo, Sng Peng; Hock, Ng Eng; Kang, Wee Loo

2011-01-01

We describe an example of learning with multiple representations in an A-level revision lesson on mechanics. The context of the problem involved the motion of a ball thrown vertically upwards in air and studying how the associated physical quantities changed during its flight. Different groups of students were assigned to look at the ball's motion…

A test to identify judgement bias in mice

NARCIS (Netherlands)

Boleij, H.; van't Klooster, J.; Lavrijsen, M.; Kirchhoff, S.; Arndt, S.S.; Ohl, F.

2012-01-01

Emotional states are known to affect cognitive processes. For example highly anxious individuals interpret ambiguous stimuli more negatively than low anxious people, an effect called negative judgement bias. Recently, the measurement of judgement bias has been used to try and indicate emotional

Assessing the Potential for Bias From Nonresponse to a Study Follow-up Interview: An Example From the Agricultural Health Study.

Science.gov (United States)

Rinsky, Jessica L; Richardson, David B; Wing, Steve; Beard, John D; Alavanja, Michael; Beane Freeman, Laura E; Chen, Honglei; Henneberger, Paul K; Kamel, Freya; Sandler, Dale P; Hoppin, Jane A

2017-08-15

Prospective cohort studies are important tools for identifying causes of disease. However, these studies are susceptible to attrition. When information collected after enrollment is through interview or exam, attrition leads to missing information for nonrespondents. The Agricultural Health Study enrolled 52,394 farmers in 1993-1997 and collected additional information during subsequent interviews. Forty-six percent of enrolled farmers responded to the 2005-2010 interview; 7% of farmers died prior to the interview. We examined whether response was related to attributes measured at enrollment. To characterize potential bias from attrition, we evaluated differences in associations between smoking and incidence of 3 cancer types between the enrolled cohort and the subcohort of 2005-2010 respondents, using cancer registry information. In the subcohort we evaluated the ability of inverse probability weighting (IPW) to reduce bias. Response was related to age, state, race/ethnicity, education, marital status, smoking, and alcohol consumption. When exposure and outcome were associated and case response was differential by exposure, some bias was observed; IPW conditional on exposure and covariates failed to correct estimates. When response was nondifferential, subcohort and full-cohort estimates were similar, making IPW unnecessary. This example provides a demonstration of investigating the influence of attrition in cohort studies using information that has been self-reported after enrollment. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

E-learning in an undergraduate radiography programme: Example of an interactive website

International Nuclear Information System (INIS)

White, Peter; Cheung, Alice K.Y.

2006-01-01

Purpose: To demonstrate how e-learning can be integrated into an undergraduate radiography programme, using an academic subject dealing with ethico-legal issues as an example. Information provided could be applied to any form of online learning. Methods: One academic subject from an undergraduate radiography programme, Case-Based Learning for Professional Studies, which had previously been taught using traditional face-to-face methods, was transformed into an e-learning format. Students who experienced the new e-learning format were evaluated by means of an online evaluation questionnaire. Results: Eighty-three percentage of respondents felt confident/semi-confident about participating in online Chat sessions. Around 34% of respondents thought that the Discussion Board was useful for communicating with fellow students. Nearly 70% of respondents believed that access to online materials enabled them to prepare for lectures and tutorials. However, 34% of students preferred more face-to-face lectures/tutorials. Overall, feedback was positive. Conclusion: Course providers and other relevant stakeholders need to be proactive in determining ways to facilitate undergraduate and post-registration development and learning. E-learning can be utilized to benefit learners who wish to work at their own pace and who cannot attend courses at remote sites. Individuals can reap the benefits of an online learning format and affording learners more flexibility and providing guidance for them, by means of a website, may help to promote a positive attitude to lifelong learning

E-learning in an undergraduate radiography programme: Example of an interactive website

Energy Technology Data Exchange (ETDEWEB)

White, Peter [Department of Optometry and Radiography, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (China)]. E-mail: orpwhite@polyu.edu.hk; Cheung, Alice K.Y. [Department of Optometry and Radiography, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (China)]. E-mail: alice.cheung@iee.org

2006-08-15

Purpose: To demonstrate how e-learning can be integrated into an undergraduate radiography programme, using an academic subject dealing with ethico-legal issues as an example. Information provided could be applied to any form of online learning. Methods: One academic subject from an undergraduate radiography programme, Case-Based Learning for Professional Studies, which had previously been taught using traditional face-to-face methods, was transformed into an e-learning format. Students who experienced the new e-learning format were evaluated by means of an online evaluation questionnaire. Results: Eighty-three percentage of respondents felt confident/semi-confident about participating in online Chat sessions. Around 34% of respondents thought that the Discussion Board was useful for communicating with fellow students. Nearly 70% of respondents believed that access to online materials enabled them to prepare for lectures and tutorials. However, 34% of students preferred more face-to-face lectures/tutorials. Overall, feedback was positive. Conclusion: Course providers and other relevant stakeholders need to be proactive in determining ways to facilitate undergraduate and post-registration development and learning. E-learning can be utilized to benefit learners who wish to work at their own pace and who cannot attend courses at remote sites. Individuals can reap the benefits of an online learning format and affording learners more flexibility and providing guidance for them, by means of a website, may help to promote a positive attitude to lifelong learning.

Implicit learning of sequential bias in a guessing task: failure to demonstrate effects of dopamine administration and paranormal belief.

Science.gov (United States)

Palmer, John; Mohr, Christine; Krummenacher, Peter; Brugger, Peter

2007-06-01

Previous research suggests that implicit sequence learning (ISL) is superior for believers in the paranormal and individuals with increased cerebral dopamine. Thirty-five healthy participants performed feedback-guided anticipations of four arrow directions. A 100-trial random sequence preceded two 100-trial biased sequences in which visual targets (arrows) on trial t tended to be displaced 90 degrees clockwise (CW) or counter-clockwise (CCW) from those on t - 1. ISL was defined as a positive change during the course of the biased run in the difference between pro-bias and counter-bias responses. It was hypothesized that this difference would be greater for believers in the paranormal than for skeptics, for those who received dopamine than for those who received placebo, and for believers who received dopamine than for the other groups. None of the hypotheses were supported by the data. It is suggested that a simple binary guessing task with a focus on prediction accuracy during early trials should be considered for future explorations.

Fear of negative evaluation biases social evaluation inference: evidence from a probabilistic learning task.

Science.gov (United States)

Button, Katherine S; Kounali, Daphne; Stapinski, Lexine; Rapee, Ronald M; Lewis, Glyn; Munafò, Marcus R

2015-01-01

Fear of negative evaluation (FNE) defines social anxiety yet the process of inferring social evaluation, and its potential role in maintaining social anxiety, is poorly understood. We developed an instrumental learning task to model social evaluation learning, predicting that FNE would specifically bias learning about the self but not others. During six test blocks (3 self-referential, 3 other-referential), participants (n = 100) met six personas and selected a word from a positive/negative pair to finish their social evaluation sentences "I think [you are / George is]…". Feedback contingencies corresponded to 3 rules, liked, neutral and disliked, with P[positive word correct] = 0.8, 0.5 and 0.2, respectively. As FNE increased participants selected fewer positive words (β = -0.4, 95% CI -0.7, -0.2, p = 0.001), which was strongest in the self-referential condition (FNE × condition 0.28, 95% CI 0.01, 0.54, p = 0.04), and the neutral and dislike rules (FNE × condition × rule, p = 0.07). At low FNE the proportion of positive words selected for self-neutral and self-disliked greatly exceeded the feedback contingency, indicating poor learning, which improved as FNE increased. FNE is associated with differences in processing social-evaluative information specifically about the self. At low FNE this manifests as insensitivity to learning negative self-referential evaluation. High FNE individuals are equally sensitive to learning positive or negative evaluation, which although objectively more accurate, may have detrimental effects on mental health.

Introduction to Unconscious Bias

Science.gov (United States)

Schmelz, Joan T.

2010-05-01

We all have biases, and we are (for the most part) unaware of them. In general, men and women BOTH unconsciously devalue the contributions of women. This can have a detrimental effect on grant proposals, job applications, and performance reviews. Sociology is way ahead of astronomy in these studies. When evaluating identical application packages, male and female University psychology professors preferred 2:1 to hire "Brian” over "Karen” as an assistant professor. When evaluating a more experienced record (at the point of promotion to tenure), reservations were expressed four times more often when the name was female. This unconscious bias has a repeated negative effect on Karen's career. This talk will introduce the concept of unconscious bias and also give recommendations on how to address it using an example for a faculty search committee. The process of eliminating unconscious bias begins with awareness, then moves to policy and practice, and ends with accountability.

Negative learning bias is associated with risk aversion in a genetic animal model of depression.

Science.gov (United States)

Shabel, Steven J; Murphy, Ryan T; Malinow, Roberto

2014-01-01

The lateral habenula (LHb) is activated by aversive stimuli and the omission of reward, inhibited by rewarding stimuli and is hyperactive in helpless rats-an animal model of depression. Here we test the hypothesis that congenital learned helpless (cLH) rats are more sensitive to decreases in reward size and/or less sensitive to increases in reward than wild-type (WT) control rats. Consistent with the hypothesis, we found that cLH rats were slower to switch preference between two responses after a small upshift in reward size on one of the responses but faster to switch their preference after a small downshift in reward size. cLH rats were also more risk-averse than WT rats-they chose a response delivering a constant amount of reward ("safe" response) more often than a response delivering a variable amount of reward ("risky" response) compared to WT rats. Interestingly, the level of bias toward negative events was associated with the rat's level of risk aversion when compared across individual rats. cLH rats also showed impaired appetitive Pavlovian conditioning but more accurate responding in a two-choice sensory discrimination task. These results are consistent with a negative learning bias and risk aversion in cLH rats, suggesting abnormal processing of rewarding and aversive events in the LHb of cLH rats.

The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity.

Science.gov (United States)

Howard, Ayanna; Borenstein, Jason

2017-09-21

Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as "black teenagers" are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.

Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory

Directory of Open Access Journals (Sweden)

Stephan Koenig

2017-05-01

Full Text Available We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L, partial (P, and high (H shock expectancy, respectively. The cues differed with respect to the strength of their shock association (L < P < H and the uncertainty of their prediction (L < P > H. During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues. After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically. Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention.

Developmental Changes in the Whole Number Bias

Science.gov (United States)

Braithwaite, David W.; Siegler, Robert S.

2018-01-01

Many students' knowledge of fractions is adversely affected by whole number bias, the tendency to focus on the separate whole number components (numerator and denominator) of a fraction rather than on the fraction's magnitude (ratio of numerator to denominator). Although whole number bias appears early in the fraction learning process and under…

Gender bias and discrimination in nursing education: can we change it?

Science.gov (United States)

Anthony, Ann Strong

2004-01-01

Gender bias in nursing education impedes recruitment and retention of males into the profession. Nurse educators who are unaware of men's historical contributions to the profession may unknowingly perpetuate gender bias. The author describes how traditional stereotypes can be challenged and teaching/learning strategies can be customized to gender-driven learning styles.

Student Behavior and Epistemological Framing: Examples from Collaborative Active-Learning Activities in Physics

Science.gov (United States)

Scherr, Rachel E.; Hammer, David

2009-01-01

The concept of framing from anthropology and sociolinguistics is useful for understanding student reasoning. For example, a student may frame a learning activity as an opportunity for sensemaking or as an assignment to fill out a worksheet. The student's framing affects what she notices, what knowledge she accesses, and how she thinks to act. We…

Using Perturbed Physics Ensembles and Machine Learning to Select Parameters for Reducing Regional Biases in a Global Climate Model

Science.gov (United States)

Li, S.; Rupp, D. E.; Hawkins, L.; Mote, P.; McNeall, D. J.; Sarah, S.; Wallom, D.; Betts, R. A.

2017-12-01

This study investigates the potential to reduce known summer hot/dry biases over Pacific Northwest in the UK Met Office's atmospheric model (HadAM3P) by simultaneously varying multiple model parameters. The bias-reduction process is done through a series of steps: 1) Generation of perturbed physics ensemble (PPE) through the volunteer computing network weather@home; 2) Using machine learning to train "cheap" and fast statistical emulators of climate model, to rule out regions of parameter spaces that lead to model variants that do not satisfy observational constraints, where the observational constraints (e.g., top-of-atmosphere energy flux, magnitude of annual temperature cycle, summer/winter temperature and precipitation) are introduced sequentially; 3) Designing a new PPE by "pre-filtering" using the emulator results. Steps 1) through 3) are repeated until results are considered to be satisfactory (3 times in our case). The process includes a sensitivity analysis to find dominant parameters for various model output metrics, which reduces the number of parameters to be perturbed with each new PPE. Relative to observational uncertainty, we achieve regional improvements without introducing large biases in other parts of the globe. Our results illustrate the potential of using machine learning to train cheap and fast statistical emulators of climate model, in combination with PPEs in systematic model improvement.

Fear of Negative Evaluation Biases Social Evaluation Inference: Evidence from a Probabilistic Learning Task

Science.gov (United States)

Button, Katherine S.; Kounali, Daphne; Stapinski, Lexine; Rapee, Ronald M.; Lewis, Glyn; Munafò, Marcus R.

2015-01-01

Background Fear of negative evaluation (FNE) defines social anxiety yet the process of inferring social evaluation, and its potential role in maintaining social anxiety, is poorly understood. We developed an instrumental learning task to model social evaluation learning, predicting that FNE would specifically bias learning about the self but not others. Methods During six test blocks (3 self-referential, 3 other-referential), participants (n = 100) met six personas and selected a word from a positive/negative pair to finish their social evaluation sentences “I think [you are / George is]…”. Feedback contingencies corresponded to 3 rules, liked, neutral and disliked, with P[positive word correct] = 0.8, 0.5 and 0.2, respectively. Results As FNE increased participants selected fewer positive words (β = −0.4, 95% CI −0.7, −0.2, p = 0.001), which was strongest in the self-referential condition (FNE × condition 0.28, 95% CI 0.01, 0.54, p = 0.04), and the neutral and dislike rules (FNE × condition × rule, p = 0.07). At low FNE the proportion of positive words selected for self-neutral and self-disliked greatly exceeded the feedback contingency, indicating poor learning, which improved as FNE increased. Conclusions FNE is associated with differences in processing social-evaluative information specifically about the self. At low FNE this manifests as insensitivity to learning negative self-referential evaluation. High FNE individuals are equally sensitive to learning positive or negative evaluation, which although objectively more accurate, may have detrimental effects on mental health. PMID:25853835

Minimum bias measurement at 13 TeV

CERN Document Server

Orlando, Nicola; The ATLAS collaboration

2017-01-01

The modelling of Minimum Bias (MB) is a crucial ingredient to learn about the description of soft QCD processes and to simulate the environment at the LHC with many concurrent pp interactions (pile-up). We summarise the ATLAS minimum bias measurements with proton-proton collision at 13 TeV center-of-mass-energy at the Large Hadron Collider.

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

Science.gov (United States)

Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

Science.gov (United States)

Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147

Teaching Research Methods and Statistics in eLearning Environments:Pedagogy, Practical Examples and Possible Futures

Directory of Open Access Journals (Sweden)

Adam John Rock

2016-03-01

Full Text Available Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal, Ginsburg, & Schau, 1997. Given the ubiquitous and distributed nature of eLearning systems (Nof, Ceroni, Jeong, & Moghaddam, 2015, teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Codon usage bias analysis for the coding sequences of Camellia ...

African Journals Online (AJOL)

sunny t

2016-02-24

Feb 24, 2016 ... suggested that codon usage bias is driven by selection, particularly for .... For example, as mentioned above, highly expressed genes tend to use fewer ... directional codon bias measure effective number of codons (ENc) was ...

Symmetry as Bias: Rediscovering Special Relativity

Science.gov (United States)

Lowry, Michael R.

1992-01-01

This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

Brief learning induces a memory bias for arousing-negative words: An fMRI study in high and low trait anxious persons

OpenAIRE

Annuschka Salima Eden; Annuschka Salima Eden; Vera eDehmelt; Vera eDehmelt; Matthias eBischoff; Pienie eZwitserlood; Harald eKugel; Kati eKeuper; Peter eZwanzger; Christian eDobel; Christian eDobel; Christian eDobel

2015-01-01

Persons suffering from anxiety disorders display facilitated processing of arousing and negative stimuli, such as negative words. This memory bias is reflected in better recall and increased amygdala activity in response to such stimuli. However, individual learning histories were not considered in most studies, a concern that we meet here. Thirty-four female persons (half with high-, half with low trait anxiety) participated in a criterion-based associative word-learning paradigm, in which n...

Brief learning induces a memory bias for arousing-negative words: an fMRI study in high and low trait anxious persons

OpenAIRE

Eden, Annuschka S.; Dehmelt, Vera; Bischoff, Matthias; Zwitserlood, Pienie; Kugel, Harald; Keuper, Kati; Zwanzger, Peter; Dobel, Christian

2015-01-01

Persons suffering from anxiety disorders display facilitated processing of arousing and negative stimuli, such as negative words. This memory bias is reflected in better recall and increased amygdala activity in response to such stimuli. However, individual learning histories were not considered in most studies, a concern that we meet here. Thirty-four female persons (half with high-, half with low trait anxiety) participated in a criterion-based associative word-learning paradigm, in which n...

Affective bias as a rational response to the statistics of rewards and punishments.

Science.gov (United States)

Pulcu, Erdem; Browning, Michael

2017-10-04

Affective bias, the tendency to differentially prioritise the processing of negative relative to positive events, is commonly observed in clinical and non-clinical populations. However, why such biases develop is not known. Using a computational framework, we investigated whether affective biases may reflect individuals' estimates of the information content of negative relative to positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of distinct positive and negative outcomes which may bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel approach to treatment development.

Bias-Free Chemically Diverse Test Sets from Machine Learning.

Science.gov (United States)

Swann, Ellen T; Fernandez, Michael; Coote, Michelle L; Barnard, Amanda S

2017-08-14

Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-, two-, and three-dimensional structure of a molecule. Using data from the NIST Computational Chemistry Comparison and Benchmark Database and machine learning techniques, we demonstrate the functional relationship between these structural descriptors and the electronic energy of molecules. Archetypes and prototypes found with topological or Coulomb matrix descriptors can be used to identify smaller, statistically significant test sets that better capture the diversity of chemical space. We apply this same method to find a diverse subset of organic molecules to demonstrate how the methods can easily be reapplied to individual research projects. Finally, we use our bias-free test sets to assess the performance of density functional theory and quantum Monte Carlo methods.

Training Self-Regulated Learning Skills with Video Modeling Examples: Do Task-Selection Skills Transfer?

Science.gov (United States)

Raaijmakers, Steven F.; Baars, Martine; Schaap, Lydia; Paas, Fred; van Merriënboer, Jeroen; van Gog, Tamara

2018-01-01

Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and…

Learning the Brain in Introductory Psychology: Examining the Generation Effect for Mnemonics and Examples

Science.gov (United States)

McCabe, Jennifer A.

2015-01-01

The goal of this research was to determine whether there is a generation effect for learner-created keyword mnemonics and real-life examples, compared to instructor-provided materials, when learning neurophysiological terms and definitions in introductory psychology. Students participated in an individual (Study 1) or small-group (Study 2)…

Bayesian methods for the physical sciences learning from examples in astronomy and physics

CERN Document Server

Andreon, Stefano

2015-01-01

Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications.

Individuals With OCD Lack Unrealistic Optimism Bias in Threat Estimation.

Science.gov (United States)

Zetsche, Ulrike; Rief, Winfried; Exner, Cornelia

2015-07-01

Overestimating the occurrence of threatening events has been highlighted as a central cognitive factor in the maintenance of obsessive-compulsive disorder (OCD). The present study examined the different facets of this cognitive bias, its underlying mechanisms, and its specificity to OCD. For this purpose, threat estimation, probabilistic classification learning (PCL) and psychopathological measures were assessed in 23 participants with OCD, 30 participants with social phobia, and 31 healthy controls. Whereas healthy participants showed an optimistic expectation bias regarding positive and negative future events, OCD participants lacked such a bias. This lack of an optimistic expectation bias was not specific to OCD. Compared to healthy controls, OCD participants overestimated their personal risk for experiencing negative events, but did not differ from controls in their risk estimation regarding other people. Finally, OCD participants' biases in the prediction of checking-related events were associated with their impairments in learning probabilistic cue-outcome associations in a disorder-relevant context. In sum, the present results add to a growing body of research demonstrating that cognitive biases in OCD are context-dependent. Copyright © 2015. Published by Elsevier Ltd.

Transforming Passive Receptivity of Knowledge into Deep Learning Experiences at the Undergraduate Level: An Example from Music Theory

Science.gov (United States)

Ferenc, Anna

2015-01-01

This article discusses transformation of passive knowledge receptivity into experiences of deep learning in a lecture-based music theory course at the second-year undergraduate level through implementation of collaborative projects that evoke natural critical learning environments. It presents an example of such a project, addresses key features…

Selection bias and the perils of benchmarking.

Science.gov (United States)

Denrell, Jerker

2005-04-01

To find the secrets of business success, what could be more natural than studying successful businesses? In fact, nothing could be more dangerous, warns this Stanford professor. Generalizing from the examples of successful companies is like generalizing about New England weather from data taken only in the summer. That's essentially what businesspeople do when they learn from good examples and what consultants, authors, and researchers do when they study only existing companies or--worse yet--only high-performing companies. They reach conclusions from unrepresentative data samples, falling into the classic statistical trap of selection bias. Drawing on a wealth of case studies, for instance, one researcher concluded that great leaders share two key traits: They persist, often despite initial failures, and they are able to persuade others to join them. But those traits are also the hallmarks of spectacularly unsuccessful entrepreneurs, who must persist in the face of failure to incur large losses and must be able to persuade others to pour their money down the drain. To discover what makes a business successful, then, managers should look at both successes and failures. Otherwise, they will overvalue risky business practices, seeing only those companies that won big and not the ones that lost dismally. They will not be able to tell if their current good fortune stems from smart business practices or if they are actually coasting on past accomplishments or good luck. Fortunately, economists have developed relatively simple tools that can correct for selection bias even when data about failed companies are hard to come by. Success may be inspirational, but managers are more likely to find the secrets of high performance if they give the stories of their competitors'failures as full a hearing as they do the stories of dazzling successes.

It's All a Matter of Perspective: Viewing First-Person Video Modeling Examples Promotes Learning of an Assembly Task

Science.gov (United States)

Fiorella, Logan; van Gog, Tamara; Hoogerheide, Vincent; Mayer, Richard E.

2017-01-01

The present study tests whether presenting video modeling examples from the learner's (first-person) perspective promotes learning of an assembly task, compared to presenting video examples from a third-person perspective. Across 2 experiments conducted in different labs, university students viewed a video showing how to assemble an 8-component…

Liberal bias and the five-factor model.

Science.gov (United States)

Charney, Evan

2015-01-01

Duarte et al. draw attention to the "embedding of liberal values and methods" in social psychological research. They note how these biases are often invisible to the researchers themselves. The authors themselves fall prey to these "invisible biases" by utilizing the five-factor model of personality and the trait of openness to experience as one possible explanation for the under-representation of political conservatives in social psychology. I show that the manner in which the trait of openness to experience is conceptualized and measured is a particularly blatant example of the very liberal bias the authors decry.

Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

Science.gov (United States)

Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2017-01-25

The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

It’s all a matter of perspective : Viewing first-person video modeling examples promotes learning of an assembly task

NARCIS (Netherlands)

Fiorella, Logan; van Gog, T.; Hoogerheide, V.; Mayer, Richard

2017-01-01

The present study tests whether presenting video modeling examples from the learner’s (first-person) perspective promotes learning of an assembly task, compared to presenting video examples from a third-person perspective. Across 2 experiments conducted in different labs, university students viewed

Evaluating OO example programs for CS1

DEFF Research Database (Denmark)

Börstler, Jürgen; Christensen, Henrik Bærbak; Bennedsen, Jens

2008-01-01

Example programs play an important role in learning to program. They work as templates, guidelines, and inspiration for learners when developing their own programs. It is therefore important to provide learners with high quality examples. In this paper, we discuss properties of example programs...... that might affect the teaching and learning of object-oriented programming. Furthermore, we present an evaluation instrument for example programs and report on initial experiences of its application to a selection of examples from popular introductory programming textbooks....

On the distribution of linear biases: Three instructive examples

DEFF Research Database (Denmark)

Abdelraheem, Mohamed Ahmed; Beelen, Peter; Leander, Gregor

2012-01-01

, we often tend to embed a security margin - from an efficiency perspective nothing else than wasted performance. The aim of this paper is to stimulate research on these foundations of block ciphers. We do this by presenting three examples of ciphers that behave differently to what is normally assumed...

Showing a model's eye movements in examples does not improve learning of problem-solving tasks

NARCIS (Netherlands)

van Marlen, Tim; van Wermeskerken, Margot; Jarodzka, Halszka; van Gog, Tamara

2016-01-01

Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention. EMME have been shown to enhance learning of perceptual classification tasks; however, it is an

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...

HTML5 web application development by example

CERN Document Server

Gustafson, JM

2013-01-01

The best way to learn anything is by doing. The author uses a friendly tone and fun examples to ensure that you learn the basics of application development. Once you have read this book, you should have the necessary skills to build your own applications.If you have no experience but want to learn how to create applications in HTML5, this book is the only help you'll need. Using practical examples, HTML5 Web Application Development by Example will develop your knowledge and confidence in application development.

Fixed points of occasionally weakly biased mappings

OpenAIRE

Y. Mahendra Singh, M. R. Singh

2012-01-01

Common fixed point results due to Pant et al. [Pant et al., Weak reciprocal continuity and fixed point theorems, Ann Univ Ferrara, 57(1), 181-190 (2011)] are extended to a class of non commuting operators called occasionally weakly biased pair[ N. Hussain, M. A. Khamsi A. Latif, Commonfixed points for JH-operators and occasionally weakly biased pairs under relaxed conditions, Nonlinear Analysis, 74, 2133-2140 (2011)]. We also provideillustrative examples to justify the improvements. Abstract....

Adaptable history biases in human perceptual decisions.

Science.gov (United States)

Abrahamyan, Arman; Silva, Laura Luz; Dakin, Steven C; Carandini, Matteo; Gardner, Justin L

2016-06-21

When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject's default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics.

Sources of bias in clinical ethics case deliberation.

Science.gov (United States)

Magelssen, Morten; Pedersen, Reidar; Førde, Reidun

2014-10-01

A central task for clinical ethics consultants and committees (CEC) is providing analysis of, and advice on, prospective or retrospective clinical cases. However, several kinds of biases may threaten the integrity, relevance or quality of the CEC's deliberation. Bias should be identified and, if possible, reduced or counteracted. This paper provides a systematic classification of kinds of bias that may be present in a CEC's case deliberation. Six kinds of bias are discussed, with examples, as to their significance and risk factors. Possible remedies are suggested. The potential for bias is greater when the case deliberation is performed by an individual ethics consultant than when an entire clinical ethics committee is involved. 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.

Non-Gaussian halo assembly bias

International Nuclear Information System (INIS)

Reid, Beth A.; Verde, Licia; Dolag, Klaus; Matarrese, Sabino; Moscardini, Lauro

2010-01-01

The strong dependence of the large-scale dark matter halo bias on the (local) non-Gaussianity parameter, f NL , offers a promising avenue towards constraining primordial non-Gaussianity with large-scale structure surveys. In this paper, we present the first detection of the dependence of the non-Gaussian halo bias on halo formation history using N-body simulations. We also present an analytic derivation of the expected signal based on the extended Press-Schechter formalism. In excellent agreement with our analytic prediction, we find that the halo formation history-dependent contribution to the non-Gaussian halo bias (which we call non-Gaussian halo assembly bias) can be factorized in a form approximately independent of redshift and halo mass. The correction to the non-Gaussian halo bias due to the halo formation history can be as large as 100%, with a suppression of the signal for recently formed halos and enhancement for old halos. This could in principle be a problem for realistic galaxy surveys if observational selection effects were to pick galaxies occupying only recently formed halos. Current semi-analytic galaxy formation models, for example, imply an enhancement in the expected signal of ∼ 23% and ∼ 48% for galaxies at z = 1 selected by stellar mass and star formation rate, respectively

Understanding and Overcoming Implicit Gender Bias in Plastic Surgery.

Science.gov (United States)

Phillips, Nicole A; Tannan, Shruti C; Kalliainen, Loree K

2016-11-01

Although explicit sex-based discrimination has largely been deemed unacceptable in professional settings, implicit gender bias persists and results in a significant lack of parity in plastic surgery and beyond. Implicit gender bias is the result of a complex interplay of cultural and societal expectations, learned behaviors, and standardized associations. As such, both male and female surgeons are subject to its influence. A review of the literature was conducted, examining theories of gender bias, current manifestations of gender bias in plastic surgery and other fields, and interventions designed to address gender bias. Multiple studies demonstrate persistent gender bias that impacts female physicians at all levels of training. Several institutions have enacted successful interventions to identify and address gender bias. Explicit gender bias has largely disappeared, yet unconscious or implicit gender bias persists. A wide-scale commitment to addressing implicit gender bias in plastic surgery is necessary and overdue. Recommendations include immediate actions that can be undertaken on an individual basis, and changes that should be implemented at a national and international level by leaders in the field.

Learning perceptual aspects of diagnosis in medicine via eye movement modeling examples on patient video cases

NARCIS (Netherlands)

Jarodzka, Halszka; Balslev, Thomas; Holmqvist, Kenneth; Nyström, Marcus; Scheiter, Katharina; Gerjets, Peter; Eika, Berit

2010-01-01

Jarodzka, H., Balslev, T., Holmqvist, K., Nyström, M., Scheiter, K., Gerjets, P., & Eika, B. (2010, August). Learning perceptual aspects of diagnosis in medicine via eye movement modeling examples on patient video cases. Poster presented at the 32nd Annual Conference of the Cognitive Science

Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function

Science.gov (United States)

Rodríguez-Quiñonez, J. C.; Sergiyenko, O.; Hernandez-Balbuena, D.; Rivas-Lopez, M.; Flores-Fuentes, W.; Basaca-Preciado, L. C.

2014-12-01

Many laser scanners depend on their mechanical construction to guarantee their measurements accuracy, however, the current computational technologies allow us to improve these measurements by mathematical methods implemented in neural networks. In this article we are going to introduce the current laser scanner technologies, give a description of our 3D laser scanner and adjust their measurement error by a previously trained feed forward back propagation (FFBP) neural network with a Widrow-Hoff weight/bias learning function. A comparative analysis with other learning functions such as the Kohonen algorithm and gradient descendent with momentum algorithm is presented. Finally, computational simulations are conducted to verify the performance and method uncertainty in the proposed system.

Vicarious learning of children's social-anxiety-related fear beliefs and emotional Stroop bias.

Science.gov (United States)

Askew, Chris; Hagel, Anna; Morgan, Julie

2015-08-01

Models of social anxiety suggest that negative social experiences contribute to the development of social anxiety, and this is supported by self-report research. However, there is relatively little experimental evidence for the effects of learning experiences on social cognitions. The current study examined the effect of observing a social performance situation with a negative outcome on children's (8 to 11 years old) fear-related beliefs and cognitive processing. Two groups of children were each shown 1 of 2 animated films of a person trying to score in basketball while being observed by others; in 1 film, the outcome was negative, and in the other, it was neutral. Children's fear-related beliefs about performing in front of others were measured before and after the film and children were asked to complete an emotional Stroop task. Results showed that social fear beliefs increased for children who saw the negative social performance film. In addition, these children showed an emotional Stroop bias for social-anxiety-related words compared to children who saw the neutral film. The findings have implications for our understanding of social anxiety disorder and suggest that vicarious learning experiences in childhood may contribute to the development of social anxiety. (c) 2015 APA, all rights reserved).

Learning perceptual aspects of diagnosis in medicine via eye movement modeling examples on patient video cases

NARCIS (Netherlands)

Jarodzka, Halszka; Balslev, Thomas; Holmqvist, Kenneth; Nyström, Marcus; Scheiter, Katharina; Gerjets, Peter; Eika, Berit

2010-01-01

Jarodzka, H., Balslev, T., Holmqvist, K., Nyström, M., Scheiter, K., Gerjets, P., & Eika, B. (2010). Learning perceptual aspects of diagnosis in medicine via eye movement modeling examples on patient video cases. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the

Importance biasing quality criterion based on contribution response theory

International Nuclear Information System (INIS)

Borisov, N.M.; Panin, M.P.

2001-01-01

The report proposes a visual criterion of importance biasing both of forward and adjoint simulation. The similarity of contribution Monte Carlo and importance biasing random collision event distribution is proved. The conservation of total number of random trajectory crossings of surfaces, which separate the source and the detector is proposed as importance biasing quality criterion. The use of this criterion is demonstrated on the example of forward vs. adjoint importance biasing in gamma ray deep penetration problem. The larger amount of published data on forward field characteristics than on adjoint leads to the more accurate approximation of adjoint importance function in comparison to forward, for it adjoint importance simulation is more effective than forward. The proposed criterion indicates it visually, showing the most uniform distribution of random trajectory crossing events for the most effective importance biasing parameters and pointing to the direction of tuning importance biasing parameters. (orig.)

Enhancing Learning in Statistics Classes Through The Use of Concrete Historical Examples: The Space Shuttle Challenger, Pearl Harbor, and the RMS Titanic.

Science.gov (United States)

Schumm, Walter R.; Webb, Farrell J.; Castelo, Carlos S.; Akagi, Cynthia G.; Jensen, Erick J.; Ditto, Rose M.; Spencer Carver, Elaine; Brown, Beverlyn F.

2002-01-01

Discusses the use of historical events as examples for teaching college level statistics courses. Focuses on examples of the space shuttle Challenger, Pearl Harbor (Hawaii), and the RMS Titanic. Finds real life examples can bridge a link to short term experiential learning and provide a means for long term understanding of statistics. (KDR)

Alfanet Worked Example: What is Greatness?

NARCIS (Netherlands)

dr. Pierre Gorissen

2004-01-01

This document consists of an example of a Learning Design based on the What is Greatness example originally created by James Dalziel from WebMCQ using LAMS. Note: The example has been created in parallel with the actual development of the Alfanet system. So no claims can be made that the example

An SIS model for cultural trait transmission with conformity bias.

Science.gov (United States)

Walters, Caroline E; Kendal, Jeremy R

2013-12-01

Epidemiological models have been applied to human health-related behaviors that are affected by social interaction. Typically these models have not considered conformity bias, that is, the exaggerated propensity to adopt commonly observed behaviors or opinions, or content biases, where the content of the learned trait affects the probability of adoption. Here we consider an interaction of these two effects, presenting an SIS-type model for the spread and persistence of a behavior which is transmitted via social learning. Uptake is controlled by a nonlinear dependence on the proportion of individuals demonstrating the behavior in a population. Three equilibrium solutions are found, their linear stability is analyzed and the results are compared with a model for unbiased social learning. Our analysis focuses on the effects of the strength of conformity bias and the effects of content biases which alter a conformity threshold frequency of the behavior, above which there is an exaggerated propensity for adoption. The strength of the conformity bias is found to qualitatively alter the predictions regarding whether the trait becomes endemic within the population and the proportion of individuals who display the trait when it is endemic. As the conformity strength increases, the number of feasible equilibrium solutions increases from two to three, leading to a situation where the stable equilibrium attained is dependent upon the initial state. Varying the conformity threshold frequency directionally alters the behavior invasion threshold. Finally we discuss the possible application of this model to binge drinking behavior. Copyright © 2013 Elsevier Inc. All rights reserved.

Reporting bias in medical research - a narrative review

Directory of Open Access Journals (Sweden)

Kölsch Heike

2010-04-01

Full Text Available Abstract Reporting bias represents a major problem in the assessment of health care interventions. Several prominent cases have been described in the literature, for example, in the reporting of trials of antidepressants, Class I anti-arrhythmic drugs, and selective COX-2 inhibitors. The aim of this narrative review is to gain an overview of reporting bias in the medical literature, focussing on publication bias and selective outcome reporting. We explore whether these types of bias have been shown in areas beyond the well-known cases noted above, in order to gain an impression of how widespread the problem is. For this purpose, we screened relevant articles on reporting bias that had previously been obtained by the German Institute for Quality and Efficiency in Health Care in the context of its health technology assessment reports and other research work, together with the reference lists of these articles. We identified reporting bias in 40 indications comprising around 50 different pharmacological, surgical (e.g. vacuum-assisted closure therapy, diagnostic (e.g. ultrasound, and preventive (e.g. cancer vaccines interventions. Regarding pharmacological interventions, cases of reporting bias were, for example, identified in the treatment of the following conditions: depression, bipolar disorder, schizophrenia, anxiety disorder, attention-deficit hyperactivity disorder, Alzheimer's disease, pain, migraine, cardiovascular disease, gastric ulcers, irritable bowel syndrome, urinary incontinence, atopic dermatitis, diabetes mellitus type 2, hypercholesterolaemia, thyroid disorders, menopausal symptoms, various types of cancer (e.g. ovarian cancer and melanoma, various types of infections (e.g. HIV, influenza and Hepatitis B, and acute trauma. Many cases involved the withholding of study data by manufacturers and regulatory agencies or the active attempt by manufacturers to suppress publication. The ascertained effects of reporting bias included the

Cognitive bias test as a tool for accessing fish welfare

Directory of Open Access Journals (Sweden)

Krzysztof Wojtas

2015-12-01

Difference in behaviour during the cognitive bias test suggests that fish cognitive bias can be affected by living conditions. Therefore this type of test should be taken to consideration as a tool in further fish welfare studies. It can be especially useful in studies concerning influence of living conditions that cannot be examined in direct way for example by preference test.

Are most samples of animals systematically biased? Consistent individual trait differences bias samples despite random sampling.

Science.gov (United States)

Biro, Peter A

2013-02-01

Sampling animals from the wild for study is something nearly every biologist has done, but despite our best efforts to obtain random samples of animals, 'hidden' trait biases may still exist. For example, consistent behavioral traits can affect trappability/catchability, independent of obvious factors such as size and gender, and these traits are often correlated with other repeatable physiological and/or life history traits. If so, systematic sampling bias may exist for any of these traits. The extent to which this is a problem, of course, depends on the magnitude of bias, which is presently unknown because the underlying trait distributions in populations are usually unknown, or unknowable. Indeed, our present knowledge about sampling bias comes from samples (not complete population censuses), which can possess bias to begin with. I had the unique opportunity to create naturalized populations of fish by seeding each of four small fishless lakes with equal densities of slow-, intermediate-, and fast-growing fish. Using sampling methods that are not size-selective, I observed that fast-growing fish were up to two-times more likely to be sampled than slower-growing fish. This indicates substantial and systematic bias with respect to an important life history trait (growth rate). If correlations between behavioral, physiological and life-history traits are as widespread as the literature suggests, then many animal samples may be systematically biased with respect to these traits (e.g., when collecting animals for laboratory use), and affect our inferences about population structure and abundance. I conclude with a discussion on ways to minimize sampling bias for particular physiological/behavioral/life-history types within animal populations.

Bias against research on gender bias.

Science.gov (United States)

Cislak, Aleksandra; Formanowicz, Magdalena; Saguy, Tamar

2018-01-01

The bias against women in academia is a documented phenomenon that has had detrimental consequences, not only for women, but also for the quality of science. First, gender bias in academia affects female scientists, resulting in their underrepresentation in academic institutions, particularly in higher ranks. The second type of gender bias in science relates to some findings applying only to male participants, which produces biased knowledge. Here, we identify a third potentially powerful source of gender bias in academia: the bias against research on gender bias. In a bibliometric investigation covering a broad range of social sciences, we analyzed published articles on gender bias and race bias and established that articles on gender bias are funded less often and published in journals with a lower Impact Factor than articles on comparable instances of social discrimination. This result suggests the possibility of an underappreciation of the phenomenon of gender bias and related research within the academic community. Addressing this meta-bias is crucial for the further examination of gender inequality, which severely affects many women across the world.

Leaf swallowing behavior in chimpanzees (Pan troglodytes): biased learning and the emergence of group level cultural differences.

Science.gov (United States)

Huffman, Michael A; Spiezio, Caterina; Sgaravatti, Andrea; Leca, Jean-Baptiste

2010-11-01

Demonstrating the ability to 'copy' the behavior of others is an important aspect in determining whether social learning occurs and whether group level differences in a given behavior represent cultural differences or not. Understanding the occurrence of this process in its natural context is essential, but can be a daunting task in the wild. In order to test the social learning hypothesis for the acquisition of leaf swallowing (LS), a self-medicative behavior associated with the expulsion of parasites, we conducted semi-naturalistic experiments on two captive groups of parasite-free, naïve chimpanzees (Pan troglodytes). Individuals in the group were systematically provided appropriate stimuli (rough hispid leaves) identical to those used by chimpanzees in the wild. Individuals initially responded in a variety of ways, ranging from total aversion to normal chewing and swallowing. Over time, however, the two groups adopted different variants for inserting and folding the leaves in the mouth prior to swallowing them (complete and partial LS), following the specific method spontaneously displayed by the first and primary LS models in their respective groups. These variants were similar to LS displayed by chimpanzees in the wild. Using the option-bias method, we found evidence for social learning leading to group-level biased transmission and group-level stabilization of these two variants. This is the first report on two distinct cultural variants innovated in response to the introduction of natural stimuli that emerged and spread spontaneously and concurrently within two adjacent groups of socially housed primates. These observations support the assertion that LS may reflect a generalized propensity for ingesting rough hispid leaves, which can be socially induced and transmitted within a group. Ingesting an adequate number of these leaves induces increased gut motility, which is responsible for the subsequent expulsion of particular parasite species in the wild

Biased Intensity Judgements of Visceral Sensations After Learning to Fear Visceral Stimuli: A Drift Diffusion Approach.

Science.gov (United States)

Zaman, Jonas; Madden, Victoria J; Iven, Julie; Wiech, Katja; Weltens, Nathalie; Ly, Huynh Giao; Vlaeyen, Johan W S; Van Oudenhove, Lukas; Van Diest, Ilse

2017-10-01

A growing body of research has identified fear of visceral sensations as a potential mechanism in the development and maintenance of visceral pain disorders. However, the extent to which such learned fear affects visceroception remains unclear. To address this question, we used a differential fear conditioning paradigm with nonpainful esophageal balloon distensions of 2 different intensities as conditioning stimuli (CSs). The experiment comprised of preacquisition, acquisition, and postacquisition phases during which participants categorized the CSs with respect to their intensity. The CS+ was always followed by a painful electrical stimulus (unconditioned stimulus) during the acquisition phase and in 60% of the trials during postacquisition. The second stimulus (CS-) was never associated with pain. Analyses of galvanic skin and startle eyeblink responses as physiological markers of successful conditioning showed increased fear responses to the CS+ compared with the CS-, but only in the group with the low-intensity stimulus as CS+. Computational modeling of response times and response accuracies revealed that differential fear learning affected perceptual decision-making about the intensities of visceral sensations such that sensations were more likely to be categorized as more intense. These results suggest that associative learning might indeed contribute to visceral hypersensitivity in functional gastrointestinal disorders. This study shows that associative fear learning biases intensity judgements of visceral sensations toward perceiving such sensations as more intense. Learning-induced alterations in visceroception might therefore contribute to the development or maintenance of visceral pain. Copyright © 2017 American Pain Society. Published by Elsevier Inc. All rights reserved.

The Effects of Training Contingency Awareness During Attention Bias Modification on Learning and Stress Reactivity.

Science.gov (United States)

Lazarov, Amit; Abend, Rany; Seidner, Shiran; Pine, Daniel S; Bar-Haim, Yair

2017-09-01

Current attention bias modification (ABM) procedures are designed to implicitly train attention away from threatening stimuli with the hope of reducing stress reactivity and anxiety symptoms. However, the mechanisms underlying effective ABM delivery are not well understood, with awareness of the training contingency suggested as one possible factor contributing to ABM efficacy. Here, 45 high-anxious participants were trained to divert attention away from threat in two ABM sessions. They were randomly assigned to one of three training protocols: an implicit protocol, comprising two standard implicit ABM training sessions; an explicit protocol, comprising two sessions with explicit instruction as to the attention training contingency; and an implicit-explicit protocol, in which participants were not informed of the training contingency in the first ABM session and informed of it at the start of the second session. We examined learning processes and stress reactivity following a stress-induction task. Results indicate that relative to implicit instructions, explicit instructions led to stronger learning during the first training session. Following rest, the explicit and implicit groups exhibited consolidation-related improvement in performance, whereas no such improvement was noted for the implicit-explicit group. Finally, although stress reactivity was reduced after training, contingency awareness did not yield a differential effect on stress reactivity measured using both self-reports and skin conductance, within and across sessions. These results suggest that explicit ABM administration leads to greater initial learning during the training protocol while not differing from standard implicit administration in terms of off-line learning and stress reactivity. Copyright © 2017. Published by Elsevier Ltd.

A model-based correction for outcome reporting bias in meta-analysis.

Science.gov (United States)

Copas, John; Dwan, Kerry; Kirkham, Jamie; Williamson, Paula

2014-04-01

It is often suspected (or known) that outcomes published in medical trials are selectively reported. A systematic review for a particular outcome of interest can only include studies where that outcome was reported and so may omit, for example, a study that has considered several outcome measures but only reports those giving significant results. Using the methodology of the Outcome Reporting Bias (ORB) in Trials study of (Kirkham and others, 2010. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. British Medical Journal 340, c365), we suggest a likelihood-based model for estimating the effect of ORB on confidence intervals and p-values in meta-analysis. Correcting for bias has the effect of moving estimated treatment effects toward the null and hence more cautious assessments of significance. The bias can be very substantial, sometimes sufficient to completely overturn previous claims of significance. We re-analyze two contrasting examples, and derive a simple fixed effects approximation that can be used to give an initial estimate of the effect of ORB in practice.

Bias-dependent hybrid PKI empirical-neural model of microwave FETs

Science.gov (United States)

Marinković, Zlatica; Pronić-Rančić, Olivera; Marković, Vera

2011-10-01

Empirical models of microwave transistors based on an equivalent circuit are valid for only one bias point. Bias-dependent analysis requires repeated extractions of the model parameters for each bias point. In order to make model bias-dependent, a new hybrid empirical-neural model of microwave field-effect transistors is proposed in this article. The model is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs) aimed at introducing bias dependency of scattering (S) and noise parameters, respectively. The prior knowledge of the proposed ANNs involves the values of the S- and noise parameters obtained by the empirical model. The proposed hybrid model is valid in the whole range of bias conditions. Moreover, the proposed model provides better accuracy than the empirical model, which is illustrated by an appropriate modelling example of a pseudomorphic high-electron mobility transistor device.

On the Quality of Examples in Introductory Java Textbooks

Science.gov (United States)

Borstler, Jurgen; Nordstrom, Marie; Paterson, James H.

2011-01-01

Example programs play an important role in the teaching and learning of programming. Students as well as teachers rank examples as the most important resources for learning to program. Example programs work as role models and must therefore always be consistent with the principles and rules we are teaching. However, it is difficult to find or…

Encouraging Example Generation: A Teaching Experiment in First-Semester Calculus

Science.gov (United States)

Wagner, Elaine Rumsey; Orme, Susan Marla; Turner, Heidi Jean; Yopp, David

2017-01-01

Mathematicians use example generation to test and verify mathematical ideas; however, the processes through which undergraduates learn to productively generate examples are not well understood. We engaged calculus students in a teaching experiment designed to develop skills in productively generating examples to learn novel concepts. This article…

Comparing Examples: WebAssign versus Textbook

Science.gov (United States)

Richards, Evan; Polak, Jeff; Hardin, Ashley; Risley, John, , Dr.

2005-11-01

Research shows students can learn from worked examples.^1 This pilot study compared two groups of students' performance (10 each) in solving physics problems. One group had access to interactive examples^2 released in WebAssign^3, while the other group had access to the counterpart textbook examples. Verbal data from students in problem solving sessions was collected using a think aloud protocol^4 and the data was analyzed using Chi's procedures.^5 An explanation of the methodology and results will be presented. Future phases of this pilot study based upon these results will also be discussed. ^1Atkinson, R.K., Derry, S.J., Renkl A., Wortham, D. (2000). ``Learning from Examples: Instructional Principles from the Worked Examples Research'', Review of Educational Research, vol. 70, n. 2, pp. 181-214. ^2Serway, R.A. & Faughn, J.S. (2006). College Physics (7^th ed.). Belmont, CA: Thomson Brooks/Cole. ^3 see www.webassign.net ^4 Ericsson, K.A. & Simon, H.A. (1984). Protocol Analysis: Verbal Reports as Data. Cambridge, Massachusetts: The MIT Press. ^5 Chi, Michelene T.H. (1997). ``Quantifying Qualitative Analyses of Verbal Data: A Practical Guide,'' The Journal of the Learning Sciences, vol. 6, n. 3, pp. 271-315.

Machine Learning examples on Invenio

CERN Document Server

CERN. Geneva

2017-01-01

This talk will present the different Machine Learning tools that the INSPIRE is developing and integrating in order to automatize as much as possible content selection and curation in a subject based repository.

Young macaques (Macaca fascicularis) preferentially bias attention towards closer, older, and better tool users.

Science.gov (United States)

Tan, Amanda W Y; Hemelrijk, Charlotte K; Malaivijitnond, Suchinda; Gumert, Michael D

2018-05-12

Examining how animals direct social learning during skill acquisition under natural conditions, generates data for examining hypotheses regarding how transmission biases influence cultural change in animal populations. We studied a population of macaques on Koram Island, Thailand, and examined model-based biases during interactions by unskilled individuals with tool-using group members. We first compared the prevalence of interactions (watching, obtaining food, object exploration) and proximity to tool users during interactions, in developing individuals (infants, juveniles) versus mature non-learners (adolescents, adults), to provide evidence that developing individuals are actively seeking information about tool use from social partners. All infants and juveniles, but only 49% of mature individuals carried out interacted with tool users. Macaques predominantly obtained food by scrounging or stealing, suggesting maximizing scrounging opportunities motivates interactions with tool users. However, while interactions by adults was limited to obtaining food, young macaques and particularly infants also watched tool users and explored objects, indicating additional interest in tool use itself. We then ran matrix correlations to identify interaction biases, and what attributes of tool users influenced these. Biases correlated with social affiliation, but macaques also preferentially targeted tool users that potentially increase scrounging and learning opportunities. Results suggest that social structure may constrain social learning, but the motivation to bias interactions towards tool users to maximize feeding opportunities may also socially modulate learning by facilitating close proximity to better tool users, and further interest in tool-use actions and materials, especially during development.

Attention to the Model's Face When Learning from Video Modeling Examples in Adolescents with and without Autism Spectrum Disorder

Science.gov (United States)

van Wermeskerken, Margot; Grimmius, Bianca; van Gog, Tamara

2018-01-01

We investigated the effects of seeing the instructor's (i.e., the model's) face in video modeling examples on students' attention and their learning outcomes. Research with university students suggested that the model's face attracts students' attention away from what the model is doing, but this did not hamper learning. We aimed to investigate…

The Value of Identifying and Recovering Lost GN&C Lessons Learned: Aeronautical, Spacecraft, and Launch Vehicle Examples

Science.gov (United States)

Dennehy, Cornelius J.; Labbe, Steve; Lebsock, Kenneth L.

2010-01-01

Within the broad aerospace community the importance of identifying, documenting and widely sharing lessons learned during system development, flight test, operational or research programs/projects is broadly acknowledged. Documenting and sharing lessons learned helps managers and engineers to minimize project risk and improve performance of their systems. Often significant lessons learned on a project fail to get captured even though they are well known 'tribal knowledge' amongst the project team members. The physical act of actually writing down and documenting these lessons learned for the next generation of NASA GN&C engineers fails to happen on some projects for various reasons. In this paper we will first review the importance of capturing lessons learned and then will discuss reasons why some lessons are not documented. A simple proven approach called 'Pause and Learn' will be highlighted as a proven low-impact method of organizational learning that could foster the timely capture of critical lessons learned. Lastly some examples of 'lost' GN&C lessons learned from the aeronautics, spacecraft and launch vehicle domains are briefly highlighted. In the context of this paper 'lost' refers to lessons that have not achieved broad visibility within the NASA-wide GN&C CoP because they are either undocumented, masked or poorly documented in the NASA Lessons Learned Information System (LLIS).

Looking on the bright side: biased attention and the human serotonin transporter gene

OpenAIRE

Fox, Elaine; Ridgewell, Anna; Ashwin, Chris

2009-01-01

Humans differ in terms of biased attention for emotional stimuli and these biases can confer differential resilience and vulnerability to emotional disorders. Selective processing of positive emotional information, for example, is associated with enhanced sociability and well-being while a bias for negative material is associated with neuroticism and anxiety. A tendency to selectively avoid negative material might also be associated with mental health and well-being. The neurobiological mecha...

Compositions of fuzzy relations applied to veryfication learning outcomes on the example of the major “Geodesy and Cartography”

Directory of Open Access Journals (Sweden)

A. Mreła

2015-05-01

    Abstract The paper presents discussion about using mathematical functions in order to help academic teachers to verify acquirement of learning outcomes by students on the example of the major “geodesy and cartography”. It is relatively easy to build fuzzy relation describing levels of realization and validation learning outcomes during subject examinations and the fuzzy relation with students’ grades is already built by teachers, the problem is to combine these two relations to get one which describes the level of acquiring learning outcomes by students. There are two main requirements facing this combinations and the paper shows that the best combination according to these requirements is algebraic composition.   Keywords: learning outcome, fuzzy relation, algebraic composition.

Dual Systems for Spatial Updating in Immediate and Retrieved Environments: Evidence from Bias Analysis.

Science.gov (United States)

Liu, Chuanjun; Xiao, Chengli

2018-01-01

The spatial updating and memory systems are employed during updating in both the immediate and retrieved environments. However, these dual systems seem to work differently, as the difference of pointing latency and absolute error between the two systems vary across environments. To verify this issue, the present study employed the bias analysis of signed errors based on the hypothesis that the transformed representation will bias toward the original one. Participants learned a spatial layout and then either stayed in the learning location or were transferred to a neighboring room directly or after being disoriented. After that, they performed spatial judgments from perspectives aligned with the learning direction, aligned with the direction they faced during the test, or a novel direction misaligned with the two above-mentioned directions. The patterns of signed error bias were consistent across environments. Responses for memory aligned perspectives were unbiased, whereas responses for sensorimotor aligned perspectives were biased away from the memory aligned perspective, and responses for misaligned perspectives were biased toward sensorimotor aligned perspectives. These findings indicate that the spatial updating system is consistently independent of the spatial memory system regardless of the environments, but the updating system becomes less accessible as the environment changes from immediate to a retrieved one.

Dual Systems for Spatial Updating in Immediate and Retrieved Environments: Evidence from Bias Analysis

Directory of Open Access Journals (Sweden)

Chuanjun Liu

2018-02-01

Full Text Available The spatial updating and memory systems are employed during updating in both the immediate and retrieved environments. However, these dual systems seem to work differently, as the difference of pointing latency and absolute error between the two systems vary across environments. To verify this issue, the present study employed the bias analysis of signed errors based on the hypothesis that the transformed representation will bias toward the original one. Participants learned a spatial layout and then either stayed in the learning location or were transferred to a neighboring room directly or after being disoriented. After that, they performed spatial judgments from perspectives aligned with the learning direction, aligned with the direction they faced during the test, or a novel direction misaligned with the two above-mentioned directions. The patterns of signed error bias were consistent across environments. Responses for memory aligned perspectives were unbiased, whereas responses for sensorimotor aligned perspectives were biased away from the memory aligned perspective, and responses for misaligned perspectives were biased toward sensorimotor aligned perspectives. These findings indicate that the spatial updating system is consistently independent of the spatial memory system regardless of the environments, but the updating system becomes less accessible as the environment changes from immediate to a retrieved one.

Genesis and Maintenance of Attentional Biases: The Role of the Locus Coeruleus-Noradrenaline System

Directory of Open Access Journals (Sweden)

Mana R. Ehlers

2017-01-01

Full Text Available Emotionally arousing events are typically better remembered than mundane ones, in part because emotionally relevant aspects of our environment are prioritized in attention. Such biased attentional tuning is itself the result of associative processes through which we learn affective and motivational relevance of cues. We propose that the locus coeruleus-noradrenaline (LC-NA system plays an important role in the genesis of attentional biases through associative learning processes as well as their maintenance. We further propose that individual differences in and disruptions of the LC-NA system underlie the development of maladaptive biases linked to psychopathology. We provide support for the proposed role of the LC-NA system by first reviewing work on attentional biases in development and its link to psychopathology in relation to alterations and individual differences in NA availability. We focus on pharmacological manipulations to demonstrate the effect of a disrupted system as well as the ADRA2b polymorphism as a tool to investigate naturally occurring differences in NA availability. We next review associative learning processes that—modulated by the LC-NA system—result in such implicit attentional biases. Further, we demonstrate how NA may influence aversive and appetitive conditioning linked to anxiety disorders as well as addiction and depression.

Learning Algebra from Worked Examples

Science.gov (United States)

Lange, Karin E.; Booth, Julie L.; Newton, Kristie J.

2014-01-01

For students to be successful in algebra, they must have a truly conceptual understanding of key algebraic features as well as the procedural skills to complete a problem. One strategy to correct students' misconceptions combines the use of worked example problems in the classroom with student self-explanation. "Self-explanation" is the…

Example-Based Learning: Effects of Different Types of Examples on Student Performance, Cognitive Load and Self-Efficacy in a Statistical Learning Task

Science.gov (United States)

Huang, Xiaoxia

2017-01-01

Previous research has indicated the disconnect between example-based research focusing on worked examples (WEs) and that focusing on modeling examples. The purpose of this study was to examine and compare the effect of four different types of examples from the two separate lines of research, including standard WEs, erroneous WEs, expert (masterly)…

Implicit Bias and Mental Health Professionals: Priorities and Directions for Research.

Science.gov (United States)

Merino, Yesenia; Adams, Leslie; Hall, William J

2018-06-01

This Open Forum explores the role of implicit bias along the mental health care continuum, which may contribute to mental health disparities among vulnerable populations. Emerging research shows that implicit bias is prevalent among service providers. These negative or stigmatizing attitudes toward population groups are held at a subconscious level and are automatically activated during practitioner-client encounters. The authors provide examples of how implicit bias may impede access to care, clinical screening and diagnosis, treatment processes, and crisis response. They also discuss how implicit attitudes may manifest at the intersection between mental health and criminal justice institutions. Finally, they discuss the need for more research on the impact of implicit bias on health practices throughout the mental health system, including the development of interventions to address implicit bias among mental health professionals.

Opportunity to Learn, Test Bias, and School Effects.

Science.gov (United States)

Anderson, Lorin W.

The purpose of the study was to examine test bias and the "non-effects" of schooling. Teachers were given a list of words selected from standardized vocabulary tests and asked to indicate the words they had taught. The words were classified by the grade level at which they were first introduced. Ninety-five third-grade students in four schools…

PAC-Learning from General Examples

DEFF Research Database (Denmark)

Fischer, Paul; Hoeffgen, K.- U.; Lefmann, H.

1997-01-01

dimension of a target class with respect to a sample class, which replaces the Vapnik-Chervonenkis dimension (V.N. Vapnik and A.Y. Chervonenkis, 1971). The investigation of structural aspects of the relative dimension is followed by its applications to learning environments. It turns out that computing...

Differential recall bias, intermediate confounding, and mediation analysis in life course epidemiology: An analytic framework with empirical example.

Directory of Open Access Journals (Sweden)

Mashhood Ahmed Sheikh

2016-11-01

Full Text Available The mechanisms by which childhood socioeconomic status (CSES affects adult mental health, general health, and well-being are not clear. Moreover, the analytical assumptions employed when assessing mediation in social and psychiatric epidemiology are rarely explained. The aim of this paper was to explain the intermediate confounding assumption, and to quantify differential recall bias in the association between CSES, childhood abuse, and mental health (SCL-10, general health (EQ-5D, and subjective well-being (SWLS. Furthermore, we assessed the mediating role of psychological and physical abuse in the association between CSES and mental health, general health, and well-being; and the influence of differential recall bias in the estimation of total effects, direct effects, and proportion of mediated effects. The assumptions employed when assessing mediation are explained with reference to a causal diagram. Poisson regression models (relative risk, RR and 99% CI were used to assess the association between CSES and psychological and physical abuse in childhood. Mediation analysis (difference method was used to assess the indirect effect of CSES (through psychological and physical abuse in childhood on mental health, general health, and well-being. Psychological abuse and physical abuse mediated the association between CSES and adult mental health, general health, and well-being (6-16% among men and 7-14% among women, p<0.001. The results suggest that up to 27% of the association between CSES and childhood abuse, 23% of the association between childhood abuse, and mental health, general health, and well-being, and 44% of the association between CSES and mental health, general health, and well-being is driven by differential recall bias. Assessing mediation with cross-sectional data (exposure, mediator, and outcome measured at the same time showed that the total effects and direct effects were vastly overestimated (biased upwards. Consequently, the

Can decision biases improve insurance outcomes? An experiment on status quo bias in health insurance choice.

Science.gov (United States)

Krieger, Miriam; Felder, Stefan

2013-06-19

Rather than conforming to the assumption of perfect rationality in neoclassical economic theory, decision behavior has been shown to display a host of systematic biases. Properly understood, these patterns can be instrumentalized to improve outcomes in the public realm. We conducted a laboratory experiment to study whether decisions over health insurance policies are subject to status quo bias and, if so, whether experience mitigates this framing effect. Choices in two treatment groups with status quo defaults are compared to choices in a neutrally framed control group. A two-step design features sorting of subjects into the groups, allowing us to control for selection effects due to risk preferences. The results confirm the presence of a status quo bias in consumer choices over health insurance policies. However, this effect of the default framing does not persist as subjects repeat this decision in later periods of the experiment. Our results have implications for health care policy, for example suggesting that the use of non-binding defaults in health insurance can facilitate the spread of co-insurance policies and thereby help contain health care expenditure.

Active Learning by Querying Informative and Representative Examples.

Science.gov (United States)

Huang, Sheng-Jun; Jin, Rong; Zhou, Zhi-Hua

2014-10-01

Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.

Optimism Bias in Fans and Sports Reporters.

Science.gov (United States)

Love, Bradley C; Kopeć, Łukasz; Guest, Olivia

2015-01-01

People are optimistic about their prospects relative to others. However, existing studies can be difficult to interpret because outcomes are not zero-sum. For example, one person avoiding cancer does not necessitate that another person develops cancer. Ideally, optimism bias would be evaluated within a closed formal system to establish with certainty the extent of the bias and the associated environmental factors, such that optimism bias is demonstrated when a population is internally inconsistent. Accordingly, we asked NFL fans to predict how many games teams they liked and disliked would win in the 2015 season. Fans, like ESPN reporters assigned to cover a team, were overly optimistic about their team's prospects. The opposite pattern was found for teams that fans disliked. Optimism may flourish because year-to-year team results are marked by auto-correlation and regression to the group mean (i.e., good teams stay good, but bad teams improve).

Associations among negative parenting, attention bias to anger, and social anxiety among youth.

Science.gov (United States)

Gulley, Lauren D; Oppenheimer, Caroline W; Hankin, Benjamin L

2014-02-01

Theories of affective learning suggest that early experiences contribute to emotional disorders by influencing the development of processing biases for negative emotional stimuli. Although studies have shown that physically abused children preferentially attend to angry faces, it is unclear whether youth exposed to more typical aspects of negative parenting exhibit the same type of bias. The current studies extend previous research by linking observed negative parenting styles (e.g., authoritarian) and behaviors (e.g., criticism and negative affect) to attention bias for angry faces in both a psychiatrically enriched (ages 11-17 years; N = 60) and a general community (ages 9-15 years; N = 75) sample of youth. In addition, the association between observed negative parenting (e.g., authoritarian style and negative affect) and youth social anxiety was mediated by attention bias for angry faces in the general community sample. Overall, findings provide preliminary support for theories of affective learning and risk for psychopathology among youth.

Killer "Killer Examples" for Design Patterns

DEFF Research Database (Denmark)

Caspersen, Michael Edelgaard; Alphonce, Carl; Decker, Adrienne

2007-01-01

Giving students an appreciation of the benefits of using design patterns and an ability to use them effectively in developing code presents several interesting pedagogical challenges. This paper discusses pedagogical lessons learned at the "Killer Examples" for Design Patterns and Objects First s...... series of workshops held at the Object Oriented Programming, Systems, Languages and Applications (OOPSLA) conference over the past four years. It also showcases three "killer examples" which can be used to support the teaching of design patterns.......Giving students an appreciation of the benefits of using design patterns and an ability to use them effectively in developing code presents several interesting pedagogical challenges. This paper discusses pedagogical lessons learned at the "Killer Examples" for Design Patterns and Objects First...

Off-line learning from clustered input examples

NARCIS (Netherlands)

Marangi, Carmela; Solla, Sara A.; Biehl, Michael; Riegler, Peter; Marinaro, Maria; Tagliaferri, Roberto

1996-01-01

We analyze the generalization ability of a simple perceptron acting on a structured input distribution for the simple case of two clusters of input data and a linearly separable rule. The generalization ability computed for three learning scenarios: maximal stability, Gibbs, and optimal learning, is

Dopaminergic medication affects choice bias in Parkinson's disease

NARCIS (Netherlands)

Nuland, A.J.M. van; Helmich, R.C.G.; Dirkx, M.F.M.; Zach, H.; Bloem, B.R.; Toni, I.; Cools, R.; Ouden, H.E.M. den

2016-01-01

Objective: Assess dopaminergic effects on choice bias in Parkinson's disease (PD). Background: Bradykinesia, rigidity and resting tremor are the core symptoms of PD, but many patients also suffer from cognitive dysfunction. For instance, PD patients have an increased tendency to learn from aversive

Self-learning Monte Carlo (dynamical biasing)

International Nuclear Information System (INIS)

Matthes, W.

1981-01-01

In many applications the histories of a normal Monte Carlo game rarely reach the target region. An approximate knowledge of the importance (with respect to the target) may be used to guide the particles more frequently into the target region. A Monte Carlo method is presented in which each history contributes to update the importance field such that eventually most target histories are sampled. It is a self-learning method in the sense that the procedure itself: (a) learns which histories are important (reach the target) and increases their probability; (b) reduces the probabilities of unimportant histories; (c) concentrates gradually on the more important target histories. (U.K.)

Bias in estimating food consumption of fish from stomach-content analysis

DEFF Research Database (Denmark)

Rindorf, Anna; Lewy, Peter

2004-01-01

This study presents an analysis of the bias introduced by using simplified methods to calculate food intake of fish from stomach contents. Three sources of bias were considered: (1) the effect of estimating consumption based on a limited number of stomach samples, (2) the effect of using average......, a serious positive bias was introduced by estimating food intake from the contents of pooled stomach samples. An expression is given that can be used to correct analytically for this bias. A new method, which takes into account the distribution and evacuation of individual prey types as well as the effect...... of other food in the stomach on evacuation, is suggested for estimating the intake of separate prey types. Simplifying the estimation by ignoring these factors biased estimates of consumption of individual prey types by up to 150% in a data example....

Is grey literature essential for a better control of publication bias in psychiatry? An example from three meta-analyses of schizophrenia.

Science.gov (United States)

Martin, José Luis R; Pérez, Víctor; Sacristán, Montse; Alvarez, Enric

2005-12-01

Systematic reviews in mental health have become useful tools for health professionals in view of the massive amount and heterogeneous nature of biomedical information available today. In order to determine the risk of bias in the studies evaluated and to avoid bias in generalizing conclusions from the reviews it is therefore important to use a very strict methodology in systematic reviews. One bias which may affect the generalization of results is publication bias, which is determined by the nature and direction of the study results. To control or minimize this type of bias, the authors of systematic reviews undertake comprehensive searches of medical databases and expand on the findings, often undertaking searches of grey literature (material which is not formally published). This paper attempts to show the consequences (and risk) of generalizing the implications of grey literature in the control of publication bias, as was proposed in a recent systematic work. By repeating the analyses for the same outcome from three different systematic reviews that included both published and grey literature our results showed that confusion between grey literature and publication bias may affect the results of a concrete meta-analysis.

Higher Education Outcomes at the National Level on the Example of the Project “Collegiate Learning Assessment”

Directory of Open Access Journals (Sweden)

Sabelnikova E. V.

2015-08-01

Full Text Available We discuss the interpretation of the concept of “learning outcomes”. Theoretical analysis widely represents the interpretations of the learning outcomes of a high school student: academic skills: understanding, application of knowledge to solve problems, synthesis, analysis and evaluation; basic skills and basic knowledge, and skills of a higher order and advanced knowledge; skills of a higher order represented as a system of critical thinking, analytic reasoning, problem solving and written communication; wide abilities interpreted as verbal, quantitative and spatial thinking, understanding, problem solving and decision making. We conclude that each considered approach distinguishes meta-subjective skills, i.e. skills to interact with the quality of information regardless of the context. The ability to measure the meta-skills is discussed on an example of the “Collegiate learning assessment”, realized in the United States

Long-Term Memory Biases Auditory Spatial Attention

Science.gov (United States)

Zimmermann, Jacqueline F.; Moscovitch, Morris; Alain, Claude

2017-01-01

Long-term memory (LTM) has been shown to bias attention to a previously learned visual target location. Here, we examined whether memory-predicted spatial location can facilitate the detection of a faint pure tone target embedded in real world audio clips (e.g., soundtrack of a restaurant). During an initial familiarization task, participants…

The Effect of Cooperative Learning: University Example

Science.gov (United States)

Tombak, Busra; Altun, Sertel

2016-01-01

Problem Statement: Motivation is a significant component of success in education, and it is best achieved by constructivist learning methods, especially cooperative Learning (CL). CL is a popular method among primary and secondary schools, but it is rarely used in higher education due to the large numbers of students and time restrictions. The…

Editorial Bias in Crowd-Sourced Political Information.

Directory of Open Access Journals (Sweden)

Joshua L Kalla

Full Text Available The Internet has dramatically expanded citizens' access to and ability to engage with political information. On many websites, any user can contribute and edit "crowd-sourced" information about important political figures. One of the most prominent examples of crowd-sourced information on the Internet is Wikipedia, a free and open encyclopedia created and edited entirely by users, and one of the world's most accessed websites. While previous studies of crowd-sourced information platforms have found them to be accurate, few have considered biases in what kinds of information are included. We report the results of four randomized field experiments that sought to explore what biases exist in the political articles of this collaborative website. By randomly assigning factually true but either positive or negative and cited or uncited information to the Wikipedia pages of U.S. senators, we uncover substantial evidence of an editorial bias toward positivity on Wikipedia: Negative facts are 36% more likely to be removed by Wikipedia editors than positive facts within 12 hours and 29% more likely within 3 days. Although citations substantially increase an edit's survival time, the editorial bias toward positivity is not eliminated by inclusion of a citation. We replicate this study on the Wikipedia pages of deceased as well as recently retired but living senators and find no evidence of an editorial bias in either. Our results demonstrate that crowd-sourced information is subject to an editorial bias that favors the politically active.

Editorial Bias in Crowd-Sourced Political Information.

Science.gov (United States)

Kalla, Joshua L; Aronow, Peter M

2015-01-01

The Internet has dramatically expanded citizens' access to and ability to engage with political information. On many websites, any user can contribute and edit "crowd-sourced" information about important political figures. One of the most prominent examples of crowd-sourced information on the Internet is Wikipedia, a free and open encyclopedia created and edited entirely by users, and one of the world's most accessed websites. While previous studies of crowd-sourced information platforms have found them to be accurate, few have considered biases in what kinds of information are included. We report the results of four randomized field experiments that sought to explore what biases exist in the political articles of this collaborative website. By randomly assigning factually true but either positive or negative and cited or uncited information to the Wikipedia pages of U.S. senators, we uncover substantial evidence of an editorial bias toward positivity on Wikipedia: Negative facts are 36% more likely to be removed by Wikipedia editors than positive facts within 12 hours and 29% more likely within 3 days. Although citations substantially increase an edit's survival time, the editorial bias toward positivity is not eliminated by inclusion of a citation. We replicate this study on the Wikipedia pages of deceased as well as recently retired but living senators and find no evidence of an editorial bias in either. Our results demonstrate that crowd-sourced information is subject to an editorial bias that favors the politically active.

A method of bias correction for maximal reliability with dichotomous measures.

Science.gov (United States)

Penev, Spiridon; Raykov, Tenko

2010-02-01

This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.

Variable-bias coin tossing

International Nuclear Information System (INIS)

Colbeck, Roger; Kent, Adrian

2006-01-01

Alice is a charismatic quantum cryptographer who believes her parties are unmissable; Bob is a (relatively) glamorous string theorist who believes he is an indispensable guest. To prevent possibly traumatic collisions of self-perception and reality, their social code requires that decisions about invitation or acceptance be made via a cryptographically secure variable-bias coin toss (VBCT). This generates a shared random bit by the toss of a coin whose bias is secretly chosen, within a stipulated range, by one of the parties; the other party learns only the random bit. Thus one party can secretly influence the outcome, while both can save face by blaming any negative decisions on bad luck. We describe here some cryptographic VBCT protocols whose security is guaranteed by quantum theory and the impossibility of superluminal signaling, setting our results in the context of a general discussion of secure two-party computation. We also briefly discuss other cryptographic applications of VBCT

Variable-bias coin tossing

Science.gov (United States)

Colbeck, Roger; Kent, Adrian

2006-03-01

Alice is a charismatic quantum cryptographer who believes her parties are unmissable; Bob is a (relatively) glamorous string theorist who believes he is an indispensable guest. To prevent possibly traumatic collisions of self-perception and reality, their social code requires that decisions about invitation or acceptance be made via a cryptographically secure variable-bias coin toss (VBCT). This generates a shared random bit by the toss of a coin whose bias is secretly chosen, within a stipulated range, by one of the parties; the other party learns only the random bit. Thus one party can secretly influence the outcome, while both can save face by blaming any negative decisions on bad luck. We describe here some cryptographic VBCT protocols whose security is guaranteed by quantum theory and the impossibility of superluminal signaling, setting our results in the context of a general discussion of secure two-party computation. We also briefly discuss other cryptographic applications of VBCT.

Collaborative Learning in Practice : Examples from Natural ...

International Development Research Centre (IDRC) Digital Library (Canada)

1 déc. 2010 ... Parmi ses publications récentes, citons Learning from the Field: Innovating China's Higher Education System (Foundation Books et CRDI, 2008) et Social and Gender Analysis in Natural Resource Management: Learning Studies and Lessons from Asia (Sage India, CAP et CRDI, 2006). Edición español: ...

Psychometric Principles in Measurement for Geoscience Education Research: A Climate Change Example

Science.gov (United States)

Libarkin, J. C.; Gold, A. U.; Harris, S. E.; McNeal, K.; Bowles, R.

2015-12-01

Understanding learning in geoscience classrooms requires that we use valid and reliable instruments aligned with intended learning outcomes. Nearly one hundred instruments assessing conceptual understanding in undergraduate science and engineering classrooms (often called concept inventories) have been published and are actively being used to investigate learning. The techniques used to develop these instruments vary widely, often with little attention to psychometric principles of measurement. This paper will discuss the importance of using psychometric principles to design, evaluate, and revise research instruments, with particular attention to the validity and reliability steps that must be undertaken to ensure that research instruments are providing meaningful measurement. An example from a climate change inventory developed by the authors will be used to exemplify the importance of validity and reliability, including the value of item response theory for instrument development. A 24-item instrument was developed based on published items, conceptions research, and instructor experience. Rasch analysis of over 1000 responses provided evidence for the removal of 5 items for misfit and one item for potential bias as measured via differential item functioning. The resulting 18-item instrument can be considered a valid and reliable measure based on pre- and post-implementation metrics. Consideration of the relationship between respondent demographics and concept inventory scores provides unique insight into the relationship between gender, religiosity, values and climate change understanding.

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Science.gov (United States)

Malakar, Nabin K.; Lary, D. L.; Moore, A.; Gencaga, D.; Roscoe, B.; Albayrak, Arif; Petrenko, Maksym; Wei, Jennifer

2012-01-01

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

Science.gov (United States)

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

Optimism Bias in Fans and Sports Reporters

Science.gov (United States)

Love, Bradley C.

2015-01-01

People are optimistic about their prospects relative to others. However, existing studies can be difficult to interpret because outcomes are not zero-sum. For example, one person avoiding cancer does not necessitate that another person develops cancer. Ideally, optimism bias would be evaluated within a closed formal system to establish with certainty the extent of the bias and the associated environmental factors, such that optimism bias is demonstrated when a population is internally inconsistent. Accordingly, we asked NFL fans to predict how many games teams they liked and disliked would win in the 2015 season. Fans, like ESPN reporters assigned to cover a team, were overly optimistic about their team’s prospects. The opposite pattern was found for teams that fans disliked. Optimism may flourish because year-to-year team results are marked by auto-correlation and regression to the group mean (i.e., good teams stay good, but bad teams improve). PMID:26352146

Gender bias in medical textbooks: examples from coronary heart disease, depression, alcohol abuse and pharmacology

NARCIS (Netherlands)

Dijkstra, A.F.; Verdonk, P.; Lagro-Janssen, A.L.M.

2008-01-01

OBJECTIVES: This study aimed to review the availability and accessibility of gender-specific knowledge in current medical textbooks used in Dutch medical schools. Medicine has been criticised as being gender-biased by assuming male and female bodies to be generally the same. The authors wondered

Gender bias in medical textbooks: examples from coronary heart disease, depression, alcohol abuse and pharmacology.

NARCIS (Netherlands)

Dijkstra, A.F.; Verdonk, P.; Lagro-Janssen, A.L.M.

2008-01-01

OBJECTIVES: This study aimed to review the availability and accessibility of gender-specific knowledge in current medical textbooks used in Dutch medical schools. Medicine has been criticised as being gender-biased by assuming male and female bodies to be generally the same. The authors wondered

Rational Learning and Information Sampling: On the "Naivety" Assumption in Sampling Explanations of Judgment Biases

Science.gov (United States)

Le Mens, Gael; Denrell, Jerker

2011-01-01

Recent research has argued that several well-known judgment biases may be due to biases in the available information sample rather than to biased information processing. Most of these sample-based explanations assume that decision makers are "naive": They are not aware of the biases in the available information sample and do not correct for them.…

Two biased estimation techniques in linear regression: Application to aircraft

Science.gov (United States)

Klein, Vladislav

1988-01-01

Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.

Bias by default? A means for a priori interface measurement

Energy Technology Data Exchange (ETDEWEB)

Cottam, Joseph A.; Blaha, Leslie M.

2017-10-03

Systems have biases. Their interfaces naturally guide a user toward specific patterns of action. For example, modern word-processors and spreadsheets are both capable of taking word wrapping, checking spelling, storing tables, and calculating formulas. You could write a paper in a spreadsheet or could do simple business modeling in a word-processor. However, their interfaces naturally communicate which function they are designed for. Visual analytic interfaces also have biases. In this paper, we outline why simple Markov models are a plausible tool for investigating that bias and how they might be applied. We also discuss some anticipated difficulties in such modeling and touch briefly on what some Markov model extensions might provide.

Speed Biases With Real-Life Video Clips

Directory of Open Access Journals (Sweden)

Federica Rossi

2018-03-01

Full Text Available We live almost literally immersed in an artificial visual world, especially motion pictures. In this exploratory study, we asked whether the best speed for reproducing a video is its original, shooting speed. By using adjustment and double staircase methods, we examined speed biases in viewing real-life video clips in three experiments, and assessed their robustness by manipulating visual and auditory factors. With the tested stimuli (short clips of human motion, mixed human-physical motion, physical motion and ego-motion, speed underestimation was the rule rather than the exception, although it depended largely on clip content, ranging on average from 2% (ego-motion to 32% (physical motion. Manipulating display size or adding arbitrary soundtracks did not modify these speed biases. Estimated speed was not correlated with estimated duration of these same video clips. These results indicate that the sense of speed for real-life video clips can be systematically biased, independently of the impression of elapsed time. Measuring subjective visual tempo may integrate traditional methods that assess time perception: speed biases may be exploited to develop a simple, objective test of reality flow, to be used for example in clinical and developmental contexts. From the perspective of video media, measuring speed biases may help to optimize video reproduction speed and validate “natural” video compression techniques based on sub-threshold temporal squeezing.

All in its proper time: monitoring the emergence of a memory bias for novel, arousing-negative words in individuals with high and low trait anxiety.

Science.gov (United States)

Eden, Annuschka Salima; Zwitserlood, Pienie; Keuper, Katharina; Junghöfer, Markus; Laeger, Inga; Zwanzger, Peter; Dobel, Christian

2014-01-01

The well-established memory bias for arousing-negative stimuli seems to be enhanced in high trait-anxious persons and persons suffering from anxiety disorders. We monitored the emergence and development of such a bias during and after learning, in high and low trait anxious participants. A word-learning paradigm was applied, consisting of spoken pseudowords paired either with arousing-negative or neutral pictures. Learning performance during training evidenced a short-lived advantage for arousing-negative associated words, which was not present at the end of training. Cued recall and valence ratings revealed a memory bias for pseudowords that had been paired with arousing-negative pictures, immediately after learning and two weeks later. This held even for items that were not explicitly remembered. High anxious individuals evidenced a stronger memory bias in the cued-recall test, and their ratings were also more negative overall compared to low anxious persons. Both effects were evident, even when explicit recall was controlled for. Regarding the memory bias in anxiety prone persons, explicit memory seems to play a more crucial role than implicit memory. The study stresses the need for several time points of bias measurement during the course of learning and retrieval, as well as the employment of different measures for learning success.

All in its proper time: monitoring the emergence of a memory bias for novel, arousing-negative words in individuals with high and low trait anxiety.

Directory of Open Access Journals (Sweden)

Annuschka Salima Eden

Full Text Available The well-established memory bias for arousing-negative stimuli seems to be enhanced in high trait-anxious persons and persons suffering from anxiety disorders. We monitored the emergence and development of such a bias during and after learning, in high and low trait anxious participants. A word-learning paradigm was applied, consisting of spoken pseudowords paired either with arousing-negative or neutral pictures. Learning performance during training evidenced a short-lived advantage for arousing-negative associated words, which was not present at the end of training. Cued recall and valence ratings revealed a memory bias for pseudowords that had been paired with arousing-negative pictures, immediately after learning and two weeks later. This held even for items that were not explicitly remembered. High anxious individuals evidenced a stronger memory bias in the cued-recall test, and their ratings were also more negative overall compared to low anxious persons. Both effects were evident, even when explicit recall was controlled for. Regarding the memory bias in anxiety prone persons, explicit memory seems to play a more crucial role than implicit memory. The study stresses the need for several time points of bias measurement during the course of learning and retrieval, as well as the employment of different measures for learning success.

Can Decision Biases Improve Insurance Outcomes? An Experiment on Status Quo Bias in Health Insurance Choice

Science.gov (United States)

Krieger, Miriam; Felder, Stefan

2013-01-01

Rather than conforming to the assumption of perfect rationality in neoclassical economic theory, decision behavior has been shown to display a host of systematic biases. Properly understood, these patterns can be instrumentalized to improve outcomes in the public realm. We conducted a laboratory experiment to study whether decisions over health insurance policies are subject to status quo bias and, if so, whether experience mitigates this framing effect. Choices in two treatment groups with status quo defaults are compared to choices in a neutrally framed control group. A two-step design features sorting of subjects into the groups, allowing us to control for selection effects due to risk preferences. The results confirm the presence of a status quo bias in consumer choices over health insurance policies. However, this effect of the default framing does not persist as subjects repeat this decision in later periods of the experiment. Our results have implications for health care policy, for example suggesting that the use of non-binding defaults in health insurance can facilitate the spread of co-insurance policies and thereby help contain health care expenditure. PMID:23783222

Can Decision Biases Improve Insurance Outcomes? An Experiment on Status Quo Bias in Health Insurance Choice

Directory of Open Access Journals (Sweden)

Stefan Felder

2013-06-01

Full Text Available Rather than conforming to the assumption of perfect rationality in neoclassical economic theory, decision behavior has been shown to display a host of systematic biases. Properly understood, these patterns can be instrumentalized to improve outcomes in the public realm. We conducted a laboratory experiment to study whether decisions over health insurance policies are subject to status quo bias and, if so, whether experience mitigates this framing effect. Choices in two treatment groups with status quo defaults are compared to choices in a neutrally framed control group. A two-step design features sorting of subjects into the groups, allowing us to control for selection effects due to risk preferences. The results confirm the presence of a status quo bias in consumer choices over health insurance policies. However, this effect of the default framing does not persist as subjects repeat this decision in later periods of the experiment. Our results have implications for health care policy, for example suggesting that the use of non-binding defaults in health insurance can facilitate the spread of co-insurance policies and thereby help contain health care expenditure.

Diversity Matters in Academic Radiology: Acknowledging and Addressing Unconscious Bias.

Science.gov (United States)

Allen, Brenda J; Garg, Kavita

2016-12-01

To meet challenges related to changing demographics, and to optimize the promise of diversity, radiologists must bridge the gap between numbers of women and historically underrepresented minorities in radiology and radiation oncology as contrasted with other medical specialties. Research reveals multiple ways that women and underrepresented minorities can benefit radiology education, research, and practice. To achieve those benefits, promising practices promote developing and implementing strategies that support diversity as an institutional priority and cultivate shared responsibility among all members to create inclusive learning and workplace environments. Strategies also include providing professional development to empower and equip members to accomplish diversity-related goals. Among topics for professional development about diversity, unconscious bias has shown positive results. Unconscious bias refers to ways humans unknowingly draw upon assumptions about individuals and groups to make decisions about them. Researchers have documented unconscious bias in a variety of contexts and professions, including health care, in which they have studied differential treatment, diagnosis, prescribed care, patient well-being and compliance, physician-patient interactions, clinical decision making, and medical school education. These studies demonstrate unfavorable impacts on members of underrepresented groups and women. Learning about and striving to counteract unconscious bias points to promising practices for increasing the numbers of women and underrepresented minorities in the radiology and radiation oncology workforce. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Positivity effect in healthy aging in observational but not active feedback-learning.

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Bellebaum, Christian; Rustemeier, Martina; Daum, Irene

2012-01-01

The present study investigated the impact of healthy aging on the bias to learn from positive or negative performance feedback in observational and active feedback learning. In active learning, a previous study had already shown a negative learning bias in healthy seniors older than 75 years, while no bias was found for younger seniors. However, healthy aging is accompanied by a 'positivity effect', a tendency to primarily attend to stimuli with positive valence. Based on recent findings of dissociable neural mechanisms in active and observational feedback learning, the positivity effect was hypothesized to influence older participants' observational feedback learning in particular. In two separate experiments, groups of young (mean age 27) and older participants (mean age 60 years) completed an observational or active learning task designed to differentially assess positive and negative learning. Older but not younger observational learners showed a significant bias to learn better from positive than negative feedback. In accordance with previous findings, no bias was found for active learning. This pattern of results is discussed in terms of differences in the neural underpinnings of active and observational learning from performance feedback.

Simplicity and Specificity in Language: Domain-General Biases Have Domain-Specific Effects

Science.gov (United States)

Culbertson, Jennifer; Kirby, Simon

2016-01-01

The extent to which the linguistic system—its architecture, the representations it operates on, the constraints it is subject to—is specific to language has broad implications for cognitive science and its relation to evolutionary biology. Importantly, a given property of the linguistic system can be “specific” to the domain of language in several ways. For example, if the property evolved by natural selection under the pressure of the linguistic function it serves then the property is domain-specific in the sense that its design is tailored for language. Equally though, if that property evolved to serve a different function or if that property is domain-general, it may nevertheless interact with the linguistic system in a way that is unique. This gives a second sense in which a property can be thought of as specific to language. An evolutionary approach to the language faculty might at first blush appear to favor domain-specificity in the first sense, with individual properties of the language faculty being specifically linguistic adaptations. However, we argue that interactions between learning, culture, and biological evolution mean any domain-specific adaptations that evolve will take the form of weak biases rather than hard constraints. Turning to the latter sense of domain-specificity, we highlight a very general bias, simplicity, which operates widely in cognition and yet interacts with linguistic representations in domain-specific ways. PMID:26793132

A selective emotional decision-making bias elicited by facial expressions.

Directory of Open Access Journals (Sweden)

Nicholas Furl

Full Text Available Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices.

A Selective Emotional Decision-Making Bias Elicited by Facial Expressions

Science.gov (United States)

Furl, Nicholas; Gallagher, Shannon; Averbeck, Bruno B.

2012-01-01

Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices. PMID:22438936

A selective emotional decision-making bias elicited by facial expressions.

Science.gov (United States)

Furl, Nicholas; Gallagher, Shannon; Averbeck, Bruno B

2012-01-01

Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection

OpenAIRE

Kwan, Johnny S. H.; Kung, Annie W. C.; Sham, Pak C.

2011-01-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias. © The Author(s) 2011.

A Framework for Integrating Implicit Bias Recognition Into Health Professions Education.

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Sukhera, Javeed; Watling, Chris

2018-01-01

Existing literature on implicit bias is fragmented and comes from a variety of fields like cognitive psychology, business ethics, and higher education, but implicit-bias-informed educational approaches have been underexplored in health professions education and are difficult to evaluate using existing tools. Despite increasing attention to implicit bias recognition and management in health professions education, many programs struggle to meaningfully integrate these topics into curricula. The authors propose a six-point actionable framework for integrating implicit bias recognition and management into health professions education that draws on the work of previous researchers and includes practical tools to guide curriculum developers. The six key features of this framework are creating a safe and nonthreatening learning context, increasing knowledge about the science of implicit bias, emphasizing how implicit bias influences behaviors and patient outcomes, increasing self-awareness of existing implicit biases, improving conscious efforts to overcome implicit bias, and enhancing awareness of how implicit bias influences others. Important considerations for designing implicit-bias-informed curricula-such as individual and contextual variables, as well as formal and informal cultural influences-are discussed. The authors also outline assessment and evaluation approaches that consider outcomes at individual, organizational, community, and societal levels. The proposed framework may facilitate future research and exploration regarding the use of implicit bias in health professions education.

Family matters: Directionality of turning bias while kissing is modulated by context.

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Sedgewick, Jennifer R; Elias, Lorin J

2016-01-28

When leaning forward to kiss to a romantic partner, individuals tend to direct their kiss to the right more often than the left. Studies have consistently demonstrated this kissing asymmetry, although other factors known to influence lateral biases, such as sex or situational context, had yet to be explored. The primary purpose of our study was to investigate if turning direction was consistent between a romantic (parent-parent) and parental (parent-child) kissing context, and secondly, to examine if sex differences influenced turning bias between parent-child kissing partners. An archival analysis coded the direction of turning bias for 161 images of romantic kissing (mothers kissing fathers) and 529 images of parental kissing (mothers or fathers kissing sons or daughters). The results indicated that the direction of turning bias differed between kissing contexts. As expected, a right-turn bias was observed for romantic kissing; however, a left-turn bias was exhibited for parental kissing. There was no significant difference of turning bias between any parent-child kissing partners. Interpretations for the left-turn bias discuss parental kissing as a learned lateral behaviour.

Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

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Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J

2017-02-01

Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.

Organizational Learning Capability: An Example of University Hospital

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Yasin UZUNTARLA

2015-06-01

Full Text Available In health care institutions aiming healthy society by the way protecting and promoting human health, reaching information has a vital importance. This descriptive research purposed an evaluation of organizational learning capability of 396 employees working in Gülhane Military Medical Academy Hospital. A questionnaire including socio-demographic characteristics was used along with Organizational Learning Capability scale designed by Ricardo CHIVA and His Friends. Data acquired was analyzed with SPSS 15.0 program. Participants’ Organizational Learning Capability and its subscales means were assessed in terms of their sociodemographic characteristics. Assessing participants’ answers in terms of 5 subscales which are experimentation, risk taking, interaction with the external environment, dialogue and participatory decision-making; for education level and professional groups, statistical significant differences was found between Organizational Learning Capability and its subscales means.

Method for exploiting bias in factor analysis using constrained alternating least squares algorithms

Science.gov (United States)

Keenan, Michael R.

2008-12-30

Bias plays an important role in factor analysis and is often implicitly made use of, for example, to constrain solutions to factors that conform to physical reality. However, when components are collinear, a large range of solutions may exist that satisfy the basic constraints and fit the data equally well. In such cases, the introduction of mathematical bias through the application of constraints may select solutions that are less than optimal. The biased alternating least squares algorithm of the present invention can offset mathematical bias introduced by constraints in the standard alternating least squares analysis to achieve factor solutions that are most consistent with physical reality. In addition, these methods can be used to explicitly exploit bias to provide alternative views and provide additional insights into spectral data sets.

Rational learning and information sampling: on the "naivety" assumption in sampling explanations of judgment biases.

Science.gov (United States)

Le Mens, Gaël; Denrell, Jerker

2011-04-01

Recent research has argued that several well-known judgment biases may be due to biases in the available information sample rather than to biased information processing. Most of these sample-based explanations assume that decision makers are "naive": They are not aware of the biases in the available information sample and do not correct for them. Here, we show that this "naivety" assumption is not necessary. Systematically biased judgments can emerge even when decision makers process available information perfectly and are also aware of how the information sample has been generated. Specifically, we develop a rational analysis of Denrell's (2005) experience sampling model, and we prove that when information search is interested rather than disinterested, even rational information sampling and processing can give rise to systematic patterns of errors in judgments. Our results illustrate that a tendency to favor alternatives for which outcome information is more accessible can be consistent with rational behavior. The model offers a rational explanation for behaviors that had previously been attributed to cognitive and motivational biases, such as the in-group bias or the tendency to prefer popular alternatives. 2011 APA, all rights reserved

Statistical methods for elimination of guarantee-time bias in cohort studies: a simulation study

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In Sung Cho

2017-08-01

Full Text Available Abstract Background Aspirin has been considered to be beneficial in preventing cardiovascular diseases and cancer. Several pharmaco-epidemiology cohort studies have shown protective effects of aspirin on diseases using various statistical methods, with the Cox regression model being the most commonly used approach. However, there are some inherent limitations to the conventional Cox regression approach such as guarantee-time bias, resulting in an overestimation of the drug effect. To overcome such limitations, alternative approaches, such as the time-dependent Cox model and landmark methods have been proposed. This study aimed to compare the performance of three methods: Cox regression, time-dependent Cox model and landmark method with different landmark times in order to address the problem of guarantee-time bias. Methods Through statistical modeling and simulation studies, the performance of the above three methods were assessed in terms of type I error, bias, power, and mean squared error (MSE. In addition, the three statistical approaches were applied to a real data example from the Korean National Health Insurance Database. Effect of cumulative rosiglitazone dose on the risk of hepatocellular carcinoma was used as an example for illustration. Results In the simulated data, time-dependent Cox regression outperformed the landmark method in terms of bias and mean squared error but the type I error rates were similar. The results from real-data example showed the same patterns as the simulation findings. Conclusions While both time-dependent Cox regression model and landmark analysis are useful in resolving the problem of guarantee-time bias, time-dependent Cox regression is the most appropriate method for analyzing cumulative dose effects in pharmaco-epidemiological studies.

Simulations with Elaborated Worked Example Modeling: Beneficial Effects on Schema Acquisition

Science.gov (United States)

Meier, Debra K.; Reinhard, Karl J.; Carter, David O.; Brooks, David W.

2008-01-01

Worked examples have been effective in enhancing learning outcomes, especially with novice learners. Most of this research has been conducted in laboratory settings. This study examined the impact of embedding elaborated worked example modeling in a computer simulation practice activity on learning achievement among 39 undergraduate students…

Local linear density estimation for filtered survival data, with bias correction

DEFF Research Database (Denmark)

Nielsen, Jens Perch; Tanggaard, Carsten; Jones, M.C.

2009-01-01

it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a 'pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias-correction methods...... within our framework. The multiplicative bias-correction method proves to be the best in a simulation study comparing the performance of the considered estimators. An example concerning old-age mortality demonstrates the importance of the improvements provided....

Local Linear Density Estimation for Filtered Survival Data with Bias Correction

DEFF Research Database (Denmark)

Tanggaard, Carsten; Nielsen, Jens Perch; Jones, M.C.

it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a ‘pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias correction methods...... within our framework. The multiplicative bias correction method proves to be best in a simulation study comparing the performance of the considered estimators. An example concerning old age mortality demonstrates the importance of the improvements provided....

Back-dropout Transfer Learning for Action Recognition

DEFF Research Database (Denmark)

Ren, Huamin; Kanhabua, Nattiya; Møgelmose, Andreas

2018-01-01

transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category...

Content specificity of attention bias to threat in anxiety disorders: a meta-analysis.

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Pergamin-Hight, Lee; Naim, Reut; Bakermans-Kranenburg, Marian J; van IJzendoorn, Marinus H; Bar-Haim, Yair

2015-02-01

Despite the established evidence for threat-related attention bias in anxiety, the mechanisms underlying this bias remain unclear. One important unresolved question is whether disorder-congruent threats capture attention to a greater extent than do more general or disorder-incongruent threat stimuli. Evidence for attention bias specificity in anxiety would implicate involvement of previous learning and memory processes in threat-related attention bias, whereas lack of content specificity would point to perturbations in more generic attention processes. Enhanced clarity of mechanism could have clinical implications for the stimuli types used in Attention Bias Modification Treatments (ABMT). Content specificity of threat-related attention bias in anxiety and potential moderators of this effect were investigated. A systematic search identified 37 samples from 29 articles (N=866). Relevant data were extracted based on specific coding rules, and Cohen's d effect size was used to estimate bias specificity effects. The results indicate greater attention bias toward disorder-congruent relative to disorder-incongruent threat stimuli (d=0.28, pattention tasks, or type of disorder-incongruent stimuli. No evidence of publication bias was observed. Implications for threat bias in anxiety and ABMT are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Gaze Bias in Preference Judgments by Younger and Older Adults

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Toshiki Saito

2017-08-01

Full Text Available Individuals’ gaze behavior reflects the choice they will ultimately make. For example, people confronting a choice among multiple stimuli tend to look longer at stimuli that are subsequently chosen than at other stimuli. This tendency, called the gaze bias effect, is a key aspect of visual decision-making. Nevertheless, no study has examined the generality of the gaze bias effect in older adults. Here, we used a two-alternative forced-choice task (2AFC to compare the gaze behavior reflective of different stages of decision processes demonstrated by younger and older adults. Participants who had viewed two faces were instructed to choose the one that they liked/disliked or the one that they judged to be more/less similar to their own face. Their eye movements were tracked while they chose. The results show that the gaze bias effect occurred during the remaining time in both age groups irrespective of the decision type. However, no gaze bias effect was observed for the preference judgment during the first dwell time. Our study demonstrated that the gaze bias during the remaining time occurred regardless of decision-making task and age. Further study using diverse participants, such as clinic patients or infants, may help to generalize the gaze bias effect and to elucidate the mechanisms underlying the gaze bias.

Language learning, language use and the evolution of linguistic variation

Science.gov (United States)

Perfors, Amy; Fehér, Olga; Samara, Anna; Swoboda, Kate; Wonnacott, Elizabeth

2017-01-01

Linguistic universals arise from the interaction between the processes of language learning and language use. A test case for the relationship between these factors is linguistic variation, which tends to be conditioned on linguistic or sociolinguistic criteria. How can we explain the scarcity of unpredictable variation in natural language, and to what extent is this property of language a straightforward reflection of biases in statistical learning? We review three strands of experimental work exploring these questions, and introduce a Bayesian model of the learning and transmission of linguistic variation along with a closely matched artificial language learning experiment with adult participants. Our results show that while the biases of language learners can potentially play a role in shaping linguistic systems, the relationship between biases of learners and the structure of languages is not straightforward. Weak biases can have strong effects on language structure as they accumulate over repeated transmission. But the opposite can also be true: strong biases can have weak or no effects. Furthermore, the use of language during interaction can reshape linguistic systems. Combining data and insights from studies of learning, transmission and use is therefore essential if we are to understand how biases in statistical learning interact with language transmission and language use to shape the structural properties of language. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872370

Language learning, language use and the evolution of linguistic variation.

Science.gov (United States)

Smith, Kenny; Perfors, Amy; Fehér, Olga; Samara, Anna; Swoboda, Kate; Wonnacott, Elizabeth

2017-01-05

Linguistic universals arise from the interaction between the processes of language learning and language use. A test case for the relationship between these factors is linguistic variation, which tends to be conditioned on linguistic or sociolinguistic criteria. How can we explain the scarcity of unpredictable variation in natural language, and to what extent is this property of language a straightforward reflection of biases in statistical learning? We review three strands of experimental work exploring these questions, and introduce a Bayesian model of the learning and transmission of linguistic variation along with a closely matched artificial language learning experiment with adult participants. Our results show that while the biases of language learners can potentially play a role in shaping linguistic systems, the relationship between biases of learners and the structure of languages is not straightforward. Weak biases can have strong effects on language structure as they accumulate over repeated transmission. But the opposite can also be true: strong biases can have weak or no effects. Furthermore, the use of language during interaction can reshape linguistic systems. Combining data and insights from studies of learning, transmission and use is therefore essential if we are to understand how biases in statistical learning interact with language transmission and language use to shape the structural properties of language.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Authors.

To what degree does the missing-data technique influence the estimated growth in learning strategies over time? A tutorial example of sensitivity analysis for longitudinal data.

Science.gov (United States)

Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

2017-01-01

Longitudinal data is almost always burdened with missing data. However, in educational and psychological research, there is a large discrepancy between methodological suggestions and research practice. The former suggests applying sensitivity analysis in order to the robustness of the results in terms of varying assumptions regarding the mechanism generating the missing data. However, in research practice, participants with missing data are usually discarded by relying on listwise deletion. To help bridge the gap between methodological recommendations and applied research in the educational and psychological domain, this study provides a tutorial example of sensitivity analysis for latent growth analysis. The example data concern students' changes in learning strategies during higher education. One cohort of students in a Belgian university college was asked to complete the Inventory of Learning Styles-Short Version, in three measurement waves. A substantial number of students did not participate on each occasion. Change over time in student learning strategies was assessed using eight missing data techniques, which assume different mechanisms for missingness. The results indicated that, for some learning strategy subscales, growth estimates differed between the models. Guidelines in terms of reporting the results from sensitivity analysis are synthesised and applied to the results from the tutorial example.

Growth hormone biases amygdala network activation after fear learning.

Science.gov (United States)

Gisabella, B; Farah, S; Peng, X; Burgos-Robles, A; Lim, S H; Goosens, K A

2016-11-29

Prolonged stress exposure is a risk factor for developing posttraumatic stress disorder, a disorder characterized by the 'over-encoding' of a traumatic experience. A potential mechanism by which this occurs is through upregulation of growth hormone (GH) in the amygdala. Here we test the hypotheses that GH promotes the over-encoding of fearful memories by increasing the number of neurons activated during memory encoding and biasing the allocation of neuronal activation, one aspect of the process by which neurons compete to encode memories, to favor neurons that have stronger inputs. Viral overexpression of GH in the amygdala increased the number of amygdala cells activated by fear memory formation. GH-overexpressing cells were especially biased to express the immediate early gene c-Fos after fear conditioning, revealing strong autocrine actions of GH in the amygdala. In addition, we observed dramatically enhanced dendritic spine density in GH-overexpressing neurons. These data elucidate a previously unrecognized autocrine role for GH in the regulation of amygdala neuron function and identify specific mechanisms by which chronic stress, by enhancing GH in the amygdala, may predispose an individual to excessive fear memory formation.

Mobile Learning Based Worked Example in Electric Circuit (WEIEC) Application to Improve the High School Students' Electric Circuits Interpretation Ability

Science.gov (United States)

Yadiannur, Mitra; Supahar

2017-01-01

This research aims to determine the feasibility and effectivity of mobile learning based Worked Example in Electric Circuits (WEIEC) application in improving the high school students' electric circuits interpretation ability on Direct Current Circuits materials. The research method used was a combination of Four-D Models and ADDIE model. The…

Strategi Mengatasi Common Measures Bias dalam Balanced Scorecard

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Sekar Akrom Faradiza

2016-06-01

Balanced Scorecard (BSC is a comprehensive performance measurement. BSC is not only used financial indicators but also non financial indicators there are customer, internal process business and learning and growth perspective. By using BSC, evaluators have common and unique measures. When evaluate manager performance, evaluator tends to only use common measures and ignore unique measures. This is called common measures bias. This study aims to investigate whether dissaggregated and aggregated BSC and management communication can overcome common measures bias and intent to BSC approach. This study also will evaluate whether these approach will affect evaluator decision when allocated compensation. We conduct 2x2x2 experiment of undergraduate accounting students. Participant act as a senior manager and evaluate the performance of two divisions and then allocated the bonus. ANOVA repeated measurement are used to conduct hypothesis test. The results showed that dissaggregated BSC and management communication could not overcome common measures bias but effected  management decision when allocated compensation.

Learning by Example: Designing and Developing Linked Data Application

Science.gov (United States)

Tharani, Karim

2016-01-01

According to constructivist theory of learning, new knowledge is learned on the basis of what is already known by learners. Thus for an emerging and transformative technology such as Linked Data to be learned, the technology must be made relevant for learners and must be compatible with their skillset. Designing and developing Linked Data…

Who Learns More? Cultural Differences in Implicit Sequence Learning

Science.gov (United States)

Fu, Qiufang; Dienes, Zoltan; Shang, Junchen; Fu, Xiaolan

2013-01-01

Background It is well documented that East Asians differ from Westerners in conscious perception and attention. However, few studies have explored cultural differences in unconscious processes such as implicit learning. Methodology/Principal Findings The global-local Navon letters were adopted in the serial reaction time (SRT) task, during which Chinese and British participants were instructed to respond to global or local letters, to investigate whether culture influences what people acquire in implicit sequence learning. Our results showed that from the beginning British expressed a greater local bias in perception than Chinese, confirming a cultural difference in perception. Further, over extended exposure, the Chinese learned the target regularity better than the British when the targets were global, indicating a global advantage for Chinese in implicit learning. Moreover, Chinese participants acquired greater unconscious knowledge of an irrelevant regularity than British participants, indicating that the Chinese were more sensitive to contextual regularities than the British. Conclusions/Significance The results suggest that cultural biases can profoundly influence both what people consciously perceive and unconsciously learn. PMID:23940773

Lost in translation: Review of identification bias, translation bias and research waste in dentistry.

Science.gov (United States)

Layton, Danielle M; Clarke, Michael

2016-01-01

To review how articles are retrieved from bibliographic databases, what article identification and translation problems have affected research, and how these problems can contribute to research waste and affect clinical practice. This literature review sought and appraised articles regarding identification- and translation-bias in the medical and dental literature, which limit the ability of users to find research articles and to use these in practice. Articles can be retrieved from bibliographic databases by performing a word or index-term (for example, MeSH for MEDLINE) search. Identification of articles is challenging when it is not clear which words are most relevant, and which terms have been allocated to indexing fields. Poor reporting quality of abstracts and articles has been reported across the medical literature at large. Specifically in dentistry, research regarding time-to-event survival analyses found the allocation of MeSH terms to be inconsistent and inaccurate, important words were omitted from abstracts by authors, and the quality of reporting in the body of articles was generally poor. These shortcomings mean that articles will be difficult to identify, and difficult to understand if found. Use of specialized electronic search strategies can decrease identification bias, and use of tailored reporting guidelines can decrease translation bias. Research that cannot be found, or cannot be used results in research waste, and undermines clinical practice. Identification- and translation-bias have been shown to affect time-to-event dental articles, are likely affect other fields of research, and are largely unrecognized by authors and evidence seekers alike. By understanding that the problems exist, solutions can be sought to improve identification and translation of our research. Copyright © 2015 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Eagerness and Optimistically Biased Metaperception: The More Eager to Learn Others’ Evaluations, the Higher the Estimation of Others’ Evaluations

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Jingyi Lu

2018-05-01

Full Text Available People frequently judge how they are viewed by others during social interactions. These judgments are called metaperceptions. This study investigates the relationship between eagerness to determine the evaluation of others and metaperceptions. We propose that eagerness, which reflects approach motivation, induces positive emotions. We apply feelings-as-information theory and hypothesize that positive emotions cause optimistic self-evaluations and metaperceptions. Participants in three studies interact with judges during a singing contest (Study 1, a speech (Study 2, and an interview (Study 3. Results corroborate that eagerness to learn the evaluation of others is overall related to optimistically biased metaperceptions. This effect is mediated sequentially by positive emotions, optimistic self-evaluations, and increased metaperceptions.

Examples of learning activities for Earth and Space Sciences in the new Italian National curriculum

Science.gov (United States)

Macario, Maddalena

2016-04-01

In the last few years, starting from 2010, science curricula were changed dramatically in the secondary Italian school as consequence of a radical law reform. In particular, Earth Science and Astronomy subjects have been shifted from the last to the previous school years; in addition, these subjects have been integrated with other natural sciences learning, such as biology and chemistry. As a consequence, Italian teachers felt forced to totally revise their teaching methods for all of these disciplines. The most demanding need was adapting content to younger learners, as those of the first years are, who usually do have neither pre-knowledge in physics nor high level maths skills. Secondly, content learning was progressively driven toward a greater attention to environmental issues in order to raise more awareness in learners about global changes as examples of fragile equilibrium of our planet. In this work some examples of activities are shown, to introduce students to some astronomical phenomena in a simpler way, which play a key role in influencing other Earth's events, in order to make pupils more conscious about how and to what extent our planet depends on space, at different time scales. The activities range from moon motions affecting tides, to secondary Earth motions, which are responsible for climate changes, to the possibility to find life forms in other parts of the Universe, to the possibility for humans to live in the space for future space missions. Students are involved in hands-on inquiry-based laboratories that scaffold both theoretic knowledge and practical skills for a deeper understanding of cause-effect relationships existing in the Earth.

Target selection biases from recent experience transfer across effectors.

Science.gov (United States)

Moher, Jeff; Song, Joo-Hyun

2016-02-01

Target selection is often biased by an observer's recent experiences. However, not much is known about whether these selection biases influence behavior across different effectors. For example, does looking at a red object make it easier to subsequently reach towards another red object? In the current study, we asked observers to find the uniquely colored target object on each trial. Randomly intermixed pre-trial cues indicated the mode of action: either an eye movement or a visually guided reach movement to the target. In Experiment 1, we found that priming of popout, reflected in faster responses following repetition of the target color on consecutive trials, occurred regardless of whether the effector was repeated from the previous trial or not. In Experiment 2, we examined whether an inhibitory selection bias away from a feature could transfer across effectors. While priming of popout reflects both enhancement of the repeated target features and suppression of the repeated distractor features, the distractor previewing effect isolates a purely inhibitory component of target selection in which a previewed color is presented in a homogenous display and subsequently inhibited. Much like priming of popout, intertrial suppression biases in the distractor previewing effect transferred across effectors. Together, these results suggest that biases for target selection driven by recent trial history transfer across effectors. This indicates that representations in memory that bias attention towards or away from specific features are largely independent from their associated actions.

When being narrow minded is a good thing: locally biased people show stronger contextual cueing.

Science.gov (United States)

Bellaera, Lauren; von Mühlenen, Adrian; Watson, Derrick G

2014-01-01

Repeated contexts allow us to find relevant information more easily. Learning such contexts has been proposed to depend upon either global processing of the repeated contexts, or alternatively processing of the local region surrounding the target information. In this study, we measured the extent to which observers were by default biased to process towards a more global or local level. The findings showed that the ability to use context to help guide their search was strongly related to an observer's local/global processing bias. Locally biased people could use context to help improve their search better than globally biased people. The results suggest that the extent to which context can be used depends crucially on the observer's attentional bias and thus also to factors and influences that can change this bias.

Biases in Farm-Level Yield Risk Analysis due to Data Aggregation

NARCIS (Netherlands)

Finger, R.

2012-01-01

We investigate biases in farm-level yield risk analysis caused by data aggregation from the farm-level to regional and national levels using the example of Swiss wheat and barley yields. The estimated yield variability decreases significantly with increasing level of aggregation, with crop yield

Moisture Forecast Bias Correction in GEOS DAS

Science.gov (United States)

Dee, D.

1999-01-01

Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.

Language experience changes subsequent learning

Science.gov (United States)

Onnis, Luca; Thiessen, Erik

2013-01-01

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning. PMID:23200510

A father effect explains sex-ratio bias.

Science.gov (United States)

Malo, Aurelio F; Martinez-Pastor, Felipe; Garcia-Gonzalez, Francisco; Garde, Julián; Ballou, Jonathan D; Lacy, Robert C

2017-08-30

Sex ratio allocation has important fitness consequences, and theory predicts that parents should adjust offspring sex ratio in cases where the fitness returns of producing male and female offspring vary. The ability of fathers to bias offspring sex ratios has traditionally been dismissed given the expectation of an equal proportion of X- and Y-chromosome-bearing sperm (CBS) in ejaculates due to segregation of sex chromosomes at meiosis. This expectation has been recently refuted. Here we used Peromyscus leucopus to demonstrate that sex ratio is explained by an exclusive effect of the father, and suggest a likely mechanism by which male-driven sex-ratio bias is attained. We identified a male sperm morphological marker that is associated with the mechanism leading to sex ratio bias; differences among males in the sperm nucleus area (a proxy for the sex chromosome that the sperm contains) explain 22% variation in litter sex ratio. We further show the role played by the sperm nucleus area as a mediator in the relationship between individual genetic variation and sex-ratio bias. Fathers with high levels of genetic variation had ejaculates with a higher proportion of sperm with small nuclei area. This, in turn, led to siring a higher proportion of sons (25% increase in sons per 0.1 decrease in the inbreeding coefficient). Our results reveal a plausible mechanism underlying unexplored male-driven sex-ratio biases. We also discuss why this pattern of paternal bias can be adaptive. This research puts to rest the idea that father contribution to sex ratio variation should be disregarded in vertebrates, and will stimulate research on evolutionary constraints to sex ratios-for example, whether fathers and mothers have divergent, coinciding, or neutral sex allocation interests. Finally, these results offer a potential explanation for those intriguing cases in which there are sex ratio biases, such as in humans. © 2017 The Author(s).

Mechanisms of value-learning in the guidance of spatial attention.

Science.gov (United States)

Anderson, Brian A; Kim, Haena

2018-05-11

The role of associative reward learning in the guidance of feature-based attention is well established. The extent to which reward learning can modulate spatial attention has been much more controversial. At least one demonstration of a persistent spatial attention bias following space-based associative reward learning has been reported. At the same time, multiple other experiments have been published failing to demonstrate enduring attentional biases towards locations at which a target, if found, yields high reward. This is in spite of evidence that participants use reward structures to inform their decisions where to search, leading some to suggest that, unlike feature-based attention, spatial attention may be impervious to the influence of learning from reward structures. Here, we demonstrate a robust bias towards regions of a scene that participants were previously rewarded for selecting. This spatial bias relies on representations that are anchored to the configuration of objects within a scene. The observed bias appears to be driven specifically by reinforcement learning, and can be observed with equal strength following non-reward corrective feedback. The time course of the bias is consistent with a transient shift of attention, rather than a strategic search pattern, and is evident in eye movement patterns during free viewing. Taken together, our findings reconcile previously conflicting reports and offer an integrative account of how learning from feedback shapes the spatial attention system. Copyright © 2018 Elsevier B.V. All rights reserved.

Evolutionary thinking in microeconomic models: prestige bias and market bubbles.

Directory of Open Access Journals (Sweden)

Adrian Viliami Bell

Full Text Available Evolutionary models broadly support a number of social learning strategies likely important in economic behavior. Using a simple model of price dynamics, I show how prestige bias, or copying of famed (and likely successful individuals, influences price equilibria and investor disposition in a way that exacerbates or creates market bubbles. I discuss how integrating the social learning and demographic forces important in cultural evolution with economic models provides a fruitful line of inquiry into real-world behavior.

Ultrahigh Error Threshold for Surface Codes with Biased Noise

Science.gov (United States)

Tuckett, David K.; Bartlett, Stephen D.; Flammia, Steven T.

2018-02-01

We show that a simple modification of the surface code can exhibit an enormous gain in the error correction threshold for a noise model in which Pauli Z errors occur more frequently than X or Y errors. Such biased noise, where dephasing dominates, is ubiquitous in many quantum architectures. In the limit of pure dephasing noise we find a threshold of 43.7(1)% using a tensor network decoder proposed by Bravyi, Suchara, and Vargo. The threshold remains surprisingly large in the regime of realistic noise bias ratios, for example 28.2(2)% at a bias of 10. The performance is, in fact, at or near the hashing bound for all values of the bias. The modified surface code still uses only weight-4 stabilizers on a square lattice, but merely requires measuring products of Y instead of Z around the faces, as this doubles the number of useful syndrome bits associated with the dominant Z errors. Our results demonstrate that large efficiency gains can be found by appropriately tailoring codes and decoders to realistic noise models, even under the locality constraints of topological codes.

Social biases determine spatiotemporal sparseness of ciliate mating heuristics.

Science.gov (United States)

Clark, Kevin B

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present

Social biases determine spatiotemporal sparseness of ciliate mating heuristics

Science.gov (United States)

2012-01-01

Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The

Expanding Possibilities through Metaphor: Breaking Biases to Improve Crisis Management

Science.gov (United States)

Cirka, Carol C.; Corrigall, Elizabeth A.

2010-01-01

In this article, we demonstrate that an exercise using metaphors to overcome cognitive biases helped students to proactively imagine and prepare for an expanded set of potential crises. The exercise complements traditional textbook approaches to crisis management and incorporates creativity skill building in a realistic context. Learning outcomes…

Investigating learning strategies in a dispositional learning analytics context: the case of worked examples

NARCIS (Netherlands)

Tempelaar, Dirk; Rienties, Bart; Nguyen, Quan

2018-01-01

This study aims to contribute to recent developments in empirical studies on students’ learning strategies, whereby the use of trace data is combined with self-report data to distinguish profiles of learning strategy use [3–5]. We do so in the context of an application of dispositional learning

Computer-Mediated Counter-Arguments and Individual Learning

Science.gov (United States)

Hsu, Jack Shih-Chieh; Huang, Hsieh-Hong; Linden, Lars P.

2011-01-01

This study explores a de-bias function for a decision support systems (DSS) that is designed to help a user avoid confirmation bias by increasing the user's learning opportunities. Grounded upon the theory of mental models, the use of DSS is viewed as involving a learning process, whereby a user is directed to build mental models so as to reduce…

Learning from Failed Decisions

Science.gov (United States)

Nutt, Paul C.

2010-01-01

The consequences and dilemmas posed by learning issues for decision making are discussed. Learning requires both awareness of barriers and a coping strategy. The motives to hold back information essential for learning stem from perverse incentives, obscure outcomes, and the hindsight bias. There is little awareness of perverse incentives that…

Growth hormone biases amygdala network activation after fear learning

OpenAIRE

Gisabella, Barbara; Farah, Shadia; Peng, Xiaoyu; Burgos-Robles, Anthony Noel; Lim, Seh Hong; Goosens, Ki Ann

2016-01-01

Prolonged stress exposure is a risk factor for developing posttraumatic stress disorder, a disorder characterized by the ?over-encoding' of a traumatic experience. A potential mechanism by which this occurs is through upregulation of growth hormone (GH) in the amygdala. Here we test the hypotheses that GH promotes the over-encoding of fearful memories by increasing the number of neurons activated during memory encoding and biasing the allocation of neuronal activation, one aspect of the proce...

Individualized Learning Through Non-Linear use of Learning Objects: With Examples From Math and Stat

DEFF Research Database (Denmark)

Rootzén, Helle

2015-01-01

Our aim is to ensure individualized learning that is fun, inspiring and innovative. We believe that when you enjoy, your brain will open up and learning will be easier and more effective. The methods use a non-linear learning environment based on self-contained learning objects which are pieced t...

Biased emotional recognition in depression: perception of emotions in music by depressed patients.

Science.gov (United States)

Punkanen, Marko; Eerola, Tuomas; Erkkilä, Jaakko

2011-04-01

Depression is a highly prevalent mood disorder, that impairs a person's social skills and also their quality of life. Populations affected with depression also suffer from a higher mortality rate. Depression affects person's ability to recognize emotions. We designed a novel experiment to test the hypothesis that depressed patients show a judgment bias towards negative emotions. To investigate how depressed patients differ in their perception of emotions conveyed by musical examples, both healthy (n=30) and depressed (n=79) participants were presented with a set of 30 musical excerpts, representing one of five basic target emotions, and asked to rate each excerpt using five Likert scales that represented the amount of each one of those same emotions perceived in the example. Depressed patients showed moderate but consistent negative self-report biases both in the overall use of the scales and their particular application to certain target emotions, when compared to healthy controls. Also, the severity of the clinical state (depression, anxiety and alexithymia) had an effect on the self-report biases for both positive and negative emotion ratings, particularly depression and alexithymia. Only musical stimuli were used, and they were all clear examples of one of the basic emotions of happiness, sadness, fear, anger and tenderness. No neutral or ambiguous excerpts were included. Depressed patients' negative emotional bias was demonstrated using musical stimuli. This suggests that the evaluation of emotional qualities in music could become a means to discriminate between depressed and non-depressed subjects. The practical implications of the present study relate both to diagnostic uses of such perceptual evaluations, as well as a better understanding of the emotional regulation strategies of the patients. Copyright © 2010 Elsevier B.V. All rights reserved.

Automatic Earthquake Detection by Active Learning

Science.gov (United States)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Application of learning from examples methods for on-line dynamic security assessment of electric power systems - state of the art

Energy Technology Data Exchange (ETDEWEB)

Pecas Lopes, J.A. [Universidade do Porto, Porto (Portugal). Faculdade de Engenharia] Hatziargyriou, Nikos D. [National Technical University of Athens, Athens (Greece)

1994-12-31

This paper provides an overview of the application of `learning from examples` techniques like pattern recognition, artificial neural networks and decision trees, when used for fast dynamic security assessment. Problems concerning the system security evaluation relatively to transient stability and voltage stability are addressed with more details and references to research works in this field are briefly described. (author) 44 refs., 3 tabs.

Sympathetic bias.

Science.gov (United States)

Levy, David M; Peart, Sandra J

2008-06-01

We wish to deal with investigator bias in a statistical context. We sketch how a textbook solution to the problem of "outliers" which avoids one sort of investigator bias, creates the temptation for another sort. We write down a model of the approbation seeking statistician who is tempted by sympathy for client to violate the disciplinary standards. We give a simple account of one context in which we might expect investigator bias to flourish. Finally, we offer tentative suggestions to deal with the problem of investigator bias which follow from our account. As we have given a very sparse and stylized account of investigator bias, we ask what might be done to overcome this limitation.

Bias modification training can alter approach bias and chocolate consumption.

Science.gov (United States)

Schumacher, Sophie E; Kemps, Eva; Tiggemann, Marika

2016-01-01

Recent evidence has demonstrated that bias modification training has potential to reduce cognitive biases for attractive targets and affect health behaviours. The present study investigated whether cognitive bias modification training could be applied to reduce approach bias for chocolate and affect subsequent chocolate consumption. A sample of 120 women (18-27 years) were randomly assigned to an approach-chocolate condition or avoid-chocolate condition, in which they were trained to approach or avoid pictorial chocolate stimuli, respectively. Training had the predicted effect on approach bias, such that participants trained to approach chocolate demonstrated an increased approach bias to chocolate stimuli whereas participants trained to avoid such stimuli showed a reduced bias. Further, participants trained to avoid chocolate ate significantly less of a chocolate muffin in a subsequent taste test than participants trained to approach chocolate. Theoretically, results provide support for the dual process model's conceptualisation of consumption as being driven by implicit processes such as approach bias. In practice, approach bias modification may be a useful component of interventions designed to curb the consumption of unhealthy foods. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dissociation between active and observational learning from positive and negative feedback in Parkinsonism.

Science.gov (United States)

Kobza, Stefan; Ferrea, Stefano; Schnitzler, Alfons; Pollok, Bettina; Südmeyer, Martin; Bellebaum, Christian

2012-01-01

Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson's Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson's Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson's Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning.

Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

Directory of Open Access Journals (Sweden)

Christian Klaes

Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

Contingency bias in probability judgement may arise from ambiguity regarding additional causes.

Science.gov (United States)

Mitchell, Chris J; Griffiths, Oren; More, Pranjal; Lovibond, Peter F

2013-09-01

In laboratory contingency learning tasks, people usually give accurate estimates of the degree of contingency between a cue and an outcome. However, if they are asked to estimate the probability of the outcome in the presence of the cue, they tend to be biased by the probability of the outcome in the absence of the cue. This bias is often attributed to an automatic contingency detection mechanism, which is said to act via an excitatory associative link to activate the outcome representation at the time of testing. We conducted 3 experiments to test alternative accounts of contingency bias. Participants were exposed to the same outcome probability in the presence of the cue, but different outcome probabilities in the absence of the cue. Phrasing the test question in terms of frequency rather than probability and clarifying the test instructions reduced but did not eliminate contingency bias. However, removal of ambiguity regarding the presence of additional causes during the test phase did eliminate contingency bias. We conclude that contingency bias may be due to ambiguity in the test question, and therefore it does not require postulation of a separate associative link-based mechanism.

Bespoke Bias for Obtaining Free Energy Differences within Variationally Enhanced Sampling.

Science.gov (United States)

McCarty, James; Valsson, Omar; Parrinello, Michele

2016-05-10

Obtaining efficient sampling of multiple metastable states through molecular dynamics and hence determining free energy differences is central for understanding many important phenomena. Here we present a new biasing strategy, which employs the recent variationally enhanced sampling approach (Valsson and Parrinello Phys. Rev. Lett. 2014, 113, 090601). The bias is constructed from an intuitive model of the local free energy surface describing fluctuations around metastable minima and depends on only a few parameters which are determined variationally such that efficient sampling between states is obtained. The bias constructed in this manner largely reduces the need of finding a set of collective variables that completely spans the conformational space of interest, as they only need to be a locally valid descriptor of the system about its local minimum. We introduce the method and demonstrate its power on two representative examples.

Combination of biased forecasts: Bias correction or bias based weights?

OpenAIRE

Wenzel, Thomas

1999-01-01

Most of the literature on combination of forecasts deals with the assumption of unbiased individual forecasts. Here, we consider the case of biased forecasts and discuss two different combination techniques resulting in an unbiased forecast. On the one hand we correct the individual forecasts, and on the other we calculate bias based weights. A simulation study gives some insight in the situations where we should use the different methods.

A Variational Approach to Simultaneous Image Segmentation and Bias Correction.

Science.gov (United States)

Zhang, Kaihua; Liu, Qingshan; Song, Huihui; Li, Xuelong

2015-08-01

This paper presents a novel variational approach for simultaneous estimation of bias field and segmentation of images with intensity inhomogeneity. We model intensity of inhomogeneous objects to be Gaussian distributed with different means and variances, and then introduce a sliding window to map the original image intensity onto another domain, where the intensity distribution of each object is still Gaussian but can be better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying the bias field with a piecewise constant signal within the sliding window. A maximum likelihood energy functional is then defined on each local region, which combines the bias field, the membership function of the object region, and the constant approximating the true signal from its corresponding object. The energy functional is then extended to the whole image domain by the Bayesian learning approach. An efficient iterative algorithm is proposed for energy minimization, via which the image segmentation and bias field correction are simultaneously achieved. Furthermore, the smoothness of the obtained optimal bias field is ensured by the normalized convolutions without extra cost. Experiments on real images demonstrated the superiority of the proposed algorithm to other state-of-the-art representative methods.

Estimation bias and bias correction in reduced rank autoregressions

DEFF Research Database (Denmark)

Nielsen, Heino Bohn

2017-01-01

This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...

Biased Agonism of Endogenous Opioid Peptides at the μ-Opioid Receptor.

Science.gov (United States)

Thompson, Georgina L; Lane, J Robert; Coudrat, Thomas; Sexton, Patrick M; Christopoulos, Arthur; Canals, Meritxell

2015-08-01

Biased agonism is having a major impact on modern drug discovery, and describes the ability of distinct G protein-coupled receptor (GPCR) ligands to activate different cell signaling pathways, and to result in different physiologic outcomes. To date, most studies of biased agonism have focused on synthetic molecules targeting various GPCRs; however, many of these receptors have multiple endogenous ligands, suggesting that "natural" bias may be an unappreciated feature of these GPCRs. The μ-opioid receptor (MOP) is activated by numerous endogenous opioid peptides, remains an attractive therapeutic target for the treatment of pain, and exhibits biased agonism in response to synthetic opiates. The aim of this study was to rigorously assess the potential for biased agonism in the actions of endogenous opioids at the MOP in a common cellular background, and compare these to the effects of the agonist d-Ala2-N-MePhe4-Gly-ol enkephalin (DAMGO). We investigated activation of G proteins, inhibition of cAMP production, extracellular signal-regulated kinase 1 and 2 phosphorylation, β-arrestin 1/2 recruitment, and MOP trafficking, and applied a novel analytical method to quantify biased agonism. Although many endogenous opioids displayed signaling profiles similar to that of DAMGO, α-neoendorphin, Met-enkephalin-Arg-Phe, and the putatively endogenous peptide endomorphin-1 displayed particularly distinct bias profiles. These may represent examples of natural bias if it can be shown that they have different signaling properties and physiologic effects in vivo compared with other endogenous opioids. Understanding how endogenous opioids control physiologic processes through biased agonism can reveal vital information required to enable the design of biased opioids with improved pharmacological profiles and treat diseases involving dysfunction of the endogenous opioid system. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

The geological model calibration - Learnings from integration of reservoir geology and field performance - Example from the upper carboniferous reservoirs of the Southern North Sea

NARCIS (Netherlands)

Moscariello, A.; Hoof, T.B. van; Kunakbayeva, G.; Veen, J.H. ten; Belt, F. van den; Twerda, A.; Peters, L.; Davis, P.; Williams, H.

2013-01-01

The Geological Model Calibration - Learnings from Integration of Reservoir Geology and Field Performance: example from the Upper Carboniferous Reservoirs of the Southern North Sea. Copyright © (2012) by the European Association of Geoscientists & Engineers All rights reserved.

All in Its Proper Time: Monitoring the Emergence of a Memory Bias for Novel, Arousing-Negative Words in Individuals with High and Low Trait Anxiety

OpenAIRE

Eden, Annuschka Salima; Zwitserlood, Pienie; Keuper, Katharina; Junghöfer, Markus; Laeger, Inga; Zwanzger, Peter; Dobel, Christian

2014-01-01

The well-established memory bias for arousing-negative stimuli seems to be enhanced in high trait-anxious persons and persons suffering from anxiety disorders. We monitored the emergence and development of such a bias during and after learning, in high and low trait anxious participants. A word-learning paradigm was applied, consisting of spoken pseudowords paired either with arousing-negative or neutral pictures. Learning performance during training evidenced a short-lived advantage for arou...

Selection bias in the reported performances of AD classification pipelines

Directory of Open Access Journals (Sweden)

Alex F. Mendelson

2017-01-01

Full Text Available The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation.

Sampling of temporal networks: Methods and biases

Science.gov (United States)

Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter

2017-11-01

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.

Who Benefits from Mastery Learning?

Science.gov (United States)

Lai, Patrick; Biggs, John

1994-01-01

Data from 95 educationally disadvantaged Hong Kong students placed in mastery-learning classes were compared with 64 control students in expository-learning classes. Results indicate that under mastery learning, deep- and surface-biased learners increasingly diverge in performance and attitude, with surface learners doing better unit to unit, and…

Correction of bias in belt transect studies of immotile objects

Science.gov (United States)

Anderson, D.R.; Pospahala, R.S.

1970-01-01

Unless a correction is made, population estimates derived from a sample of belt transects will be biased if a fraction of, the individuals on the sample transects are not counted. An approach, useful for correcting this bias when sampling immotile populations using transects of a fixed width, is presented. The method assumes that a searcher's ability to find objects near the center of the transect is nearly perfect. The method utilizes a mathematical equation, estimated from the data, to represent the searcher's inability to find all objects at increasing distances from the center of the transect. An example of the analysis of data, formation of the equation, and application is presented using waterfowl nesting data collected in Colorado.

Self-serving bias effects on job analysis ratings.

Science.gov (United States)

Cucina, Jeffrey M; Martin, Nicholas R; Vasilopoulos, Nicholas L; Thibodeuax, Henry F

2012-01-01

The purpose of this study was to investigate whether worker-oriented job analysis importance ratings were influenced by subject matter experts' (SME) standing (as measured by self-rated performance) on a competency. This type of relationship (whereby SMEs indicate that the traits they have are important for successful job performance) is an example of the self-serving bias (which is widely described in the social cognition literature and rarely described in the industrial/organizational psychology literature). An archival dataset covering 57 clerical and technical occupations with 26,682 participants was used. Support was found for the relationship between self-rated performance and importance ratings. Significant relationships (typically in the .30s) were observed for all 31 competencies that were studied. Controls were taken to account for common method bias and differences in the competencies required for each of the 57 occupations. Past research has demonstrated the effects of the self-serving bias on personality-based job analysis ratings. This study was the first to extend these findings to traditional job analysis, which covers other competencies in addition to personality. In addition, this study is the first to use operational field data instead of laboratory data.

Conflict of interest and bias in publication.

Science.gov (United States)

Macklin, Ruth

2016-01-01

In his excellent article about commercial conflict of interest, Mark Wilson quotes Dennis Thompson, a political scientist who provided a searching analysis of the concept of conflict of interest (Col). Using Thompson's analysis, Wilson writes: "Determining whether factors such as ambition, the pursuit of fame and financial gain had biased a judgment was challenging. Motives are not always clear to either the conflicted party or to an outside observer." In this commentary, I aim to broaden the discussion beyond the narrowly commercial aspects of Col. I argue that bias can be introduced in major scientific journals by the editors' choices and policies. The context is a controversy that erupted in 2013 over the adequacy of informed consent in a clinical trial involving extremely premature infants. In this, as in Wilson's example, the players included the New England Journal of Medicine (NEJM), as well as the highest officials of the US National Institutes of Health (NIH).

Group rationale, collective sense: beyond intergroup bias.

Science.gov (United States)

Spears, Russell

2010-03-01

In this paper, I contest the view of the group as a source of bias and irrationality, especially prevalent within social psychology. I argue that this negative evaluation often arises by applying inappropriate standards, relating to the wrong level of analysis (often the individual level). Second, the image of the group as bad and biasing is often overstated. For example, the evidence suggests that intergroup discrimination, rather than being universal or generic, is often constrained, proportionate and reflects functional and rational strategies for managing threats and opportunities at the group level. Third, although the recent upsurge of interest in group emotions could be seen to reinforce the dualism between rationality and emotion, the contemporary functional approach argues for group emotions as augmenting rather than contradicting rationality. However, we should be wary (and weary) of narrow economic and individualist notions of rationality; group identity may offer the opportunity to redefine rationality in more collective and prosocial ways.

Auditory hindsight bias: Fluency misattribution versus memory reconstruction.

Science.gov (United States)

Higham, Philip A; Neil, Greg J; Bernstein, Daniel M

2017-06-01

We report 4 experiments investigating auditory hindsight bias-the tendency to overestimate the intelligibility of distorted auditory stimuli after learning their identity. An associative priming manipulation was used to vary the amount of processing fluency independently of prior target knowledge. For hypothetical designs, in which hindsight judgments are made for peers in foresight, we predicted that judgments would be based on processing fluency and that hindsight bias would be greater in the unrelated- compared to related-prime context (differential-fluency hypothesis). Conversely, for memory designs, in which foresight judgments are remembered in hindsight, we predicted that judgments would be based on memory reconstruction and that there would be independent effects of prime relatedness and prior target knowledge (recollection hypothesis). These predictions were confirmed. Specifically, we found support for the differential-fluency hypothesis when a hypothetical design was used in Experiments 1 and 2 (hypothetical group). Conversely, when a memory design was used in Experiments 2 (memory group), 3A, and 3B, we found support for the recollection hypothesis. Together, the results suggest that qualitatively different mechanisms create hindsight bias in the 2 designs. The results are discussed in terms of fluency misattributions, memory reconstruction, anchoring-and-adjustment, sense making, and a multicomponent model of hindsight bias. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Training hydrologists to be ecohydrologists: A 'how-you-can-do-it' example leveraging an active learning environment

Science.gov (United States)

Lyon, Steve W.; Walter, M. Todd; Jantze, Elin J.; Archibald, Josephine A.

2015-04-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a 'how-you-can-do-it' example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at Stockholm University's Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of 'activeness' across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more 'active' techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

Training hydrologists to be ecohydrologists: A ';how-you-can-do-it' example leveraging an active learning environment

Science.gov (United States)

Lyon, S. W.; Walter, M. T.; Jantze, E. J.; Archibald, J. A.

2013-12-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a ';how-you-can-do-it' example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at Stockholm University's Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of ';activeness' across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more ';active' techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

Stochastic gradient learning and instability: an example

Czech Academy of Sciences Publication Activity Database

Slobodyan, Sergey; Bogomolova, A.; Kolyuzhnov, Dmitri

2016-01-01

Roč. 20, č. 3 (2016), s. 777-790 ISSN 1365-1005 R&D Projects: GA ČR(CZ) GCP402/11/J018 Institutional support: PRVOUK-P23 Keywords : constant gain * adaptive learning * e-stability Subject RIV: AH - Economics Impact factor: 0.718, year: 2016

A Classroom Demonstration of Potential Biases in the Subjective Interpretation of Projective Tests.

Science.gov (United States)

Wiederman, Michael W.

1999-01-01

Suggests that instructors teaching psychological assessment can use a demonstration to illustrate potential biases when subjectively interpreting response to projective stimuli. Outlines the classroom procedure, notes styles of learning involved, and presents a summary of student evaluations. (DSK)

An architecture for an autonomous learning robot

Science.gov (United States)

Tillotson, Brian

1988-01-01

An autonomous learning device must solve the example bounding problem, i.e., it must divide the continuous universe into discrete examples from which to learn. We describe an architecture which incorporates an example bounder for learning. The architecture is implemented in the GPAL program. An example run with a real mobile robot shows that the program learns and uses new causal, qualitative, and quantitative relationships.

Cognitive Bias in the Verification and Validation of Space Flight Systems

Science.gov (United States)

Larson, Steve

2012-01-01

Cognitive bias is generally recognized as playing a significant role in virtually all domains of human decision making. Insight into this role is informally built into many of the system engineering practices employed in the aerospace industry. The review process, for example, typically has features that help to counteract the effect of bias. This paper presents a discussion of how commonly recognized biases may affect the verification and validation process. Verifying and validating a system is arguably more challenging than development, both technically and cognitively. Whereas there may be a relatively limited number of options available for the design of a particular aspect of a system, there is a virtually unlimited number of potential verification scenarios that may be explored. The probability of any particular scenario occurring in operations is typically very difficult to estimate, which increases reliance on judgment that may be affected by bias. Implementing a verification activity often presents technical challenges that, if they can be overcome at all, often result in a departure from actual flight conditions (e.g., 1-g testing, simulation, time compression, artificial fault injection) that may raise additional questions about the meaningfulness of the results, and create opportunities for the introduction of additional biases. In addition to mitigating the biases it can introduce directly, the verification and validation process must also overcome the cumulative effect of biases introduced during all previous stages of development. A variety of cognitive biases will be described, with research results for illustration. A handful of case studies will be presented that show how cognitive bias may have affected the verification and validation process on recent JPL flight projects, identify areas of strength and weakness, and identify potential changes or additions to commonly used techniques that could provide a more robust verification and validation of

The Amelia Bedelia effect: world knowledge and the goal bias in language acquisition.

Science.gov (United States)

Srinivasan, Mahesh; Barner, David

2013-09-01

How does world knowledge interact with syntax to constrain linguistic interpretation? We explored this question by testing children's acquisition of verbs like weed and water, which have opposite meanings despite occurring in the same syntactic frames. Whereas "weed the garden" treats "the garden" as a source, "water the garden" treats it as a goal. In five experiments, we asked how children learn these verbs. Previous theories predict that verbs which describe the transfer of an object with respect to its natural origin (e.g., "weed the garden") should receive source interpretations, whereas verbs that describe the transfer of an object with respect to something it is functionally related to (e.g., "water the garden") should receive goal interpretations. Therefore, acquiring world knowledge - about the natural origins and functional uses of objects - should be sufficient for differentiating between source and goal meanings. Experiments 1 and 2 casted doubt on this hypothesis, as 4- and 5-year-olds failed to use their world knowledge when interpreting these verbs and instead overextended goal interpretations. For example, children interpreted "weed the garden" to mean "put weeds onto a garden", even when they knew the natural origin of weeds. Experiment 3 tested children's interpretation of novel verbs and directly manipulated their access to relevant world knowledge. While younger children continued to exhibit a goal bias and failed to use world knowledge, older children generalized goal and source interpretations to novel verbs according to world knowledge. In Experiments 4 and 5, we confirmed that adults use world knowledge to guide their interpretation of novel verbs, but also showed that even adults prefer goal interpretations when they are made contextually plausible. We argue that children ultimately overcome a goal bias by learning to use their world knowledge to weigh the plausibility of events (e.g., of putting weeds into a garden). Copyright © 2013

A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting.

Science.gov (United States)

Ben Taieb, Souhaib; Atiya, Amir F

2016-01-01

Multistep-ahead forecasts can either be produced recursively by iterating a one-step-ahead time series model or directly by estimating a separate model for each forecast horizon. In addition, there are other strategies; some of them combine aspects of both aforementioned concepts. In this paper, we present a comprehensive investigation into the bias and variance behavior of multistep-ahead forecasting strategies. We provide a detailed review of the different multistep-ahead strategies. Subsequently, we perform a theoretical study that derives the bias and variance for a number of forecasting strategies. Finally, we conduct a Monte Carlo experimental study that compares and evaluates the bias and variance performance of the different strategies. From the theoretical and the simulation studies, we analyze the effect of different factors, such as the forecast horizon and the time series length, on the bias and variance components, and on the different multistep-ahead strategies. Several lessons are learned, and recommendations are given concerning the advantages, disadvantages, and best conditions of use of each strategy.

Individual differences in the shape bias in preschool children with specific language impairment and typical language development: theoretical and clinical implications.

Science.gov (United States)

Collisson, Beverly Anne; Grela, Bernard; Spaulding, Tammie; Rueckl, Jay G; Magnuson, James S

2015-05-01

We investigated whether preschool children with specific language impairment (SLI) exhibit the shape bias in word learning: the bias to generalize based on shape rather than size, color, or texture in an object naming context ('This is a wek; find another wek') but not in a non-naming similarity classification context ('See this? Which one goes with this one?'). Fifty-four preschool children (16 with SLI, 16 children with typical language [TL] in an equated control group, and 22 additional children with TL included in individual differences analyses but not group comparisons) completed a battery of linguistic and cognitive assessments and two experiments. In Experiment 1, children made generalization choices in object naming and similarity classification contexts on separate days, from options similar to a target object in shape, color, or texture. On average, TL children exhibited the shape bias in an object naming context, but children with SLI did not. In Experiment 2, we tested whether the failure to exhibit the shape bias might be linked to ability to detect systematicities in the visual domain. Experiment 2 supported this hypothesis, in that children with SLI failed to learn simple paired visual associations that were readily learned by children with TL. Analyses of individual differences in the two studies revealed that visual paired-associate learning predicted degree of shape bias in children with SLI and TL better than any other measure of nonverbal intelligence or standard assessments of language ability. We discuss theoretical and clinical implications. © 2014 John Wiley & Sons Ltd.

Does Tracing Worked Examples Enhance Geometry Learning?

Science.gov (United States)

Hu, Fang-Tzu; Ginns, Paul; Bobis, Janette

2014-01-01

Cognitive load theory seeks to generate novel instructional designs through a focus on human cognitive architecture including a limited working memory; however, the potential for enhancing learning through non-visual or non-auditory working memory channels is yet to be evaluated. This exploratory experiment tested whether explicit instructions to…

Age-differences in cognitive flexibility when overcoming a preexisting bias through feedback.

Science.gov (United States)

Wilson, Cristina G; Nusbaum, Amy T; Whitney, Paul; Hinson, John M

2018-08-01

Older adults are often worse than younger adults at adapting to changing situational demands, and this difference is commonly attributed to an age-related decline in acquiring and updating information. Previous research on aging and cognitive flexibility has used measures that require adapting to novel associations learned during a laboratory task (e.g., choice X led to positive outcomes but now leads to negative outcomes). However, in everyday life people must frequently overcome associations based on preexisting beliefs and biases (e.g., you like to eat cake, but your doctor said to limit your sugar intake). The goal of the present study was to examine possible age-differences in overcoming a preexisting bias and determine whether age-related changes in the acquisition and updating of information influence this form of flexibility. Older (n = 20) and younger (n = 20) adults completed a novel task in which repeated choices were made between a sure option (gain or loss) and one of two risky options that were initially ambiguous. Optimal performance required overcoming a framing bias toward being risk seeking to avoid a sure loss and risk averse when offered a sure gain. Probe questions assessed knowledge of choice outcomes, while skin conductance assessed physiological reactions to choices and choice outcomes. Both older and younger adults demonstrated flexibility by reducing the impact of bias over trials, but younger adults had better performance overall. Age-differences were associated with distinct aspects of processing. Young adults had more precise knowledge of choice outcomes and developed skin conductance responses in anticipation of bad choices that were not apparent in older adults. Older adults showed significant improvement over trials in their ability to decrease bias-driven choices, but younger showed greater flexibility. Age-differences in task performance were based on differences in learning and corresponding representations of task

Can Social Learning Increase Learning Speed, Performance or Both?

NARCIS (Netherlands)

Heinerman, J.V.; Stork, J.; Rebolledo Coy, M.A.; Hubert, J.G.; Eiben, A.E.; Bartz-Beielstein, Thomas; Haasdijk, Evert

2017-01-01

Social learning enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning

Deadly Attraction - Attentional Bias toward Preferred Cigarette Brand in Smokers.

Science.gov (United States)

Domaradzka, Ewa; Bielecki, Maksymilian

2017-01-01

Numerous studies have shown that biases in visual attention might be evoked by affective and personally relevant stimuli, for example addiction-related objects. Despite the fact that addiction is often linked to specific products and systematic purchase behaviors, no studies focused directly on the existence of bias evoked by brands. Smokers are characterized by high levels of brand loyalty and everyday contact with cigarette packaging. Using the incentive-salience mechanism as a theoretical framework, we hypothesized that this group might exhibit a bias toward the preferred cigarette brand. In our study, a group of smokers ( N = 40) performed a dot probe task while their eye movements were recorded. In every trial a pair of pictures was presented - each of them showed a single cigarette pack. The visual properties of stimuli were carefully controlled, so branding information was the key factor affecting subjects' reactions. For each participant, we compared gaze behavior related to the preferred vs. other brands. The analyses revealed no attentional bias in the early, orienting phase of the stimulus processing and strong differences in maintenance and disengagement. Participants spent more time looking at the preferred cigarettes and saccades starting at the preferred brand location had longer latencies. In sum, our data shows that attentional bias toward brands might be found in situations not involving choice or decision making. These results provide important insights into the mechanisms of formation and maintenance of attentional biases to stimuli of personal relevance and might serve as a first step toward developing new attitude measurement techniques.

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment.

Science.gov (United States)

Gong, Tao; Lam, Yau W; Shuai, Lan

2016-01-01

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages.

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment

Science.gov (United States)

Gong, Tao; Lam, Yau W.; Shuai, Lan

2016-01-01

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages. PMID:28066281

The central tendency bias in color perception: effects of internal and external noise.

Science.gov (United States)

Olkkonen, Maria; McCarthy, Patrice F; Allred, Sarah R

2014-09-05

Perceptual estimates can be biased by previously seen stimuli in delayed estimation tasks. These biases are often toward the mean of the whole stimulus set. Recently, we demonstrated such a central tendency bias in delayed color estimation. In the Bayesian framework of perceptual inference, perceptual biases arise when noisy sensory measurements are combined with prior information about the world. Here, we investigate this idea in color perception by manipulating stimulus range and stimulus noise while characterizing delayed color estimates. First, we manipulated the experimental prior for stimulus color by embedding stimuli in collections with different hue ranges. Stimulus range affected hue bias: Hue estimates were always biased toward the mean of the current set. Next, we studied the effect of internal and external noise on the amount of hue bias. Internal noise was manipulated by increasing the delay between the reference and test from 0.4 to 4 s. External noise was manipulated by increasing the amount of chromatic noise in the reference stimulus, while keeping the delay between the reference and test constant at 2 s. Both noise manipulations had a reliable effect on the strength of the central tendency bias. Furthermore, there was a tendency for a positive relationship between variability of the estimates and bias in both noise conditions. In conclusion, observers are able to learn an experimental hue prior, and the weight on the prior can be manipulated by introducing noise in the estimation process. © 2014 ARVO.

Operant Conditioning and Learning: Examples, Sources, Technology.

Science.gov (United States)

Pedrini, Bonnie C.; Pedrini, D. T.

The purpose of this paper is to relate psychology to teaching generally, and to relate behavior shaping to curriculum, specifically. Focusing on operant conditioning and learning, many studies are cited which illustrate some of the work being done toward effectively shaping or modifying student behavior whether in terms of subject matter or…

E-learning for health professionals.

Science.gov (United States)

Vaona, Alberto; Banzi, Rita; Kwag, Koren H; Rigon, Giulio; Cereda, Danilo; Pecoraro, Valentina; Tramacere, Irene; Moja, Lorenzo

2018-01-21

The use of e-learning, defined as any educational intervention mediated electronically via the Internet, has steadily increased among health professionals worldwide. Several studies have attempted to measure the effects of e-learning in medical practice, which has often been associated with large positive effects when compared to no intervention and with small positive effects when compared with traditional learning (without access to e-learning). However, results are not conclusive. To assess the effects of e-learning programmes versus traditional learning in licensed health professionals for improving patient outcomes or health professionals' behaviours, skills and knowledge. We searched CENTRAL, MEDLINE, Embase, five other databases and three trial registers up to July 2016, without any restrictions based on language or status of publication. We examined the reference lists of the included studies and other relevant reviews. If necessary, we contacted the study authors to collect additional information on studies. Randomised trials assessing the effectiveness of e-learning versus traditional learning for health professionals. We excluded non-randomised trials and trials involving undergraduate health professionals. Two authors independently selected studies, extracted data and assessed risk of bias. We graded the certainty of evidence for each outcome using the GRADE approach and standardised the outcome effects using relative risks (risk ratio (RR) or odds ratio (OR)) or standardised mean difference (SMD) when possible. We included 16 randomised trials involving 5679 licensed health professionals (4759 mixed health professionals, 587 nurses, 300 doctors and 33 childcare health consultants).When compared with traditional learning at 12-month follow-up, low-certainty evidence suggests that e-learning may make little or no difference for the following patient outcomes: the proportion of patients with low-density lipoprotein (LDL) cholesterol of less than 100 mg

R statistical application development by example : beginner's guide

CERN Document Server

Tattar, Narayanachart Prabhanjan

2013-01-01

Full of screenshots and examples, this Beginner's Guide by Example will teach you practically everything you need to know about R statistical application development from scratch. You will begin learning the first concepts of statistics in R which is vital in this fast paced era and it is also a bargain as you do not need to do a preliminary course on the subject.

Looking on the bright side: biased attention and the human serotonin transporter gene.

Science.gov (United States)

Fox, Elaine; Ridgewell, Anna; Ashwin, Chris

2009-05-22

Humans differ in terms of biased attention for emotional stimuli and these biases can confer differential resilience and vulnerability to emotional disorders. Selective processing of positive emotional information, for example, is associated with enhanced sociability and well-being while a bias for negative material is associated with neuroticism and anxiety. A tendency to selectively avoid negative material might also be associated with mental health and well-being. The neurobiological mechanisms underlying these cognitive phenotypes are currently unknown. Here we show for the first time that allelic variation in the promotor region of the serotonin transporter gene (5-HTTLPR) is associated with differential biases for positive and negative affective pictures. Individuals homozygous for the long allele (LL) showed a marked bias to selectively process positive affective material alongside selective avoidance of negative affective material. This potentially protective pattern was absent among individuals carrying the short allele (S or SL). Thus, allelic variation on a common genetic polymorphism was associated with the tendency to selectively process positive or negative information. The current study is important in demonstrating a genotype-related alteration in a well-established processing bias, which is a known risk factor in determining both resilience and vulnerability to emotional disorders.

Network Structure and Biased Variance Estimation in Respondent Driven Sampling.

Science.gov (United States)

Verdery, Ashton M; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J

2015-01-01

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.

Length bias correction in gene ontology enrichment analysis using logistic regression.

Science.gov (United States)

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

[Acceptance of case-based, interactive e-learning in veterinary medicine on the example of the CASUS system].

Science.gov (United States)

Börchers, M; Tipold, A; Pfarrer, Ch; Fischer, M R; Ehlers, J P

2010-01-01

New teaching methods such as e-learning, are increasingly used to support common methods such as lectures, seminars and practical training in universities providing education in veterinary medicine. In the current study, the acceptance of e-learning in the example of the CASUS system by veterinarians as well as students of veterinary medicine of all German-speaking universities was analyzed. Material und methods: For this purpose an online evaluation questionnaire was developed. Members of the target groups were informed by e-mail and references in professional journals, as well as through veterinarian exchange platforms on the internet. Additionally, 224 students' final anatomy marks were compared and correlated to the utilization of CASUS to gain an important insight for the development of new teaching practices in the teaching of veterinary medicine. In total 1581 questionnaires were evaluated. A good acceptance regarding new teaching practices was found, although the classical textbook is still the most important instrument for imparting knowledge. The degree of utilization of e-learning strongly depends on its integration into the teaching content. CASUS is regarded as an efficient teaching method, with over 90% of the respondents indicating a strong desire to expand the number of case studies. Due to the present low degree of integration into the teaching content, no significant correlation could be found between the utilization of anatomy case studies and the final anatomy mark. However, based on their subjective perception, the students reported a high level of success in their study results with the likely effect of supporting increasing self-assurance in the situation of examinations. With the help of e-learning, educational objectives can be achieved that are not attainable by traditional teaching methods, e.g. the review of individual improvements by using the integrated feedback-function of e-learning programs. However, e-learning is not able to

Web-based experiments controlled by JavaScript: an example from probability learning.

Science.gov (United States)

Birnbaum, Michael H; Wakcher, Sandra V

2002-05-01

JavaScript programs can be used to control Web experiments. This technique is illustrated by an experiment that tested the effects of advice on performance in the classic probability-learning paradigm. Previous research reported that people tested via the Web or in the lab tended to match the probabilities of their responses to the probabilities that those responses would be reinforced. The optimal strategy, however, is to consistently choose the more frequent event; probability matching produces suboptimal performance. We investigated manipulations we reasoned should improve performance. A horse race scenario in which participants predicted the winner in each of a series of races between two horses was compared with an abstract scenario used previously. Ten groups of learners received different amounts of advice, including all combinations of (1) explicit instructions concerning the optimal strategy, (2) explicit instructions concerning a monetary sum to maximize, and (3) accurate information concerning the probabilities of events. The results showed minimal effects of horse race versus abstract scenario. Both advice concerning the optimal strategy and probability information contributed significantly to performance in the task. This paper includes a brief tutorial on JavaScript, explaining with simple examples how to assemble a browser-based experiment.

The bias against new innovations in health care: value uncertainty and willingness to pay.

Science.gov (United States)

Walton, Surrey M; Graves, Philip E; Mueser, Peter R; Dow, Jay K

2002-01-01

This paper offers a model for the bias found in willingness-to-pay valuations against new treatments. For example, this bias provides an explanation for patient preferences that make it difficult for formularies to take treatments off their lists, even when newer treatments would appear to be clearly preferable. The appeal of the model, which is based on imperfect information, is that it is consistent with rational preferences and rational behavior by patients, which are necessary for standard models and methods related to decision theory, cost-effectiveness, and efficiency.

Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

Science.gov (United States)

Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

2017-06-01

Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.

Engaging Gatekeepers, Optimizing Decision Making, and Mitigating Bias: Design Specifications for Systemic Diversity Interventions.

Science.gov (United States)

Vinkenburg, Claartje J

2017-06-01

In this contribution to the Journal of Applied Behavioral Science Special Issue on Understanding Diversity Dynamics in Systems: Social Equality as an Organization Change Issue, I develop and describe design specifications for systemic diversity interventions in upward mobility career systems, aimed at optimizing decision making through mitigating bias by engaging gatekeepers. These interventions address the paradox of meritocracy that underlies the surprising lack of diversity at the top of the career pyramid in these systems. I ground the design specifications in the limited empirical evidence on "what works" in systemic interventions. Specifically, I describe examples from interventions in academic settings, including a bias literacy program, participatory modeling, and participant observation. The design specifications, paired with inspirational examples of successful interventions, should assist diversity officers and consultants in designing and implementing interventions to promote the advancement to and representation of nondominant group members at the top of the organizational hierarchy.

Attention and memory benefits for physical attractiveness may mediate prosocial biases.

Science.gov (United States)

Becker, David Vaughn

2017-01-01

Mating motivations can explain attractiveness benefits, but what proximate mechanisms might serve as efficient causes of these biases? There is growing evidence that visual cues of physical attractiveness capture attention and facilitate memory, enhancing salience in ways that could underlie, for example, preferring one job applicant over another. All of these effects beg deeper questions about the meaning of attractiveness.

Reward associations magnify memory-based biases on perception.

Science.gov (United States)

Doallo, Sonia; Patai, Eva Zita; Nobre, Anna Christina

2013-02-01

Long-term spatial contextual memories are a rich source of predictions about the likely locations of relevant objects in the environment and should enable tuning of neural processing of unfolding events to optimize perception and action. Of particular importance is whether and how the reward outcome of past events can impact perception. We combined behavioral measures with recordings of brain activity with high temporal resolution to test whether the previous reward outcome associated with a memory could modulate the impact of memory-based biases on perception, and if so, the level(s) at which visual neural processing is biased by reward-associated memory-guided attention. Data showed that past rewards potentiate the effects of spatial memories upon the discrimination of target objects embedded within complex scenes starting from early perceptual stages. We show that a single reward outcome of learning impacts on how we perceive events in our complex environments.

Is Bible Translation "Imperialist"? Challenging Another Anti-Christian Bias in the Academy

Science.gov (United States)

Adrian, William

2007-01-01

A strong anti-Christian bias exists in the modern American university. It has been documented by George Marsden in his 1994 book, "The Soul of the American University," and by a growing number of other scholars. The modern university response to the history of Bible translation movements provides another example of the anti-Christian…

Journal bias or author bias?

Science.gov (United States)

Harris, Ian

2016-01-01

I read with interest the comment by Mark Wilson in the Indian Journal of Medical Ethics regarding bias and conflicts of interest in medical journals. Wilson targets one journal (the New England Journal of Medicine: NEJM) and one particular "scandal" to make his point that journals' decisions on publication are biased by commercial conflicts of interest (CoIs). It is interesting that he chooses the NEJM which, by his own admission, had one of the strictest CoI policies and had published widely on this topic. The feeling is that if the NEJM can be guilty, they can all be guilty.

Biased low differential input impedance current receiver/converter device and method for low noise readout from voltage-controlled detectors

Science.gov (United States)

Degtiarenko, Pavel V [Williamsburg, VA; Popov, Vladimir E [Newport News, VA

2011-03-22

A first stage electronic system for receiving charge or current from voltage-controlled sensors or detectors that includes a low input impedance current receiver/converter device (for example, a transimpedance amplifier), which is directly coupled to the sensor output, a source of bias voltage, and the device's power supply (or supplies), which use the biased voltage point as a baseline.

Machine Learning Techniques in Clinical Vision Sciences.

Science.gov (United States)

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

Motivational mechanisms and outcome expectancies underlying the approach bias towards addictive substances

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Poppy eWatson

2012-10-01

Full Text Available Human behavior can be paradoxical, in that actions can be initiated that are seemingly incongruent with an individual’s explicit desires. This is most commonly observed in drug addiction, where maladaptive behavior (i.e. drug seeking appears to be compulsive, continuing at great personal cost. Approach biases towards addictive substances have been correlated with actual drug-use in a number of studies, suggesting that this measure can, in some cases, index everyday maladaptive tendencies. At present it is unclear whether this bias to drug cues is a Pavlovian conditioned approach response, a habitual response, the result of a Pavlovian-instrumental transfer process or a goal-directed action in the sense that expectancy of the rewarding effects of drugs controls approach. We consider this question by combining the theoretical framework of associative learning with the available evidence from approach bias research. Although research investigating the relative contributions of these mechanisms to the approach bias is to date relatively limited, we review existing studies and also outline avenues for future research.

Motivational Mechanisms and Outcome Expectancies Underlying the Approach Bias toward Addictive Substances.

Science.gov (United States)

Watson, P; de Wit, S; Hommel, Bernhard; Wiers, R W

2012-01-01

Human behavior can be paradoxical, in that actions can be initiated that are seemingly incongruent with an individual's explicit desires. This is most commonly observed in drug addiction, where maladaptive behavior (i.e., drug seeking) appears to be compulsive, continuing at great personal cost. Approach biases toward addictive substances have been correlated with actual drug-use in a number of studies, suggesting that this measure can, in some cases, index everyday maladaptive tendencies. At present it is unclear whether this bias to drug cues is a Pavlovian conditioned approach response, a habitual response, the result of a Pavlovian-instrumental transfer process, or a goal-directed action in the sense that expectancy of the rewarding effects of drugs controls approach. We consider this question by combining the theoretical framework of associative learning with the available evidence from approach bias research. Although research investigating the relative contributions of these mechanisms to the approach bias is to date relatively limited, we review existing studies and also outline avenues for future research.

How Dispositional Learning Analytics helps understanding the worked-example principle

NARCIS (Netherlands)

Tempelaar, Dirk; Sampson, Demetrios G.; Spector, J. Michael; Ifenthaler, Dirk; Isaías, Pedro

2017-01-01

This empirical study aims to demonstrate how Dispositional Learning Analytics can contribute in the investigation of the effectiveness of didactical scenarios in authentic settings, where previous research has mostly been laboratory based. Using a showcase based on learning processes of 1080

Corpus-based Transitivity Biases in Individuals with Aphasia

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Gayle DeDe

2015-04-01

Full Text Available Introduction Spontaneous speech samples in individuals with aphasia (IWA have been analyzed to examine many different psycholinguistic features. The present study focused on how IWA use verbs in spontaneous speech. Some verbs can occur in more than one argument structure, but are biased to occur more frequently in one frame than another. For example, "watch" appears in transitive and intransitive structures, but is usually used transitively. This is known as a transitivity bias. It is unknown whether IWA show the same transitivity biases in production as those reported in previous corpus studies with unimpaired individuals. Studies of sentence comprehension have shown that IWA are sensitive to verb biases (e.g., DeDe, 2013. In addition, IWA have shown an overall preference for transitive structures, which are the most frequent structures in English (Roland, Dick, & Elman, 2007. The present study investigated whether IWA show the same pattern of transitive and intransitive biases in spontaneous speech as unimpaired individuals. Method Participants: 278 interviews with IWA were taken from AphasiaBank. The IWA represented a range of aphasia types. Participants were omitted if they spoke English as a second language. Materials: 54 verbs were coded. We chose verbs with the goal of representing different bias types (e.g., transitive, intransitive, sentential complement. Of these, data from 11 transitively biased and 11 intransitively biased verbs (matched for frequency of use and number of syllables are presented here. Coding: All productions of the 54 verbs were coded. The coding protocol was based on Gahl, Jurafsky, and Roland (2004. We implemented an additional level of coding to indicate erroneous verb productions, such as ungrammatical structures and verb agreement errors. Results The (intransitivity biases for IWA were compared to biases from a previously published corpus study (Gahl et al., 2004. The IWA used transitively biased verbs in

Deadly Attraction – Attentional Bias toward Preferred Cigarette Brand in Smokers

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Ewa Domaradzka

2017-08-01

Full Text Available Numerous studies have shown that biases in visual attention might be evoked by affective and personally relevant stimuli, for example addiction-related objects. Despite the fact that addiction is often linked to specific products and systematic purchase behaviors, no studies focused directly on the existence of bias evoked by brands. Smokers are characterized by high levels of brand loyalty and everyday contact with cigarette packaging. Using the incentive-salience mechanism as a theoretical framework, we hypothesized that this group might exhibit a bias toward the preferred cigarette brand. In our study, a group of smokers (N = 40 performed a dot probe task while their eye movements were recorded. In every trial a pair of pictures was presented – each of them showed a single cigarette pack. The visual properties of stimuli were carefully controlled, so branding information was the key factor affecting subjects’ reactions. For each participant, we compared gaze behavior related to the preferred vs. other brands. The analyses revealed no attentional bias in the early, orienting phase of the stimulus processing and strong differences in maintenance and disengagement. Participants spent more time looking at the preferred cigarettes and saccades starting at the preferred brand location had longer latencies. In sum, our data shows that attentional bias toward brands might be found in situations not involving choice or decision making. These results provide important insights into the mechanisms of formation and maintenance of attentional biases to stimuli of personal relevance and might serve as a first step toward developing new attitude measurement techniques.

Deadly Attraction – Attentional Bias toward Preferred Cigarette Brand in Smokers

Science.gov (United States)

Domaradzka, Ewa; Bielecki, Maksymilian

2017-01-01

Numerous studies have shown that biases in visual attention might be evoked by affective and personally relevant stimuli, for example addiction-related objects. Despite the fact that addiction is often linked to specific products and systematic purchase behaviors, no studies focused directly on the existence of bias evoked by brands. Smokers are characterized by high levels of brand loyalty and everyday contact with cigarette packaging. Using the incentive-salience mechanism as a theoretical framework, we hypothesized that this group might exhibit a bias toward the preferred cigarette brand. In our study, a group of smokers (N = 40) performed a dot probe task while their eye movements were recorded. In every trial a pair of pictures was presented – each of them showed a single cigarette pack. The visual properties of stimuli were carefully controlled, so branding information was the key factor affecting subjects’ reactions. For each participant, we compared gaze behavior related to the preferred vs. other brands. The analyses revealed no attentional bias in the early, orienting phase of the stimulus processing and strong differences in maintenance and disengagement. Participants spent more time looking at the preferred cigarettes and saccades starting at the preferred brand location had longer latencies. In sum, our data shows that attentional bias toward brands might be found in situations not involving choice or decision making. These results provide important insights into the mechanisms of formation and maintenance of attentional biases to stimuli of personal relevance and might serve as a first step toward developing new attitude measurement techniques. PMID:28848479

Collaborative Learning in Practice: Examples from Natural Resource ...

International Development Research Centre (IDRC) Digital Library (Canada)

2010-12-01

Dec 1, 2010 ... Case studies show how, through joint efforts with researchers and other actors, local ... address and learn from challenges in managing natural resources. ... health, and health systems research relevant to the emerging crisis.

Can threat information bias fear learning? : Some tentative results and methodological considerations

NARCIS (Netherlands)

Mertens, G.; De Houwer, J.

2017-01-01

Whereas it is widely recognized that both verbal threat information and stimulus pairings can install strong and persistent fear, few studies have addressed the interaction between these two pathways of fear. According to the expectancy bias theory of Davey (1992, 1997), verbal information can

Seamless Learning Environments in Higher Education with Mobile Devices and Examples

Science.gov (United States)

Marín, Victoria I.; Jääskelä, Päivikki; Häkkinen, Päivi; Juntunen, Merja; Rasku-Puttonen, Helena; Vesisenaho, Mikko

2016-01-01

The use of seamless learning environments that have the potential to support lifelong learning anytime and anywhere has become a reality. In this sense, many educational institutions have started to consider introducing seamless learning environments into their programs. The aim of this study is to analyze how various educational university…

The effect of metacognitive self on confirmation bias revealed in relation to community and competence

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Brycz Hanna

2014-09-01

Full Text Available The main goal of our study was to investigate the role of insight into one’s own biases (metacognitive self in the process of hypothesis validation in accordance to the two fundamental social perception domains (community and competence on the example of confirmation bias. The study was conducted on a group of 593 participants with the use of a confirmation bias procedure, a free recall procedure and the Metacognitive Self scale. We manipulated with the domain and the value of information given to the respondents. We suspected that individuals with a high metacognitive self, in opposition to low metacognitive self ones, would not process the given information according to the two fundamental social perception domains. The results verified the existence of an interaction effect of the metacognitive self (MCS and the domain of the information given about a perceived person on the susceptibility to follow the confirmation bias. Contrary to the low metacognitive self individuals, who show a higher tendency for the confirmation bias within the competence than the community domain, persons with a high insight into their own biases express the same level of confirmation bias in no respect to the domain of the information. The value of the information has no significant influence.

Learning Object Repositories

Science.gov (United States)

Lehman, Rosemary

2007-01-01

This chapter looks at the development and nature of learning objects, meta-tagging standards and taxonomies, learning object repositories, learning object repository characteristics, and types of learning object repositories, with type examples. (Contains 1 table.)

Teaching with Games: Online Resources and Examples for Entry Level Courses

Science.gov (United States)

Teed, R.; Manduca, C.

2004-12-01

Using games to teach introductory geoscience can motivate students to enthusiastically learn material that they might otherwise condemn as "boring". A good educational game is one that immerses the players in the material and engages them for as long as it takes to master that material. There are some good geoscience games already available, but instructors can also create their own, suitable to their students and the content that they are teaching. Game-Based Learning is a module on the Starting Point website for faculty teaching entry level geosciences. It assists faculty in using games in their teaching by providing a description of the features of game-based learning, why you would use it, how to use games to teach geoscience, examples, and references. Other issues discussed include the development of video games for teaching, having your students create educational games, what makes a good game, handling competition in the classroom, and grading. The examples include descriptions of and rules for a GPS treasure hunt, a geology quiz show, and an earthquake game, as well as links to several online geological video games, and advice on how to design a paleontology board game. Starting Point is intended to help both experienced faculty and new instructors meet the challenge of teaching introductory geoscience classes, including environmental science and oceanography as well as more traditional geology classes. For many students, these classes are both the first and the last college-level science class that they will ever take. They need to learn enough about the Earth in that one class to sustain them for many decades as voters, consumers, and sometimes even as teachers. Starting Point is produced by a group of authors working with the Science Education Resource Center. It contains dozens of detailed examples categorized by geoscience topic with advice about using them and assessing learning. Each example is linked to one of many modules, such as Game

Applications of machine learning in cancer prediction and prognosis.

Science.gov (United States)

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Eclipse plugin development by example beginner's guide

CERN Document Server

Blewitt, Alex

2013-01-01

A Beginner's Guide following the ""by Example"" approach. There will be 5-8 major examples that will be used in the book to develop advanced plugins with the Eclipse IDE.This book is for Java developers who are familiar with Eclipse as a Java IDE and are interested in learning how to develop plug-ins for Eclipse. No prior knowledge of Eclipse plug-in development or OSGi is necessary, although you are expected to know how to create, run, and debug Java programs in Eclipse.

Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

Science.gov (United States)

Aoki, Kenichi; Feldman, Marcus W

2014-02-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

Evolution of learning strategies in temporally and spatially variable environments: A review of theory

Science.gov (United States)

Aoki, Kenichi; Feldman, Marcus W.

2013-01-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

Perceptual Biases in Relation to Paranormal and Conspiracy Beliefs.

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van Elk, Michiel

2015-01-01

Previous studies have shown that one's prior beliefs have a strong effect on perceptual decision-making and attentional processing. The present study extends these findings by investigating how individual differences in paranormal and conspiracy beliefs are related to perceptual and attentional biases. Two field studies were conducted in which visitors of a paranormal conducted a perceptual decision making task (i.e. the face/house categorization task; Experiment 1) or a visual attention task (i.e. the global/local processing task; Experiment 2). In the first experiment it was found that skeptics compared to believers more often incorrectly categorized ambiguous face stimuli as representing a house, indicating that disbelief rather than belief in the paranormal is driving the bias observed for the categorization of ambiguous stimuli. In the second experiment, it was found that skeptics showed a classical 'global-to-local' interference effect, whereas believers in conspiracy theories were characterized by a stronger 'local-to-global interference effect'. The present study shows that individual differences in paranormal and conspiracy beliefs are associated with perceptual and attentional biases, thereby extending the growing body of work in this field indicating effects of cultural learning on basic perceptual processes.

Perceptual Biases in Relation to Paranormal and Conspiracy Beliefs.

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Michiel van Elk

Full Text Available Previous studies have shown that one's prior beliefs have a strong effect on perceptual decision-making and attentional processing. The present study extends these findings by investigating how individual differences in paranormal and conspiracy beliefs are related to perceptual and attentional biases. Two field studies were conducted in which visitors of a paranormal conducted a perceptual decision making task (i.e. the face/house categorization task; Experiment 1 or a visual attention task (i.e. the global/local processing task; Experiment 2. In the first experiment it was found that skeptics compared to believers more often incorrectly categorized ambiguous face stimuli as representing a house, indicating that disbelief rather than belief in the paranormal is driving the bias observed for the categorization of ambiguous stimuli. In the second experiment, it was found that skeptics showed a classical 'global-to-local' interference effect, whereas believers in conspiracy theories were characterized by a stronger 'local-to-global interference effect'. The present study shows that individual differences in paranormal and conspiracy beliefs are associated with perceptual and attentional biases, thereby extending the growing body of work in this field indicating effects of cultural learning on basic perceptual processes.

Leftward spatial bias in children's drawing placement: hemispheric activation versus directional hypotheses.

Science.gov (United States)

Picard, Delphine; Zarhbouch, Benaissa

2014-01-01

A leftward spatial bias in drawing placement was demonstrated by Heller (1991) using the draw-a-person test with right-handed American children. No such bias was observed in left-handed children who are assumed to be less lateralised than their right-handed peers. According to Heller the leftward spatial bias is primarily a reflection of the right hemisphere specialisation for spatial processing. However, an alternative explanation in terms of directional trends may be put forward. In the present study we first confirm Heller's findings of a handedness effect on drawing placement using the draw-a-tree task with a large sample of right- and left-handed French children aged 5-15 years (Exp. 1). We then provide evidence that a similar leftward bias occurs in right-handed Moroccan children aged 7-11 years with opposite script directionality and opposite preferred drawing movement directions (i.e., right-to-left directional trends) to the those of right-handed French children (Exp. 2). Taken together these findings suggest that directionality trends arising from learned cultural habits and motor preferences play little role in determining spatial bias in the centring of a single object drawn on a page. Rather there may be a cerebral origin for drawing single objects slightly on the left side of the graphic space.

Social learning in cooperative dilemmas.

Science.gov (United States)

Lamba, Shakti

2014-07-22

Helping is a cornerstone of social organization and commonplace in human societies. A major challenge for the evolutionary sciences is to explain how cooperation is maintained in large populations with high levels of migration, conditions under which cooperators can be exploited by selfish individuals. Cultural group selection models posit that such large-scale cooperation evolves via selection acting on populations among which behavioural variation is maintained by the cultural transmission of cooperative norms. These models assume that individuals acquire cooperative strategies via social learning. This assumption remains empirically untested. Here, I test this by investigating whether individuals employ conformist or payoff-biased learning in public goods games conducted in 14 villages of a forager-horticulturist society, the Pahari Korwa of India. Individuals did not show a clear tendency to conform or to be payoff-biased and are highly variable in their use of social learning. This variation is partly explained by both individual and village characteristics. The tendency to conform decreases and to be payoff-biased increases as the value of the modal contribution increases. These findings suggest that the use of social learning in cooperative dilemmas is contingent on individuals' circumstances and environments, and question the existence of stably transmitted cultural norms of cooperation. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Importance biasing scheme implemented in the PRIZMA code

International Nuclear Information System (INIS)

Kandiev, I.Z.; Malyshkin, G.N.

1997-01-01

PRIZMA code is intended for Monte Carlo calculations of linear radiation transport problems. The code has wide capabilities to describe geometry, sources, material composition, and to obtain parameters specified by user. There is a capability to calculate path of particle cascade (including neutrons, photons, electrons, positrons and heavy charged particles) taking into account possible transmutations. Importance biasing scheme was implemented to solve the problems which require calculation of functionals related to small probabilities (for example, problems of protection against radiation, problems of detection, etc.). The scheme enables to adapt trajectory building algorithm to problem peculiarities

Towards quantum signatures in a swept-bias Josephson junction

Energy Technology Data Exchange (ETDEWEB)

Losert, Harald; Vogel, Karl; Schleich, Wolfgang P. [Institut fuer Quantenphysik and Center for Integrated Quantum Science and Technology (IQST), Universitaet Ulm, D-89069 Ulm (Germany)

2016-07-01

Josephson junctions are one of the best examples for the observation of macroscopic quantum tunneling. The phase difference in a current-biased Josephson junction behaves like the position of a particle in a tilted washboard potential. The escape of this phase-particle corresponds to the voltage switching of the associated junction. Quantum mechanically, the escape from the washboard potential can be explained as tunneling from the ground state, or an excited state. However, it has been shown, that in the case of periodic driving the experimental data for quantum mechanical key features, e.g. Rabi oscillations or energy level quantization, can be reproduced by a completely classical description. Motivated by this discussion, we investigate a swept-bias Josephson junction in the case of a large critical current. In particular, we contrast the switching current distributions resulting from a quantum mechanical and classical description of the time evolution.

Query-Biased Preview over Outsourced and Encrypted Data

Directory of Open Access Journals (Sweden)

Ningduo Peng

2013-01-01

document to check if it contains the desired content. An informative query-biased preview feature, as applied in modern search engine, could help the users to learn about the content without downloading the entire document. However, when the data are encrypted, securely extracting a keyword-in-context snippet from the data as a preview becomes a challenge. Based on private information retrieval protocol and the core concept of searchable encryption, we propose a single-server and two-round solution to securely obtain a query-biased snippet over the encrypted data from the server. We achieve this novel result by making a document (plaintext previewable under any cryptosystem and constructing a secure index to support dynamic computation for a best matched snippet when queried by some keywords. For each document, the scheme has O(d storage complexity and O(log(d/s+s+d/s communication complexity, where d is the document size and s is the snippet length.

CPI Bias in Korea

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Chul Chung

2007-12-01

Full Text Available We estimate the CPI bias in Korea by employing the approach of Engel’s Law as suggested by Hamilton (2001. This paper is the first attempt to estimate the bias using Korean panel data, Korean Labor and Income Panel Study(KLIPS. Following Hamilton’s model with non­linear specification correction, our estimation result shows that the cumulative CPI bias over the sample period (2000-2005 was 0.7 percent annually. This CPI bias implies that about 21 percent of the inflation rate during the period can be attributed to the bias. In light of purchasing power parity, we provide an interpretation of the estimated bias.

Associations among Negative Parenting, Attention Bias to Anger, and Social Anxiety among Youth

Science.gov (United States)

Gulley, Lauren D.; Oppenheimer, Caroline W.; Hankin, Benjamin L.

2014-01-01

Theories of affective learning suggest that early experiences contribute to emotional disorders by influencing the development of processing biases for negative emotional stimuli. Although studies have shown that physically abused children preferentially attend to angry faces, it is unclear whether youth exposed to more typical aspects of negative…

Correction for dynamic bias error in transmission measurements of void fraction

International Nuclear Information System (INIS)

Andersson, P.; Sundén, E. Andersson; Svärd, S. Jacobsson; Sjöstrand, H.

2012-01-01

Dynamic bias errors occur in transmission measurements, such as X-ray, gamma, or neutron radiography or tomography. This is observed when the properties of the object are not stationary in time and its average properties are assessed. The nonlinear measurement response to changes in transmission within the time scale of the measurement implies a bias, which can be difficult to correct for. A typical example is the tomographic or radiographic mapping of void content in dynamic two-phase flow systems. In this work, the dynamic bias error is described and a method to make a first-order correction is derived. A prerequisite for this method is variance estimates of the system dynamics, which can be obtained using high-speed, time-resolved data acquisition. However, in the absence of such acquisition, a priori knowledge might be used to substitute the time resolved data. Using synthetic data, a void fraction measurement case study has been simulated to demonstrate the performance of the suggested method. The transmission length of the radiation in the object under study and the type of fluctuation of the void fraction have been varied. Significant decreases in the dynamic bias error were achieved to the expense of marginal decreases in precision.

Experimenter Confirmation Bias and the Correction of Science Misconceptions

Science.gov (United States)

Allen, Michael; Coole, Hilary

2012-06-01

This paper describes a randomised educational experiment ( n = 47) that examined two different teaching methods and compared their effectiveness at correcting one science misconception using a sample of trainee primary school teachers. The treatment was designed to promote engagement with the scientific concept by eliciting emotional responses from learners that were triggered by their own confirmation biases. The treatment group showed superior learning gains to control at post-test immediately after the lesson, although benefits had dissipated after 6 weeks. Findings are discussed with reference to the conceptual change paradigm and to the importance of feeling emotion during a learning experience, having implications for the teaching of pedagogies to adults that have been previously shown to be successful with children.

FPGA prototyping by Verilog examples Xilinx Spartan-3 version

CERN Document Server

Chu, Pong P

2008-01-01

FPGA Prototyping Using Verilog Examples will provide you with a hands-on introduction to Verilog synthesis and FPGA programming through a "learn by doing" approach. By following the clear, easy-to-understand templates for code development and the numerous practical examples, you can quickly develop and simulate a sophisticated digital circuit, realize it on a prototyping device, and verify the operation of its physical implementation. This introductory text that will provide you with a solid foundation, instill confidence with rigorous examples for complex systems and prepare you for future development tasks.

An example of active learning in Aerospace Engineering

NARCIS (Netherlands)

Brugemann, V.P.; Brummelen, van E.H.; Melkert, J.A.; Kamp, A.; Saunders-Smits, G.N.; Reith, B.A.; Zandbergen, B.T.C.; Graaf, de E.; Saunders-Smits, G.N.; Nieweg, M.R.

2005-01-01

This paper is a showcase for an on-going active learning capstone design project in the BSe. programme at the Faculty of Aerospace Engineering at Delft University of Technology. In multi-disciplinary teams supervised by tutors from different backgrounds students work towards an Aerospace (related)

Classification based upon gene expression data: bias and precision of error rates.

Science.gov (United States)

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Approximate Bias Correction in Econometrics

OpenAIRE

James G. MacKinnon; Anthony A. Smith Jr.

1995-01-01

This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is necessary that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some other method. If so, bias can be reduced or, in some cases that may not be unrealistic, even eliminated. In general, several evaluations of the bias function will be required to do this. Unfortunately, reducing bias may increase the variance, or even the mea...

Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues.

Science.gov (United States)

Gepperth, Alexander R T; Rebhan, Sven; Hasler, Stephan; Fritsch, Jannik

2011-03-01

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained.

Towards addressing transient learning challenges in undergraduate physics: an example from electrostatics

Science.gov (United States)

Fredlund, T.; Linder, C.; Airey, J.

2015-09-01

In this article we characterize transient learning challenges as learning challenges that arise out of teaching situations rather than conflicts with prior knowledge. We propose that these learning challenges can be identified by paying careful attention to the representations that students produce. Once a transient learning challenge has been identified, teachers can create interventions to address it. By illustration, we argue that an appropriate way to design such interventions is to create variation around the disciplinary-relevant aspects associated with the transient learning challenge.

Probabilistic causality, selection bias, and the logic of the democratic peace

OpenAIRE

Slantchev, Branislav L; Alexandrova, A; Gartzke, E

2005-01-01

Rosato (2003) claims to have discredited democratic peace theories. However, the methodological approach adopted by the study cannot reliably generate the conclusions espoused by the author. Rosato seems to misunderstand the probabilistic nature of most arguments about democratic peace and ignores issues that an appropriate research design should account for. Further, the study's use of case studies and data sets without attention to selection-bias produces examples that actually support theo...

Developing a Learning Progression for Curriculum, Instruction, and Student Learning: An Example from Mathematics Education

Science.gov (United States)

Fonger, Nicole L.; Stephens, Ana; Blanton, Maria; Isler, Isil; Knuth, Eric; Gardiner, Angela Murphy

2018-01-01

Learning progressions have been demarcated by some for science education, or only concerned with levels of sophistication in student thinking as determined by logical analyses of the discipline. We take the stance that learning progressions can be leveraged in mathematics education as a form of curriculum research that advances a linked…

Workplaces as Transformative Learning Spaces

DEFF Research Database (Denmark)

Maslo, Elina

2010-01-01

some other examples on “successful learning” from the formal, informal and non-formal learning environments, trying to prove those criteria. This presentation provides a view on to new examples on transformative learning spaces we discovered doing research on Workplace Learning in Latvia as a part......Abstract to the Vietnam Forum on Lifelong Learning: Building a Learning Society Hanoi, 7-8 December 2010 Network 2: Competence development as Workplace Learning Title of proposal: Workplaces as Transformative Learning Spaces Author: Elina Maslo, dr. paed., University of Latvia, elina@latnet.lv Key...... words: learning, lifelong learning, adult learning, workplace learning, transformative learning spaces During many years of research on lifelong foreign language learning with very different groups of learners, we found some criteria, which make learning process successful. Since then we tried to find...

Time-delayed fronts from biased random walks

International Nuclear Information System (INIS)

Fort, Joaquim; Pujol, Toni

2007-01-01

We generalize a previous model of time-delayed reaction-diffusion fronts (Fort and Mendez 1999 Phys. Rev. Lett. 82 867) to allow for a bias in the microscopic random walk of particles or individuals. We also present a second model which takes the time order of events (diffusion and reproduction) into account. As an example, we apply them to the human invasion front across the USA in the 19th century. The corrections relative to the previous model are substantial. Our results are relevant to physical and biological systems with anisotropic fronts, including particle diffusion in disordered lattices, population invasions, the spread of epidemics, etc

Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard.

Science.gov (United States)

Karch, Annika; Koch, Armin; Zapf, Antonia; Zerr, Inga; Karch, André

2016-10-01

To investigate how choice of gold standard biases estimates of sensitivity and specificity in studies reassessing the diagnostic accuracy of biomarkers that are already part of a lifetime composite gold standard (CGS). We performed a simulation study based on the real-life example of the biomarker "protein 14-3-3" used for diagnosing Creutzfeldt-Jakob disease. Three different types of gold standard were compared: perfect gold standard "autopsy" (available in a small fraction only; prone to partial verification bias), lifetime CGS (including the biomarker under investigation; prone to incorporation bias), and "best available" gold standard (autopsy if available, otherwise CGS). Sensitivity was unbiased when comparing 14-3-3 with autopsy but overestimated when using CGS or "best available" gold standard. Specificity of 14-3-3 was underestimated in scenarios comparing 14-3-3 with autopsy (up to 24%). In contrast, overestimation (up to 20%) was observed for specificity compared with CGS; this could be reduced to 0-10% when using the "best available" gold standard. Choice of gold standard affects considerably estimates of diagnostic accuracy. Using the "best available" gold standard (autopsy where available, otherwise CGS) leads to valid estimates of specificity, whereas sensitivity is estimated best when tested against autopsy alone. Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-faceted Rasch measurement and bias patterns in EFL writing performance assessment.

Science.gov (United States)

He, Tung-Hsien; Gou, Wen Johnny; Chien, Ya-Chen; Chen, I-Shan Jenny; Chang, Shan-Mao

2013-04-01

This study applied multi-faceted Rasch measurement to examine rater bias in the assessment of essays written by college students learning English as a foreign language. Four raters who had received different academic training from four distinctive disciplines applied a six-category rating scale to analytically rate essays on an argumentative topic and on a descriptive topic. FACETS, a Rasch computer program, was utilized to pinpoint bias patterns by analyzing the rater-topic, rater-category, and topic-category interactions. Results showed: argumentative essays were rated more severely than were descriptive essays; the linguistics-major rater was the most lenient rater, while the literature-major rater was the severest one; and the category of language use received the severest ratings, whereas content was given the most lenient ratings. The severity hierarchies for raters, essay topics, and rating categories suggested that raters' academic training and their perceptions of the importance of categories were associated with their bias patterns. Implications for rater training are discussed.

Using Example Problems to Improve Student Learning in Algebra: Differentiating between Correct and Incorrect Examples

Science.gov (United States)

Booth, Julie L.; Lange, Karin E.; Koedinger, Kenneth R.; Newton, Kristie J.

2013-01-01

In a series of two "in vivo" experiments, we examine whether correct and incorrect examples with prompts for self-explanation can be effective for improving students' conceptual understanding and procedural skill in Algebra when combined with guided practice. In Experiment 1, students working with the Algebra I Cognitive Tutor were randomly…

Characterizing sampling and quality screening biases in infrared and microwave limb sounding

Science.gov (United States)

Millán, Luis F.; Livesey, Nathaniel J.; Santee, Michelle L.; von Clarmann, Thomas

2018-03-01

This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected. The results show that upper-tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10 to 30 %, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30 to 100 % for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper-tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points that no additional bias is introduced, although its penetration is limited to the upper troposphere, while MIPAS may cover well into the mid-troposphere in cloud-free scenarios. We emphasize that the

Beyond attentional bias: a perceptual bias in a dot-probe task.

Science.gov (United States)

Bocanegra, Bruno R; Huijding, Jorg; Zeelenberg, René

2012-12-01

Previous dot-probe studies indicate that threat-related face cues induce a bias in spatial attention. Independently of spatial attention, a recent psychophysical study suggests that a bilateral fearful face cue improves low spatial-frequency perception (LSF) and impairs high spatial-frequency perception (HSF). Here, we combine these separate lines of research within a single dot-probe paradigm. We found that a bilateral fearful face cue, compared with a bilateral neutral face cue, speeded up responses to LSF targets and slowed down responses to HSF targets. This finding is important, as it shows that emotional cues in dot-probe tasks not only bias where information is preferentially processed (i.e., an attentional bias in spatial location), but also bias what type of information is preferentially processed (i.e., a perceptual bias in spatial frequency). PsycINFO Database Record (c) 2012 APA, all rights reserved.

Simulating publication bias

DEFF Research Database (Denmark)

Paldam, Martin

is censoring: selection by the size of estimate; SR3 selects the optimal combination of fit and size; and SR4 selects the first satisficing result. The last four SRs are steered by priors and result in bias. The MST and the FAT-PET have been developed for detection and correction of such bias. The simulations......Economic research typically runs J regressions for each selected for publication – it is often selected as the ‘best’ of the regressions. The paper examines five possible meanings of the word ‘best’: SR0 is ideal selection with no bias; SR1 is polishing: selection by statistical fit; SR2...... are made by data variation, while the model is the same. It appears that SR0 generates narrow funnels much at odds with observed funnels, while the other four funnels look more realistic. SR1 to SR4 give the mean a substantial bias that confirms the prior causing the bias. The FAT-PET MRA works well...

Adaptive cultural transmission biases in children and nonhuman primates.

Science.gov (United States)

Price, Elizabeth E; Wood, Lara A; Whiten, Andrew

2017-08-01

Comparative and evolutionary developmental analyses seek to discover the similarities and differences between humans and non-human species that might illuminate both the evolutionary foundations of our nature that we share with other animals, and the distinctive characteristics that make human development unique. As our closest animal relatives, with whom we last shared common ancestry, non-human primates have been particularly important in this endeavour. Such studies have focused on social learning, traditions, and culture, and have discovered much about the 'how' of social learning, concerned with key underlying processes such as imitation and emulation. One of the core discoveries is that the adaptive adjustment of social learning options to different contexts is not unique to human, therefore multiple new strands of research have begun to focus on more subtle questions about when, from whom, and why such learning occurs. Here we review illustrative studies on both human infants and young children and on non-human primates to identify the similarities shared more broadly across the primate order, and the apparent specialisms that distinguish human development. Adaptive biases in social learning discussed include those modulated by task comprehension, experience, conformity to majorities, and the age, skill, proficiency and familiarity of potential alternative cultural models. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

A study on investors’ personality characteristics and behavioral biases: Conservatism bias and availability bias in the Tehran Stock Exchange

Directory of Open Access Journals (Sweden)

Mahmoud Moradi

2013-04-01

Full Text Available Most economic and finance theories are based on the assumption that during economic decision making, people would act totally rational and consider all available information. Nevertheless, behavioral finance focuses on studying of the role of psychological factors on economic participants’ behavior. The study shows that in real-world environment, people are influenced by emotional and cognitive errors and may make irrational financial decisions. In many cases, the participants of financial markets are not aware of their talents for error in decision making, so they are dissatisfied with their investments by considering some behavioral biases decisions. These decisions may often yield undesirable outcomes, which could influence economy, significantly. This paper presents a survey on the relationship between personality dimensions with behavioral biases and availability bias among investment managers in the Tehran Stock Exchange using SPSS software, descriptive and inferential statistics. The necessary data are collected through questionnaire and they are analyzed using some statistical tests. The preliminary results indicate that there is a relationship between personality dimensions and behavioral biases like conservatism bias and availability bias among the investors in the Tehran Stock Exchange.

Length bias correction in one-day cross-sectional assessments - The nutritionDay study.

Science.gov (United States)

Frantal, Sophie; Pernicka, Elisabeth; Hiesmayr, Michael; Schindler, Karin; Bauer, Peter

2016-04-01

A major problem occurring in cross-sectional studies is sampling bias. Length of hospital stay (LOS) differs strongly between patients and causes a length bias as patients with longer LOS are more likely to be included and are therefore overrepresented in this type of study. To adjust for the length bias higher weights are allocated to patients with shorter LOS. We determined the effect of length-bias adjustment in two independent populations. Length-bias correction is applied to the data of the nutritionDay project, a one-day multinational cross-sectional audit capturing data on disease and nutrition of patients admitted to hospital wards with right-censoring after 30 days follow-up. We applied the weighting method for estimating the distribution function of patient baseline variables based on the method of non-parametric maximum likelihood. Results are validated using data from all patients admitted to the General Hospital of Vienna between 2005 and 2009, where the distribution of LOS can be assumed to be known. Additionally, a simplified calculation scheme for estimating the adjusted distribution function of LOS is demonstrated on a small patient example. The crude median (lower quartile; upper quartile) LOS in the cross-sectional sample was 14 (8; 24) and decreased to 7 (4; 12) when adjusted. Hence, adjustment for length bias in cross-sectional studies is essential to get appropriate estimates. Copyright © 2015 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Indirect assessment of an interpretation bias in humans: Neurophysiological and behavioral correlates

Directory of Open Access Journals (Sweden)

Anita eSchick

2013-06-01

Full Text Available Affective state can influence cognition leading to biased information processing, interpretation, attention, and memory. Such bias has been reported to be essential for the onset and maintenance of different psychopathologies, particularly affective disorders. However, empirical evidence has been very heterogeneous and little is known about the neurophysiological mechanisms underlying cognitive bias and its time-course. We therefore investigated the interpretation of ambiguous stimuli as indicators of biased information processing with an ambiguous cue-conditioning paradigm. In an acquisition phase, participants learned to discriminate two tones of different frequency, which acquired emotional and motivational value due to subsequent feedback (monetary gain or avoidance of monetary loss. In the test phase, three additional tones of intermediate frequencies were presented, whose interpretation as positive (approach of reward or negative (avoidance of punishment, indicated by a button press, was used as an indicator of the bias. Twenty healthy volunteers participated in this paradigm while a 64-channel electroencephalogram was recorded. Participants also completed questionnaires assessing individual differences in depression and rumination. Overall, we found a small positive bias, which correlated negatively with reflective pondering, a type of rumination. As expected, reaction times were increased for intermediate tones. ERP amplitudes between 300 – 700 ms post-stimulus differed depending on the interpretation of the intermediate tones. A negative compared to a positive interpretation led to an amplitude increase over frontal electrodes. Our study provides evidence that in humans, as in animal research, the ambiguous cue-conditioning paradigm is a valid procedure for indirectly assessing ambiguous cue interpretation and a potential interpretation bias, which is sensitive to individual differences in affect-related traits.

Long-term memory biases auditory spatial attention.

Science.gov (United States)

Zimmermann, Jacqueline F; Moscovitch, Morris; Alain, Claude

2017-10-01

Long-term memory (LTM) has been shown to bias attention to a previously learned visual target location. Here, we examined whether memory-predicted spatial location can facilitate the detection of a faint pure tone target embedded in real world audio clips (e.g., soundtrack of a restaurant). During an initial familiarization task, participants heard audio clips, some of which included a lateralized target (p = 50%). On each trial participants indicated whether the target was presented from the left, right, or was absent. Following a 1 hr retention interval, participants were presented with the same audio clips, which now all included a target. In Experiment 1, participants showed memory-based gains in response time and d'. Experiment 2 showed that temporal expectations modulate attention, with greater memory-guided attention effects on performance when temporal context was reinstated from learning (i.e., when timing of the target within audio clips was not changed from initially learned timing). Experiment 3 showed that while conscious recall of target locations was modulated by exposure to target-context associations during learning (i.e., better recall with higher number of learning blocks), the influence of LTM associations on spatial attention was not reduced (i.e., number of learning blocks did not affect memory-guided attention). Both Experiments 2 and 3 showed gains in performance related to target-context associations, even for associations that were not explicitly remembered. Together, these findings indicate that memory for audio clips is acquired quickly and is surprisingly robust; both implicit and explicit LTM for the location of a faint target tone modulated auditory spatial attention. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Experimental modification of interpretation bias about animal fear in young children: effects on cognition, avoidance behavior, anxiety vulnerability, and physiological responding.

Science.gov (United States)

Lester, Kathryn J; Field, Andy P; Muris, Peter

2011-01-01

This study investigated the effects of experimentally modifying interpretation biases for children's cognitions, avoidance behavior, anxiety vulnerability, and physiological responding. Sixty-seven children (6-11 years) were randomly assigned to receive a positive or negative interpretation bias modification procedure to induce interpretation biases toward or away from threat about ambiguous situations involving Australian marsupials. Children rapidly learned to select outcomes of ambiguous situations, which were congruent with their assigned condition. Furthermore, following positive modification, children's threat biases about novel ambiguous situations significantly decreased, whereas threat biases significantly increased after negative modification. In response to a stress-evoking behavioral avoidance test, positive modification attenuated behavioral avoidance compared to negative modification. However, no significant effects of bias modification on anxiety vulnerability or physiological responses to this stress-evoking Behavioral Avoidance Task were observed.

Prime Example Ingress Reframing the Pervasive Game Design Framework (PGDF

Directory of Open Access Journals (Sweden)

Heinrich Söbke

2017-06-01

Full Text Available The growing availability of mobile communication infrastructure over the last decade has contributed significantly to the maturity of Pervasive Gaming. The massive success of games such as Ingress and Pokémon Go made pervasive gaming a viable option for transforming learning. By its adaptability to location and context, pervasive technology is a valuable support for the design of engaging learning experiences. Despite profound examples of pervasive gaming as learning tool, there is still a lack of reliable methodologies to construct purposeful pervasive learning experiences. The Pervasive Game Design Framework (PGDF is intended to fill this gap. In this article, we present the PGDF using the example of Ingress. Ingress is a prominent pervasive game, as it has received huge attention since its appearance in 2012. A large community of players and third-party-tool suppliers has created a rich set of experiences since then. In this research, we examine Ingress according to PGDF’s categories based on a survey among long-term Ingress players (N=133. Founded on this analysis we identify three main benefits for Ingress players. Furthermore, we discuss the consequences of these findings on the PGDF. Summarizing, this work strengthens the applicability of the PGDF, in order to enable the construction of enriched pervasive learning experiences.

Media bias under direct and indirect government control: when is the bias smaller?

OpenAIRE

Abhra Roy

2015-01-01

We present an analytical framework to compare media bias under direct and indirect government control. In this context, we show that direct control can lead to a smaller bias and higher welfare than indirect control. We further show that the size of the advertising market affects media bias only under direct control. Media bias, under indirect control, is not affected by the size of the advertising market.

Mathematics as a constructive activity learners generating examples

CERN Document Server

Watson, Anne

2005-01-01

Explains and demonstrates the role of examples in the teaching and learning of mathematics, and their place in mathematics generally at all levels. Includes a combination of exercises for the reader, practical applications for teaching, and solid scholarly grounding.

Large-scale galaxy bias

Science.gov (United States)

Desjacques, Vincent; Jeong, Donghui; Schmidt, Fabian

2018-02-01

This review presents a comprehensive overview of galaxy bias, that is, the statistical relation between the distribution of galaxies and matter. We focus on large scales where cosmic density fields are quasi-linear. On these scales, the clustering of galaxies can be described by a perturbative bias expansion, and the complicated physics of galaxy formation is absorbed by a finite set of coefficients of the expansion, called bias parameters. The review begins with a detailed derivation of this very important result, which forms the basis of the rigorous perturbative description of galaxy clustering, under the assumptions of General Relativity and Gaussian, adiabatic initial conditions. Key components of the bias expansion are all leading local gravitational observables, which include the matter density but also tidal fields and their time derivatives. We hence expand the definition of local bias to encompass all these contributions. This derivation is followed by a presentation of the peak-background split in its general form, which elucidates the physical meaning of the bias parameters, and a detailed description of the connection between bias parameters and galaxy statistics. We then review the excursion-set formalism and peak theory which provide predictions for the values of the bias parameters. In the remainder of the review, we consider the generalizations of galaxy bias required in the presence of various types of cosmological physics that go beyond pressureless matter with adiabatic, Gaussian initial conditions: primordial non-Gaussianity, massive neutrinos, baryon-CDM isocurvature perturbations, dark energy, and modified gravity. Finally, we discuss how the description of galaxy bias in the galaxies' rest frame is related to clustering statistics measured from the observed angular positions and redshifts in actual galaxy catalogs.

Not All Emotions Are Created Equal: The Negativity Bias in Social-Emotional Development

Science.gov (United States)

Vaish, Amrisha; Grossman, Tobias; Woodward, Amanda

2008-01-01

There is ample empirical evidence for an asymmetry in the way that adults use positive versus negative information to make sense of their world; specifically, across an array of psychological situations and tasks, adults display a negativity bias, or the propensity to attend to, learn from, and use negative information far more than positive…

A biased activation theory of the cognitive and attentional modulation of emotion.

Science.gov (United States)

Rolls, Edmund T

2013-01-01

Cognition can influence emotion by biasing neural activity in the first cortical region in which the reward value and subjective pleasantness of stimuli is made explicit in the representation, the orbitofrontal cortex (OFC). The same effect occurs in a second cortical tier for emotion, the anterior cingulate cortex (ACC). Similar effects are found for selective attention, to for example the pleasantness vs. the intensity of stimuli, which modulates representations of reward value and affect in the orbitofrontal and anterior cingulate cortices. The mechanisms for the effects of cognition and attention on emotion are top-down biased competition and top-down biased activation. Affective and mood states can in turn influence memory and perception, by backprojected biasing influences. Emotion-related decision systems operate to choose between gene-specified rewards such as taste, touch, and beauty. Reasoning processes capable of planning ahead with multiple steps held in working memory in the explicit system can allow the gene-specified rewards not to be selected, or to be deferred. The stochastic, noisy, dynamics of decision-making systems in the brain may influence whether decisions are made by the selfish-gene-specified reward emotion system, or by the cognitive reasoning system that explicitly calculates reward values that are in the interests of the individual, the phenotype.

A biased activation theory of the cognitive and attentional modulation of emotion

Directory of Open Access Journals (Sweden)

Edmund eRolls

2013-03-01

Full Text Available Cognition can influence emotion by biasing neural activity in the first cortical region in which the reward value and subjective pleasantness of stimuli is made explicit in the representation, the orbitofrontal cortex. The same effect occurs in a second cortical tier for emotion, the anterior cingulate cortex. Similar effects are found for selective attention, to for example the pleasantness vs the intensity of stimuli, which modulates representations of reward value and affect in the orbitofrontal and anterior cingulate cortices. The mechanisms for the effects of cognition and attention on emotion are top-down biased competition and top-down biased activation. Affective and mood states can in turn influence memory and perception, by backprojected biasing influences. Emotion-related decision systems operate to choose between gene-specified rewards such as taste, touch, and beauty. Reasoning processes capable of planning ahead with multiple steps held in working memory in the explicit system can allow the gene-specified rewards not to be selected, or to be deferred. The stochastic, noisy, dynamics of decision-making systems in the brain may influence whether decisions are made by the selfish-gene-specified reward emotion system, or by the cognitive reasoning system that explicitly calculates reward values that are in the interests of the individual, the phenotype.

Beyond Rote Learning in Organic Chemistry: The Infusion and Impact of Argumentation in Tertiary Education

Science.gov (United States)

Pabuccu, Aybuke; Erduran, Sibel

2017-01-01

There exists bias among students that learning organic chemistry topics requires rote learning. In this paper, we address such bias through an organic chemistry activity designed to promote argumentation. We investigated how pre-service science teachers engage in an argumentation about conformational analysis. Analysis of the outcomes concentrated…

Computer game-based mathematics education : Embedded faded worked examples facilitate knowledge acquisition

NARCIS (Netherlands)

ter Vrugte, Judith; de Jong, Anthonius J.M.; Vandercruysse, Sylke; Wouters, Pieter; van Oostendorp, Herre; Elen, Jan

This study addresses the added value of faded worked examples in a computer game-based learning environment. The faded worked examples were introduced to encourage active selection and processing of domain content in the game. The content of the game was proportional reasoning and participants were

Large-scale galaxy bias

Science.gov (United States)

Jeong, Donghui; Desjacques, Vincent; Schmidt, Fabian

2018-01-01

Here, we briefly introduce the key results of the recent review (arXiv:1611.09787), whose abstract is as following. This review presents a comprehensive overview of galaxy bias, that is, the statistical relation between the distribution of galaxies and matter. We focus on large scales where cosmic density fields are quasi-linear. On these scales, the clustering of galaxies can be described by a perturbative bias expansion, and the complicated physics of galaxy formation is absorbed by a finite set of coefficients of the expansion, called bias parameters. The review begins with a detailed derivation of this very important result, which forms the basis of the rigorous perturbative description of galaxy clustering, under the assumptions of General Relativity and Gaussian, adiabatic initial conditions. Key components of the bias expansion are all leading local gravitational observables, which include the matter density but also tidal fields and their time derivatives. We hence expand the definition of local bias to encompass all these contributions. This derivation is followed by a presentation of the peak-background split in its general form, which elucidates the physical meaning of the bias parameters, and a detailed description of the connection between bias parameters and galaxy (or halo) statistics. We then review the excursion set formalism and peak theory which provide predictions for the values of the bias parameters. In the remainder of the review, we consider the generalizations of galaxy bias required in the presence of various types of cosmological physics that go beyond pressureless matter with adiabatic, Gaussian initial conditions: primordial non-Gaussianity, massive neutrinos, baryon-CDM isocurvature perturbations, dark energy, and modified gravity. Finally, we discuss how the description of galaxy bias in the galaxies' rest frame is related to clustering statistics measured from the observed angular positions and redshifts in actual galaxy catalogs.

Landscapes of Musical Metaphor and Musical Learning: The Example of Jazz Education

Science.gov (United States)

Bjerstedt, Sven

2015-01-01

Theoretical approaches to learning in practice-based jazz improvisation contexts include situated learning and ecological perspectives. This article focuses on how interest-driven, self-sustaining jazz learning activities can be matched against the results of a recent Swedish investigation based on extensive qualitative interviews with jazz…

Information environment, behavioral biases, and home bias in analysts’ recommendations

DEFF Research Database (Denmark)

Farooq, Omar; Taouss, Mohammed

2012-01-01

Can information environment of a firm explain home bias in analysts’ recommendations? Can the extent of agency problems explain optimism difference between foreign and local analysts? This paper answers these questions by documenting the effect of information environment on home bias in analysts’...

Infants learn better from left to right: a directional bias in infants' sequence learning.

Science.gov (United States)

Bulf, Hermann; de Hevia, Maria Dolores; Gariboldi, Valeria; Macchi Cassia, Viola

2017-05-26

A wealth of studies show that human adults map ordered information onto a directional spatial continuum. We asked whether mapping ordinal information into a directional space constitutes an early predisposition, already functional prior to the acquisition of symbolic knowledge and language. While it is known that preverbal infants represent numerical order along a left-to-right spatial continuum, no studies have investigated yet whether infants, like adults, organize any kind of ordinal information onto a directional space. We investigated whether 7-month-olds' ability to learn high-order rule-like patterns from visual sequences of geometric shapes was affected by the spatial orientation of the sequences (left-to-right vs. right-to-left). Results showed that infants readily learn rule-like patterns when visual sequences were presented from left to right, but not when presented from right to left. This result provides evidence that spatial orientation critically determines preverbal infants' ability to perceive and learn ordered information in visual sequences, opening to the idea that a left-to-right spatially organized mental representation of ordered dimensions might be rooted in biologically-determined constraints on human brain development.

Enhancing Learning within the 3-D Virtual Learning Environment

OpenAIRE

Shirin Shafieiyoun; Akbar Moazen Safaei

2013-01-01

Today’s using of virtual learning environments becomes more remarkable in education. The potential of virtual learning environments has frequently been related to the expansion of sense of social presence which is obtained from students and educators. This study investigated the effectiveness of social presence within virtual learning environments and analysed the impact of social presence on increasing learning satisfaction within virtual learning environments. Second Life, as an example of ...

Does gender bias influence awards given by societies?

Science.gov (United States)

Holmes, Mary Anne; Asher, Pranoti; Farrington, John; Fine, Rana; Leinen, Margaret S.; LeBoy, Phoebe

2011-11-01

AGU is a participant in a U.S. National Science Foundation (NSF)-funded project called Advancing Ways of Awarding Recognition in Disciplinary Societies (AWARDS), which seeks to examine whether gender bias affects selection of recipients of society awards. AGU is interested in learning why there is a higher proportion of female recipients of service and education awards over the past 2 decades. Combined with a lower rate of receipt of research awards, these results suggest that implicit (subconscious) bias in favor of male candidates still influences awardee selection. Six other professional societies (American Chemical Society, American Mathematical Society, American Society of Anesthesiologists, Mathematical Association of America, Society for Neuroscience, and Society for Industrial and Applied Mathematics) are participating in the project. Volunteers from each participant society attended an Association for Women in Science (AWIS)-sponsored workshop in May 2010 to examine data and review literature on best practices for fair selection of society awardees. A draft proposal for implementing these practices will be brought before the AGU Council and the Honors and Recognition Committee at their upcoming meetings.

Cultural Learning Redux

Science.gov (United States)

Tomasello, Michael

2016-01-01

M. Tomasello, A. Kruger, and H. Ratner (1993) proposed a theory of cultural learning comprising imitative learning, instructed learning, and collaborative learning. Empirical and theoretical advances in the past 20 years suggest modifications to the theory; for example, children do not just imitate but overimitate in order to identify and…

Consider the category: The effect of spacing depends on individual learning histories.

Science.gov (United States)

Slone, Lauren K; Sandhofer, Catherine M

2017-07-01

The spacing effect refers to increased retention following learning instances that are spaced out in time compared with massed together in time. By one account, the advantages of spaced learning should be independent of task particulars and previous learning experiences given that spacing effects have been demonstrated in a variety of tasks across the lifespan. However, by another account, spaced learning should be affected by previous learning because past learning affects the memory and attention processes that form the crux of the spacing effect. The current study investigated whether individuals' learning histories affect the role of spacing in category learning. We examined the effect of spacing on 24 2- to 3.5-year-old children's learning of categories organized by properties to which children's previous learning experiences have biased them to attend (i.e., shape) and properties to which children are less biased to attend (i.e., texture and color). Spaced presentations led to significantly better learning of shape categories, but not of texture or color categories, compared with massed presentations. In addition, generalized estimating equations analyses revealed positive relations between the size of children's "shape-side" productive vocabularies and their shape category learning and between the size of children's "against-the-system" productive vocabularies and their texture category learning. These results suggest that children's attention to and memory for novel object categories are strongly related to their individual word-learning histories. Moreover, children's learned attentional biases affected the types of categories for which spacing facilitated learning. These findings highlight the importance of considering how learners' previous experiences may influence future learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Individuals Who Believe in the Paranormal Expose Themselves to Biased Information and Develop More Causal Illusions than Nonbelievers in the Laboratory.

Science.gov (United States)

Blanco, Fernando; Barberia, Itxaso; Matute, Helena

2015-01-01

In the reasoning literature, paranormal beliefs have been proposed to be linked to two related phenomena: a biased perception of causality and a biased information-sampling strategy (believers tend to test fewer hypotheses and prefer confirmatory information). In parallel, recent contingency learning studies showed that, when two unrelated events coincide frequently, individuals interpret this ambiguous pattern as evidence of a causal relationship. Moreover, the latter studies indicate that sampling more cause-present cases than cause-absent cases strengthens the illusion. If paranormal believers actually exhibit a biased exposure to the available information, they should also show this bias in the contingency learning task: they would in fact expose themselves to more cause-present cases than cause-absent trials. Thus, by combining the two traditions, we predicted that believers in the paranormal would be more vulnerable to developing causal illusions in the laboratory than nonbelievers because there is a bias in the information they experience. In this study, we found that paranormal beliefs (measured using a questionnaire) correlated with causal illusions (assessed by using contingency judgments). As expected, this correlation was mediated entirely by the believers' tendency to expose themselves to more cause-present cases. The association between paranormal beliefs, biased exposure to information, and causal illusions was only observed for ambiguous materials (i.e., the noncontingent condition). In contrast, the participants' ability to detect causal relationships which did exist (i.e., the contingent condition) was unaffected by their susceptibility to believe in paranormal phenomena.

Individuals Who Believe in the Paranormal Expose Themselves to Biased Information and Develop More Causal Illusions than Nonbelievers in the Laboratory.

Directory of Open Access Journals (Sweden)

Fernando Blanco

Full Text Available In the reasoning literature, paranormal beliefs have been proposed to be linked to two related phenomena: a biased perception of causality and a biased information-sampling strategy (believers tend to test fewer hypotheses and prefer confirmatory information. In parallel, recent contingency learning studies showed that, when two unrelated events coincide frequently, individuals interpret this ambiguous pattern as evidence of a causal relationship. Moreover, the latter studies indicate that sampling more cause-present cases than cause-absent cases strengthens the illusion. If paranormal believers actually exhibit a biased exposure to the available information, they should also show this bias in the contingency learning task: they would in fact expose themselves to more cause-present cases than cause-absent trials. Thus, by combining the two traditions, we predicted that believers in the paranormal would be more vulnerable to developing causal illusions in the laboratory than nonbelievers because there is a bias in the information they experience. In this study, we found that paranormal beliefs (measured using a questionnaire correlated with causal illusions (assessed by using contingency judgments. As expected, this correlation was mediated entirely by the believers' tendency to expose themselves to more cause-present cases. The association between paranormal beliefs, biased exposure to information, and causal illusions was only observed for ambiguous materials (i.e., the noncontingent condition. In contrast, the participants' ability to detect causal relationships which did exist (i.e., the contingent condition was unaffected by their susceptibility to believe in paranormal phenomena.

Vulnerability of classifiers to evolutionary generated adversarial examples

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

submitted 14.1. 2017 (2018) ISSN 0941-0643 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : supervised learning * neural networks * kernel methods * genetic algorithm s * adversarial examples Subject RIV: IN - Informatics, Computer Science Impact factor: 2.505, year: 2016

SOME EXAMPLES OF APPLIED SYSTEMS WITH SPEECH INTERFACE

Directory of Open Access Journals (Sweden)

V. A. Zhitko

2017-01-01

Full Text Available Three examples of applied systems with a speech interface are considered in the article. The first two of these provide the end user with the opportunity to ask verbally the question and to hear the response from the system, creating an addition to the traditional I / O via the keyboard and computer screen. The third example, the «IntonTrainer» system, provides the user with the possibility of voice interaction and is designed for in-depth self-learning of the intonation of oral speech.

Which Technique Is Most Effective for Learning Declarative Concepts--Provided Examples, Generated Examples, or Both?

Science.gov (United States)

Zamary, Amanda; Rawson, Katherine A.

2018-01-01

Students in many courses are commonly expected to learn declarative concepts, which are abstract concepts denoted by key terms with short definitions that can be applied to a variety of scenarios as reported by Rawson et al. ("Educational Psychology Review" 27:483-504, 2015). Given that declarative concepts are common and foundational in…

Fostering clinical reasoning in physiotherapy: comparing the effects of concept map study and concept map completion after example study in novice and advanced learners.

Science.gov (United States)

Montpetit-Tourangeau, Katherine; Dyer, Joseph-Omer; Hudon, Anne; Windsor, Monica; Charlin, Bernard; Mamede, Sílvia; van Gog, Tamara

2017-12-01

Health profession learners can foster clinical reasoning by studying worked examples presenting fully worked out solutions to a clinical problem. It is possible to improve the learning effect of these worked examples by combining them with other learning activities based on concept maps. This study investigated which combinaison of activities, worked examples study with concept map completion or worked examples study with concept map study, fosters more meaningful learning of intervention knowledge in physiotherapy students. Moreover, this study compared the learning effects of these learning activity combinations between novice and advanced learners. Sixty-one second-year physiotherapy students participated in the study which included a pre-test phase, a 130-min guided-learning phase and a four-week self-study phase. During the guided and self-study learning sessions, participants had to study three written worked examples presenting the clinical reasoning for selecting electrotherapeutic currents to treat patients with motor deficits. After each example, participants engaged in either concept map completion or concept map study depending on which learning condition they were randomly allocated to. Students participated in an immediate post-test at the end of the guided-learning phase and a delayed post-test at the end of the self-study phase. Post-tests assessed the understanding of principles governing the domain of knowledge to be learned (conceptual knowledge) and the ability to solve new problems that have similar (i.e., near transfer) or different (i.e., far transfer) solution rationales as problems previously studied in the examples. Learners engaged in concept map completion outperformed those engaged in concept map study on near transfer (p = .010) and far transfer (p concept map completion led to greater transfer performance than worked examples study combined with concept map study for both novice and advanced learners. Concept map completion

Learning about physical parameters: the importance of model discrepancy

International Nuclear Information System (INIS)

Brynjarsdóttir, Jenný; O'Hagan, Anthony

2014-01-01

Science-based simulation models are widely used to predict the behavior of complex physical systems. It is also common to use observations of the physical system to solve the inverse problem, that is, to learn about the values of parameters within the model, a process which is often called calibration. The main goal of calibration is usually to improve the predictive performance of the simulator but the values of the parameters in the model may also be of intrinsic scientific interest in their own right. In order to make appropriate use of observations of the physical system it is important to recognize model discrepancy, the difference between reality and the simulator output. We illustrate through a simple example that an analysis that does not account for model discrepancy may lead to biased and over-confident parameter estimates and predictions. The challenge with incorporating model discrepancy in statistical inverse problems is being confounded with calibration parameters, which will only be resolved with meaningful priors. For our simple example, we model the model-discrepancy via a Gaussian process and demonstrate that through accounting for model discrepancy our prediction within the range of data is correct. However, only with realistic priors on the model discrepancy do we uncover the true parameter values. Through theoretical arguments we show that these findings are typical of the general problem of learning about physical parameters and the underlying physical system using science-based mechanistic models. (paper)

Applications of Machine Learning in Cancer Prediction and Prognosis

Directory of Open Access Journals (Sweden)

Joseph A. Cruz

2006-01-01

Full Text Available Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25% improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Working Examples (WEx): A Vehicle for Building Radical Innovations to Change Education

Science.gov (United States)

Zywica, Jolene; Roberts, Anna; Davidson, Drew

2013-01-01

Working Examples (WEx) is described by the authors as a vehicle for ideating and building radical innovations to change education. It is a community of researchers, designers, and educators working at the intersection of education and technology. "Examples" (ideas, work, and projects) allow people to explore new ideas, learn from each…

Better than I thought: positive evaluation bias in hypomania.

Directory of Open Access Journals (Sweden)

Liam Mason

Full Text Available Mania is characterised by increased impulsivity and risk-taking, and psychological accounts argue that these features may be due to hypersensitivity to reward. The neurobiological mechanisms remain poorly understood. Here we examine reinforcement learning and sensitivity to both reward and punishment outcomes in hypomania-prone individuals not receiving pharmacotherapy.We recorded EEG from 45 healthy individuals split into three groups by low, intermediate and high self-reported hypomanic traits. Participants played a computerised card game in which they learned the reward contingencies of three cues. Neural responses to monetary gain and loss were measured using the feedback-related negativity (FRN, a component implicated in motivational outcome evaluation and reinforcement learning.As predicted, rewards elicited a smaller FRN in the hypomania-prone group relative to the low hypomania group, indicative of greater reward responsiveness. The hypomania-prone group also showed smaller FRN to losses, indicating diminished response to negative feedback.Our findings indicate that proneness to hypomania is associated with both reward hypersensitivity and discounting of punishment. This positive evaluation bias may be driven by aberrant reinforcement learning signals, which fail to update future expectations. This provides a possible neural mechanism explaining risk-taking and impaired reinforcement learning in BD. Further research will be needed to explore the potential value of the FRN as a biological vulnerability marker for mania and pathological risk-taking.

Bond and Equity Home Bias and Foreign Bias: an International Study

OpenAIRE

VanPée, Rosanne; De Moor, Lieven

2012-01-01

In this paper we explore tentatively and formally the differences between bond and equity home bias and foreign bias based on one large scale dataset including developed and emerging markets for the period 2001 to 2010. We set the stage by tentatively and formally linking the diversion of bond and equity home bias in OECD countries to the increasing public debt issues under the form of government bonds i.e. the supply-driven argument. Unlike Fidora et al. (2007) we do not find that exchange r...

The Evolution of Frequency Distributions: Relating Regularization to Inductive Biases through Iterated Learning

Science.gov (United States)

Reali, Florencia; Griffiths, Thomas L.

2009-01-01

The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this…

How to Explain Receptivity to Conjunction-Fallacy Inhibition Training: Evidence from the Iowa Gambling Task

Science.gov (United States)

Cassotti, Mathieu; Moutier, Sylvain

2010-01-01

Intuitive predictions and judgments under conditions of uncertainty are often mediated by judgment heuristics that sometimes lead to biases. Using the classical conjunction bias example, the present study examines the relationship between receptivity to metacognitive executive training and emotion-based learning ability indexed by Iowa Gambling…

Reducing selection bias in case-control studies from rare disease registries.

Science.gov (United States)

Cole, J Alexander; Taylor, John S; Hangartner, Thomas N; Weinreb, Neal J; Mistry, Pramod K; Khan, Aneal

2011-09-12

In clinical research of rare diseases, where small patient numbers and disease heterogeneity limit study design options, registries are a valuable resource for demographic and outcome information. However, in contrast to prospective, randomized clinical trials, the observational design of registries is prone to introduce selection bias and negatively impact the validity of data analyses. The objective of the study was to demonstrate the utility of case-control matching and the risk-set method in order to control bias in data from a rare disease registry. Data from the International Collaborative Gaucher Group (ICGG) Gaucher Registry were used as an example. A case-control matching analysis using the risk-set method was conducted to identify two groups of patients with type 1 Gaucher disease in the ICGG Gaucher Registry: patients with avascular osteonecrosis (AVN) and those without AVN. The frequency distributions of gender, decade of birth, treatment status, and splenectomy status were presented for cases and controls before and after matching. Odds ratios (and 95% confidence intervals) were calculated for each variable before and after matching. The application of case-control matching methodology results in cohorts of cases (i.e., patients with AVN) and controls (i.e., patients without AVN) who have comparable distributions for four common parameters used in subject selection: gender, year of birth (age), treatment status, and splenectomy status. Matching resulted in odds ratios of approximately 1.00, indicating no bias. We demonstrated bias in case-control selection in subjects from a prototype rare disease registry and used case-control matching to minimize this bias. Therefore, this approach appears useful to study cohorts of heterogeneous patients in rare disease registries.

An Integrated Mixed Methods Research Design: Example of the Project Foreign Language Learning Strategies and Achievement: Analysis of Strategy Clusters and Sequences

OpenAIRE

Vlčková Kateřina

2014-01-01

The presentation focused on an so called integrated mixed method research design example on a basis of a Czech Science Foundation Project Nr. GAP407/12/0432 "Foreign Language Learning Strategies and Achievement: Analysis of Strategy Clusters and Sequences". All main integrated parts of the mixed methods research design were discussed: the aim, theoretical framework, research question, methods and validity threats. Prezentace se zaměřovala na tzv. integrovaný vícemetodový výzkumný design na...

Public policies: right to learn and formative assessment

Directory of Open Access Journals (Sweden)

Antonio Chizzotti

2016-09-01

Full Text Available This paper deals with the right to learn in school type education and considers the assessment as assurance of teaching and learning quality. It deals with the current evaluation processes and discriminatory misconceptions of merely summative assessments, which tend to qualify students. This text evaluates the punitive bias of meritocratic grading of learning and argues that only formative assessment can ensure the right to learn

Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning?

Science.gov (United States)

Gerlai, Robert

2017-08-01

Analysis of the zebrafish allows one to combine two distinct scientific approaches, comparative ethology and neurobehavioral genetics. Furthermore, this species arguably represents an optimal compromise between system complexity and practical simplicity. This mini-review focuses on a complex form of learning, relational learning and memory, in zebrafish. It argues that zebrafish are capable of this type of learning, and it attempts to show how this species may be useful in the analysis of the mechanisms and the evolution of this complex brain function. The review is not intended to be comprehensive. It is a short opinion piece that reflects the author's own biases, and it draws some of its examples from the work coming from his own laboratory. Nevertheless, it is written in the hope that it will persuade those who have not utilized zebrafish and who may be interested in opening their research horizon to this relatively novel but powerful vertebrate research tool. Copyright © 2017 Elsevier B.V. All rights reserved.

A Question of Control? Examining the Role of Control Conditions in Experimental Psychopathology using the Example of Cognitive Bias Modification Research.

Science.gov (United States)

Blackwell, Simon E; Woud, Marcella L; MacLeod, Colin

2017-10-26

While control conditions are vitally important in research, selecting the optimal control condition can be challenging. Problems are likely to arise when the choice of control condition is not tightly guided by the specific question that a given study aims to address. Such problems have become increasingly apparent in experimental psychopathology research investigating the experimental modification of cognitive biases, particularly as the focus of this research has shifted from theoretical questions concerning mechanistic aspects of the association between cognitive bias and emotional vulnerability, to questions that instead concern the clinical efficacy of 'cognitive bias modification' (CBM) procedures. We discuss the kinds of control conditions that have typically been employed in CBM research, illustrating how difficulties can arise when changes in the types of research questions asked are not accompanied by changes in the control conditions employed. Crucially, claims made on the basis of comparing active and control conditions within CBM studies should be restricted to those conclusions allowed by the specific control condition employed. CBM studies aiming to establish clinical utility are likely to require quite different control conditions from CBM studies aiming to illuminate mechanisms. Further, conclusions concerning the clinical utility of CBM interventions cannot necessarily be drawn from studies in which the control condition has been chosen to answer questions concerning mechanisms. Appreciating the need to appropriately alter control conditions in the transition from basic mechanisms-focussed investigations to applied clinical research could greatly facilitate the translational process.

SATLC applications as examples for systemic chemistry education ...

African Journals Online (AJOL)

It is also used as a vehicle to engage the students in a deep learning that focuses on relating ideas and making connection between new and prior knowledge. As applications of SATLC I present here systemic chemistry related units experimented in Egyptian secondary schools and universities with examples on systemic ...

Lessons Learned from the Development of an Example Precision Information Environment for International Safeguards

Energy Technology Data Exchange (ETDEWEB)

Gastelum, Zoe N. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Henry, Michael J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Burtner, IV, E. R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Doehle, J. R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Hampton, S. D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); La Mothe, R. R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Nordquist, P. L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zarzhitsky, D. V. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2014-12-01

The International Atomic Energy Agency (IAEA) is interested in increasing capabilities of IAEA safeguards inspectors to access information that would improve their situational awareness on the job. A mobile information platform could potentially provide access to information, analytics, and technical and logistical support to inspectors in the field, as well as providing regular updates to analysts at IAEA Headquarters in Vienna or at satellite offices. To demonstrate the potential capability of such a system, Pacific Northwest National Laboratory (PNNL) implemented a number of example capabilities within a PNNL-developed precision information environment (PIE), and using a tablet as a mobile information platform. PNNL’s safeguards proof-of-concept PIE intends to; demonstrate novel applications of mobile information platforms to international safeguards use cases; demonstrate proof-of-principle capability implementation; and provide “vision” for capabilities that could be implemented. This report documents the lessons learned from this two-year development activity for the Precision Information Environment for International Safeguards (PIE-IS), describing the developed capabilities, technical challenges, and considerations for future development, so that developers working to develop a similar system for the IAEA or other safeguards agencies might benefit from our work.

Lessons Learned from the Development of an Example Precision Information Environment for International Safeguards

International Nuclear Information System (INIS)

Gastelum, Zoe N.; Henry, Michael J.; Burtner, IV E.R.; Doehle, J. R.; Hampton, S. D.; La Mothe, R. R.; Nordquist, P. L.; Zarzhitsky, D. V.

2014-01-01

The International Atomic Energy Agency (IAEA) is interested in increasing capabilities of IAEA safeguards inspectors to access information that would improve their situational awareness on the job. A mobile information platform could potentially provide access to information, analytics, and technical and logistical support to inspectors in the field, as well as providing regular updates to analysts at IAEA Headquarters in Vienna or at satellite offices. To demonstrate the potential capability of such a system, Pacific Northwest National Laboratory (PNNL) implemented a number of example capabilities within a PNNL-developed precision information environment (PIE), and using a tablet as a mobile information platform. PNNL's safeguards proof-of-concept PIE intends to; demonstrate novel applications of mobile information platforms to international safeguards use cases; demonstrate proof-of-principle capability implementation; and provide ''vision''@ for capabilities that could be implemented. This report documents the lessons learned from this two-year development activity for the Precision Information Environment for International Safeguards (PIE-IS), describing the developed capabilities, technical challenges, and considerations for future development, so that developers working to develop a similar system for the IAEA or other safeguards agencies might benefit from our work.

Example-based learning: Effects of model expertise in relation to student expertise

NARCIS (Netherlands)

T. Boekhout (Teun); T.A.J.M. van Gog (Tamara); M.W.J. van de Wiel (Margje); D. Gerards-Last (Dorien); F. Geraets (Frank)

2010-01-01

textabstractBackground. Worked examples are very effective for novice learners. They typically present a written-out ideal (didactical) solution for learners to study. nAims. This study used worked examples of patient history taking in physiotherapy that presented a non-didactical solution (i.e.,

FLIPPED LEARNING: PRACTICAL ASPECTS

Directory of Open Access Journals (Sweden)

Olena Kuzminska

2016-03-01

Full Text Available The article is devoted to issues of implementation of the flipped learning technology in the practice of higher education institutions. The article defines the principles of technology and a model of the educational process, it notes the need to establish an information support system. The article defines online platforms and resources; it describes recommendations for the design of electronic training courses and organization of the students in the process of implementing the proposed model, as well as tools for assessing its effectiveness. The article provides a description of flipped learning implementation scenario and formulates suggestions regarding the use of this model as a mechanism to improve the efficiency of the learning process in the ICT-rich environment of high school: use of learning management systems (LMS and personal learning environments (PLE of participants in a learning process. The article provides an example of implementation of the flipped learning model as a part of the Information Technologies course in the National University of Life and Environmental Sciences of Ukraine (NULES. The article gives examples of tasks, resources and services, results of students’ research activity, as well as an example of the personal learning network, established in the course of implementation of the flipped learning model and elements of digital student portfolios. It presents the results of the monitoring of learning activities and students’ feedback. The author describes cautions against the mass introduction of the flipped learning model without monitoring of readiness of the participants of the educational process for its implementation

LEARNING TOOLS INTEROPERABILITY – A NEW STANDARD FOR INTEGRATION OF DISTANCE LEARNING PLATFORMS

Directory of Open Access Journals (Sweden)

Oleksandr A. Shcherbyna

2015-06-01

Full Text Available For information technology in education there is always an issue of re-usage of electronic educational resources, their transferring possibility from one virtual learning environment to another. Previously, standardized sets of files were used to serve this purpose, for example, SCORM-packages. In this article the new standard Learning Tools Interoperability (LTI is reviewed, which allows users from one environment to access resources from another environment. This makes it possible to integrate them into a single distributed learning environment that is created and shared. The article gives examples of the practical use of standard LTI in Moodle learning management system using External tool and LTI provider plugins.

Cognitive Bias in Systems Verification

Science.gov (United States)

Larson, Steve

2012-01-01

Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

Biases in categorization

NARCIS (Netherlands)

Das-Smaal, E.A.

1990-01-01

On what grounds can we conclude that an act of categorization is biased? In this chapter, it is contended that in the absence of objective norms of what categories actually are, biases in categorization can only be specified in relation to theoretical understandings of categorization. Therefore, the

EFFECTS OF BIASES IN VIRIAL MASS ESTIMATION ON COSMIC SYNCHRONIZATION OF QUASAR ACCRETION

International Nuclear Information System (INIS)

Steinhardt, Charles L.

2011-01-01

Recent work using virial mass estimates and the quasar mass-luminosity plane has yielded several new puzzles regarding quasar accretion, including a sub-Eddington boundary (SEB) on most quasar accretion, near-independence of the accretion rate from properties of the host galaxy, and a cosmic synchronization of accretion among black holes of a common mass. We consider how these puzzles might change if virial mass estimation turns out to have a systematic bias. As examples, we consider two recent claims of mass-dependent biases in Mg II masses. Under any such correction, the surprising cosmic synchronization of quasar accretion rates and independence from the host galaxy remain. The slope and location of the SEB are very sensitive to biases in virial mass estimation, and various mass calibrations appear to favor different possible physical explanations for feedback between the central black hole and its environment. The alternative mass estimators considered do not simply remove puzzling quasar behavior, but rather replace it with new puzzles that may be more difficult to solve than those using current virial mass estimators and the Shen et al. catalog.

A study of total measurement error in tomographic gamma scanning to assay nuclear material with emphasis on a bias issue for low-activity samples

International Nuclear Information System (INIS)

Burr, T.L.; Mercer, D.J.; Prettyman, T.H.

1998-01-01

Field experience with the tomographic gamma scanner to assay nuclear material suggests that the analysis techniques can significantly impact the assay uncertainty. For example, currently implemented image reconstruction methods exhibit a positive bias for low-activity samples. Preliminary studies indicate that bias reduction could be achieved at the expense of increased random error variance. In this paper, the authors examine three possible bias sources: (1) measurement error in the estimated transmission matrix, (2) the positivity constraint on the estimated mass of nuclear material, and (3) improper treatment of the measurement error structure. The authors present results from many small-scale simulation studies to examine this bias/variance tradeoff for a few image reconstruction methods in the presence of the three possible bias sources

Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences.

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David Herrera

Full Text Available In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS. When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'. As an alternative, a 'Gellerman-like training schedule' (GLS can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide

Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences.

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Herrera, David; Treviño, Mario

2015-01-01

In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS). When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'). As an alternative, a 'Gellerman-like training schedule' (GLS) can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide the selection of

Pharmacogenomics Bias - Systematic distortion of study results by genetic heterogeneity

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Zietemann, Vera

2008-04-01

trial. Results: We found four studies that systematically evaluated heterogeneity bias. All of them indicated that there is a potential of heterogeneity bias. However, none of these studies explicitly investigated the effect of genetic heterogeneity. Therefore, we performed our own simulation study. Our generic simulation showed that a purely HT-related bias is negative (conservative and a purely HP-related bias is positive (liberal. For many typical scenarios, the absolute bias is smaller than 10%. In case of joint HP and HT, the overall bias is likely triggered by the HP component and reaches positive values >100% if fractions of „fast progressors" and „strong treatment responders" are low. In the clinical example with pravastatin therapy, the unadjusted model overestimated the true life-years gained (LYG by 5.5% (1.07 LYG vs. 0.99 LYG for 56-year-old men. Conclusions: We have been able to predict the pharmacogenomics bias jointly caused by heterogeneity in progression of disease and heterogeneity in treatment response as a function of characteristics of patients, chronic disease, and treatment. In the case of joint presence of both types of heterogeneity, models ignoring this heterogeneity may generate results that overestimate the treatment benefit.

Integrating Worked Examples into Problem Posing in a Web-Based Learning Environment

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Hsiao, Ju-Yuan; Hung, Chun-Ling; Lan, Yu-Feng; Jeng, Yoau-Chau

2013-01-01

Most students always lack of experience and perceive difficult regarding problem posing. The study hypothesized that worked examples may have benefits for supporting students' problem posing activities. A quasi-experiment was conducted in the context of a business mathematics course for examining the effects of integrating worked examples into…

Cognitive bias measurement and social anxiety disorder: Correlating self-report data and attentional bias

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Alexander Miloff

2015-09-01

Full Text Available Social anxiety disorder (SAD and attentional bias are theoretically connected in cognitive behavioral therapeutic models. In fact, there is an emerging field focusing on modifying attentional bias as a stand-alone treatment. However, it is unclear to what degree these attentional biases are present before commencing treatment. The purpose of this study was to measure pre-treatment attentional bias in 153 participants diagnosed with SAD using a home-based Internet version of the dot-probe paradigm. Results showed no significant correlation for attentional bias (towards or away from negative words or faces and the self-rated version of the Liebowitz Social Anxiety Scale (LSAS-SR. However, two positive correlations were found for the secondary measures Generalized Anxiety Disorder 7 (GAD-7 and Patient Health Questionnaire 9 (PHQ-9. These indicated that those with elevated levels of anxiety and depression had a higher bias towards negative faces in neutral–negative and positive–negative valence combinations, respectively. The unreliability of the dot-probe paradigm and home-based Internet delivery are discussed to explain the lack of correlations between LSAS-SR and attentional bias. Changes to the dot-probe task are suggested that could improve reliability.

Learning with hierarchical-deep models.

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Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

2013-08-01

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

Suspected survivor bias in case-control studies: stratify on survival time and use a negative control.

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van Rein, Nienke; Cannegieter, Suzanne C; Rosendaal, Frits R; Reitsma, Pieter H; Lijfering, Willem M

2014-02-01

Selection bias in case-control studies occurs when control selection is inappropriate. However, selection bias due to improper case sampling is less well recognized. We describe how to recognize survivor bias (i.e., selection on exposed cases) and illustrate this with an example study. A case-control study was used to analyze the effect of statins on major bleedings during treatment with vitamin K antagonists. A total of 110 patients who experienced such bleedings were included 18-1,018 days after the bleeding complication and matched to 220 controls. A protective association of major bleeding for exposure to statins (odds ratio [OR]: 0.56; 95% confidence interval: 0.29-1.08) was found, which did not become stronger after adjustment for confounding factors. These observations lead us to suspect survivor bias. To identify this bias, results were stratified on time between bleeding event and inclusion, and repeated for a negative control (an exposure not related to survival): blood group non-O. The ORs for exposure to statins increased gradually to 1.37 with shorter time between outcome and inclusion, whereas ORs for the negative control remained constant, confirming our hypothesis. We recommend the presented method to check for overoptimistic results, that is, survivor bias in case-control studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Example-Based Learning: Effects of Model Expertise in Relation to Student Expertise

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Boekhout, Paul; van Gog, Tamara; van de Wiel, Margje W. J.; Gerards-Last, Dorien; Geraets, Jacques

2010-01-01

Background: Worked examples are very effective for novice learners. They typically present a written-out ideal (didactical) solution for learners to study. Aims: This study used worked examples of patient history taking in physiotherapy that presented a "non"-didactical solution (i.e., based on actual performance). The effects of model expertise…

Sources of Response Bias in Older Ethnic Minorities: A Case of Korean American Elderly

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Kim, Miyong T.; Ko, Jisook; Yoon, Hyunwoo; Kim, Kim B.; Jang, Yuri

2015-01-01

The present study was undertaken to investigate potential sources of response bias in empirical research involving older ethnic minorities and to identify prudent strategies to reduce those biases, using Korean American elderly (KAE) as an example. Data were obtained from three independent studies of KAE (N=1,297; age ≥60) in three states (Florida, New York, and Maryland) from 2000 to 2008. Two common measures, Pearlin’s Mastery Scale and the CES-D scale, were selected for a series of psychometric tests based on classical measurement theory. Survey items were analyzed in depth, using psychometric properties generated from both exploratory factor analysis and confirmatory factor analysis as well as correlational analysis. Two types of potential sources of bias were identified as the most significant contributors to increases in error variances for these psychological instruments. Error variances were most prominent when (1) items were not presented in a manner that was culturally or contextually congruent with respect to the target population and/or (2) the response anchors for items were mixed (e.g., positive vs. negative). The systemic patterns and magnitudes of the biases were also cross-validated for the three studies. The results demonstrate sources and impacts of measurement biases in studies of older ethnic minorities. The identified response biases highlight the need for re-evaluation of current measurement practices, which are based on traditional recommendations that response anchors should be mixed or that the original wording of instruments should be rigidly followed. Specifically, systematic guidelines for accommodating cultural and contextual backgrounds into instrument design are warranted. PMID:26049971

Threat bias, not negativity bias, underpins differences in political ideology.

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Lilienfeld, Scott O; Latzman, Robert D

2014-06-01

Although disparities in political ideology are rooted partly in dispositional differences, Hibbing et al.'s analysis paints with an overly broad brush. Research on the personality correlates of liberal-conservative differences points not to global differences in negativity bias, but to differences in threat bias, probably emanating from differences in fearfulness. This distinction bears implications for etiological research and persuasion efforts.

Fair Play: A Study of Scientific Workforce Trainers’ Experience Playing an Educational Video Game about Racial Bias

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Kaatz, Anna; Carnes, Molly; Gutierrez, Belinda; Savoy, Julia; Samuel, Clem; Filut, Amarette; Pribbenow, Christine Maidl

2017-01-01

Explicit racial bias has decreased in the United States, but racial stereotypes still exist and conspire in multiple ways to perpetuate the underparticipation of Blacks in science careers. Capitalizing on the potential effectiveness of role-playing video games to promote the type of active learning required to increase awareness of and reduce subtle racial bias, we developed the video game Fair Play, in which players take on the role of Jamal, a Black male graduate student in science, who experiences discrimination in his PhD program. We describe a mixed-methods evaluation of the experience of scientific workforce trainers who played Fair Play at the National Institutes of Health Division of Training Workforce Development and Diversity program directors’ meeting in 2013 (n = 47; 76% female, n = 34; 53% nonwhite, n = 26). The evaluation findings suggest that Fair Play can promote perspective taking and increase bias literacy, which are steps toward reducing racial bias and affording Blacks equal opportunities to excel in science. PMID:28450447

Learning to learn: self-managed learning

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Jesús Miranda Izquierdo

2006-09-01

Full Text Available Thi is article analyzes the potentialities and weaknesses that non directive Pedagogy presents, an example of the so called self managed pedagogy, whose postulates are good to analyze for the contributions that this position can make to the search of new ways of learning.

Social incentives improve deliberative but not procedural learning in older adults.

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Gorlick, Marissa A; Maddox, W Todd

2015-01-01

Age-related deficits are seen across tasks where learning depends on asocial feedback processing, however plasticity has been observed in some of the same tasks in social contexts suggesting a novel way to attenuate deficits. Socioemotional selectivity theory suggests this plasticity is due to a deliberative motivational shift toward achieving well-being with age (positivity effect) that reverses when executive processes are limited (negativity effect). The present study examined the interaction of feedback valence (positive, negative) and social salience (emotional face feedback - happy; angry, asocial point feedback - gain; loss) on learning in a deliberative task that challenges executive processes and a procedural task that does not. We predict that angry face feedback will improve learning in a deliberative task when executive function is challenged. We tested two competing hypotheses regarding the interactive effects of deliberative emotional biases on automatic feedback processing: (1) If deliberative emotion regulation and automatic feedback are interactive we expect happy face feedback to improve learning and angry face feedback to impair learning in older adults because cognitive control is available. (2) If deliberative emotion regulation and automatic feedback are not interactive we predict that emotional face feedback will not improve procedural learning regardless of valence. Results demonstrate that older adults show persistent deficits relative to younger adults during procedural category learning suggesting that deliberative emotional biases do not interact with automatic feedback processing. Interestingly, a subgroup of older adults identified as potentially using deliberative strategies tended to learn as well as younger adults with angry relative to happy feedback, matching the pattern observed in the deliberative task. Results suggest that deliberative emotional biases can improve deliberative learning, but have no effect on procedural learning.

Semi-supervised learning and domain adaptation in natural language processing