Neural computations mediating one-shot learning in the human brain.

PubMed

Lee, Sang Wan; O'Doherty, John P; Shimojo, Shinsuke

2015-04-01

Incremental learning, in which new knowledge is acquired gradually through trial and error, can be distinguished from one-shot learning, in which the brain learns rapidly from only a single pairing of a stimulus and a consequence. Very little is known about how the brain transitions between these two fundamentally different forms of learning. Here we test a computational hypothesis that uncertainty about the causal relationship between a stimulus and an outcome induces rapid changes in the rate of learning, which in turn mediates the transition between incremental and one-shot learning. By using a novel behavioral task in combination with functional magnetic resonance imaging (fMRI) data from human volunteers, we found evidence implicating the ventrolateral prefrontal cortex and hippocampus in this process. The hippocampus was selectively "switched" on when one-shot learning was predicted to occur, while the ventrolateral prefrontal cortex was found to encode uncertainty about the causal association, exhibiting increased coupling with the hippocampus for high-learning rates, suggesting this region may act as a "switch," turning on and off one-shot learning as required.

Role of dopamine D2 receptors in human reinforcement learning.

PubMed

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-09-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well.

Role of Dopamine D2 Receptors in Human Reinforcement Learning

PubMed Central

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-01-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well. PMID:24713613

Inductive reasoning about causally transmitted properties.

PubMed

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

2008-11-01

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

Cue competition effects in human causal learning.

PubMed

Vogel, Edgar H; Glynn, Jacqueline Y; Wagner, Allan R

2015-01-01

Five experiments involving human causal learning were conducted to compare the cue competition effects known as blocking and unovershadowing, in proactive and retroactive instantiations. Experiment 1 demonstrated reliable proactive blocking and unovershadowing but only retroactive unovershadowing. Experiment 2 replicated the same pattern and showed that the retroactive unovershadowing that was observed was interfered with by a secondary memory task that had no demonstrable effect on either proactive unovershadowing or blocking. Experiments 3a, 3b, and 3c demonstrated that retroactive unovershadowing was accompanied by an inflated memory effect not accompanying proactive unovershadowing. The differential pattern of proactive versus retroactive cue competition effects is discussed in relationship to amenable associative and inferential processing possibilities.

Causal cognition in human and nonhuman animals: a comparative, critical review.

PubMed

Penn, Derek C; Povinelli, Daniel J

2007-01-01

In this article, we review some of the most provocative experimental results to have emerged from comparative labs in the past few years, starting with research focusing on contingency learning and finishing with experiments exploring nonhuman animals' understanding of causal-logical relations. Although the theoretical explanation for these results is often inchoate, a clear pattern nevertheless emerges. The comparative evidence does not fit comfortably into either the traditional associationist or inferential alternatives that have dominated comparative debate for many decades now. Indeed, the similarities and differences between human and nonhuman causal cognition seem to be much more multifarious than these dichotomous alternatives allow.

A review of human-automation interaction and lessons learned

DOT National Transportation Integrated Search

2006-10-01

This report reviews 37 accidents in aviation, other vehicles, process control and other complex systems where human-automation interaction is involved. Implications about causality with respect to design, procedures, management and training are drawn...

Attention to irrelevant cues is related to positive symptoms in schizophrenia.

PubMed

Morris, Richard; Griffiths, Oren; Le Pelley, Michael E; Weickert, Thomas W

2013-05-01

Many modern learning theories assume that the amount of attention to a cue depends on how well that cue predicted important events in the past. Schizophrenia is associated with deficits in attention and recent theories of psychosis have argued that positive symptoms such as delusions and hallucinations are related to a failure of selective attention. However, evidence demonstrating that attention to irrelevant cues is related to positive symptoms in schizophrenia is lacking. We used a novel method of measuring attention to nonpredictive (and thus irrelevant) cues in a causal learning test (Le Pelley ME, McLaren IP. Learned associability and associative change in human causal learning. Q J Exp Psychol B. 2003;56:68-79) to assess whether healthy adults and people with schizophrenia discriminate previously predictive and nonpredictive cues. In a series of experiments with independent samples, we demonstrated: (1) when people with schizophrenia who had severe positive symptoms successfully distinguished between predictive and nonpredictive cues during training, they failed to discriminate between predictive and nonpredictive cues relative to healthy adults during subsequent testing and (2) learning about nonpredictive cues was correlated with more severe positive symptoms scores in schizophrenia. These results suggest that positive symptoms of schizophrenia are related to increased attention to nonpredictive cues during causal learning. This deficit in selective attention results in learning irrelevant causal associations and may be the basis of positive symptoms in schizophrenia.

Evaluation of near-miss and adverse events in radiation oncology using a comprehensive causal factor taxonomy.

PubMed

Spraker, Matthew B; Fain, Robert; Gopan, Olga; Zeng, Jing; Nyflot, Matthew; Jordan, Loucille; Kane, Gabrielle; Ford, Eric

Incident learning systems (ILSs) are a popular strategy for improving safety in radiation oncology (RO) clinics, but few reports focus on the causes of errors in RO. The goal of this study was to test a causal factor taxonomy developed in 2012 by the American Association of Physicists in Medicine and adopted for use in the RO: Incident Learning System (RO-ILS). Three hundred event reports were randomly selected from an institutional ILS database and Safety in Radiation Oncology (SAFRON), an international ILS. The reports were split into 3 groups of 100 events each: low-risk institutional, high-risk institutional, and SAFRON. Three raters retrospectively analyzed each event for contributing factors using the American Association of Physicists in Medicine taxonomy. No events were described by a single causal factor (median, 7). The causal factor taxonomy was found to be applicable for all events, but 4 causal factors were not described in the taxonomy: linear accelerator failure (n = 3), hardware/equipment failure (n = 2), failure to follow through with a quality improvement intervention (n = 1), and workflow documentation was misleading (n = 1). The most common causal factor categories contributing to events were similar in all event types. The most common specific causal factor to contribute to events was a "slip causing physical error." Poor human factors engineering was the only causal factor found to contribute more frequently to high-risk institutional versus low-risk institutional events. The taxonomy in the study was found to be applicable for all events and may be useful in root cause analyses and future studies. Communication and human behaviors were the most common errors affecting all types of events. Poor human factors engineering was found to specifically contribute to high-risk more than low-risk institutional events, and may represent a strategy for reducing errors in all types of events. Copyright © 2017 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Learning to make things happen: Infants' observational learning of social and physical causal events.

PubMed

Waismeyer, Anna; Meltzoff, Andrew N

2017-10-01

Infants learn about cause and effect through hands-on experience; however, they also can learn about causality simply from observation. Such observational causal learning is a central mechanism by which infants learn from and about other people. Across three experiments, we tested infants' observational causal learning of both social and physical causal events. Experiment 1 assessed infants' learning of a physical event in the absence of visible spatial contact between the causes and effects. Experiment 2 developed a novel paradigm to assess whether infants could learn about a social causal event from third-party observation of a social interaction between two people. Experiment 3 compared learning of physical and social events when the outcomes occurred probabilistically (happening some, but not all, of the time). Infants demonstrated significant learning in all three experiments, although learning about probabilistic cause-effect relations was most difficult. These findings about infant observational causal learning have implications for children's rapid nonverbal learning about people, things, and their causal relations. Copyright © 2017 Elsevier Inc. All rights reserved.

Backward Blocking and Interference between Cues Are Empirically Equivalent in Non-Causally Framed Learning Tasks

ERIC Educational Resources Information Center

Luque, David; Moris, Joaquin; Orgaz, Cristina; Cobos, Pedro L.; Matute, Helena

2011-01-01

Backward blocking (BB) and interference between cues (IbC) are cue competition effects produced by very similar manipulations. In a standard BB design, both effects might occur simultaneously, which implies a potential problem for studying BB. In the present study with humans, the magnitude of both effects was compared using a non-causal scenario…

Category transfer in sequential causal learning: the unbroken mechanism hypothesis.

PubMed

Hagmayer, York; Meder, Björn; von Sydow, Momme; Waldmann, Michael R

2011-07-01

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis. Copyright © 2011 Cognitive Science Society, Inc.

Theory-based Bayesian models of inductive learning and reasoning.

PubMed

Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles

2006-07-01

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Inferences about unobserved causes in human contingency learning.

PubMed

Hagmayer, York; Waldmann, Michael R

2007-03-01

Estimates of the causal efficacy of an event need to take into account the possible presence and influence of other unobserved causes that might have contributed to the occurrence of the effect. Current theoretical approaches deal differently with this problem. Associative theories assume that at least one unobserved cause is always present. In contrast, causal Bayes net theories (including Power PC theory) hypothesize that unobserved causes may be present or absent. These theories generally assume independence of different causes of the same event, which greatly simplifies modelling learning and inference. In two experiments participants were requested to learn about the causal relation between a single cause and an effect by observing their co-occurrence (Experiment 1) or by actively intervening in the cause (Experiment 2). Participants' assumptions about the presence of an unobserved cause were assessed either after each learning trial or at the end of the learning phase. The results show an interesting dissociation. Whereas there was a tendency to assume interdependence of the causes in the online judgements during learning, the final judgements tended to be more in the direction of an independence assumption. Possible explanations and implications of these findings are discussed.

Causal learning is collaborative: Examining explanation and exploration in social contexts.

PubMed

Legare, Cristine H; Sobel, David M; Callanan, Maureen

2017-10-01

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children's explanation-seeking behaviors in the form of "why" questions. Second, we examine parents' elaboration of meaning about causal relations. Finally, we consider parents' interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children's causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.

Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

PubMed Central

Weisswange, Thomas H.; Rothkopf, Constantin A.; Rodemann, Tobias; Triesch, Jochen

2011-01-01

Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference. PMID:21750717

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Learning a theory of causality.

PubMed

Goodman, Noah D; Ullman, Tomer D; Tenenbaum, Joshua B

2011-01-01

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.

A Theory of Causal Learning in Children: Causal Maps and Bayes Nets

ERIC Educational Resources Information Center

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

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously…

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

ERIC Educational Resources Information Center

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

2016-01-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about…

Cerebral Dominance: Its Use in Understanding Learning Styles and Behavioral Patterns.

ERIC Educational Resources Information Center

Matthews, Doris B.

Studies of the human brain suggest that cerebral dominance, the preference for either the right or the left hemisphere to direct the body's behavior, plays a causal role in distinctive learning styles and behavior patterns. The two halves of the brain are physically almost identical at birth, but childhood experiences which utilize one hemisphere…

Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning

PubMed Central

Wise, Richard J.S.; Geranmayeh, Fatemeh; Hampshire, Adam

2017-01-01

It is well established that networks within multiple-demand cortex (MDC) become active when diverse skills and behaviors are being learnt. However, their causal role in learning remains to be established. In the present study, we first performed functional magnetic resonance imaging on healthy female and male human participants to confirm that MDC was most active in the initial stages of learning a novel vocabulary, consisting of pronounceable nonwords (pseudowords), each associated with a picture of a real object. We then examined, in healthy female and male human participants, whether repetitive transcranial magnetic stimulation of a frontal midline node of the cingulo-opercular MDC affected learning rates specifically during the initial stages of learning. We report that stimulation of this node, but not a control brain region, substantially improved both accuracy and response times during the earliest stage of learning pseudoword–object associations. This stimulation had no effect on the processing of established vocabulary, tested by the accuracy and response times when participants decided whether a real word was accurately paired with a picture of an object. These results provide evidence that noninvasive stimulation to MDC nodes can enhance learning rates, thereby demonstrating their causal role in the learning process. We propose that this causal role makes MDC candidate target for experimental therapeutics; for example, in stroke patients with aphasia attempting to reacquire a vocabulary. SIGNIFICANCE STATEMENT Learning a task involves the brain system within which that specific task becomes established. Therefore, successfully learning a new vocabulary establishes the novel words in the language system. However, there is evidence that in the early stages of learning, networks within multiple-demand cortex (MDC), which control higher cognitive functions, such as working memory, attention, and monitoring of performance, become active. This activity declines once the task is learnt. The present study demonstrated that a node within MDC, located in midline frontal cortex, becomes active during the early stage of learning a novel vocabulary. Importantly, noninvasive brain stimulation of this node improved performance during this stage of learning. This observation demonstrated that MDC activity is important for learning. PMID:28676576

A developmental approach to learning causal models for cyber security

NASA Astrophysics Data System (ADS)

Mugan, Jonathan

2013-05-01

To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

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

PubMed

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.

Embodied experiences for science learning: A cognitive linguistics exploration of middle school students' language in learning about water

NASA Astrophysics Data System (ADS)

Salinas Barrios, Ivan Eduardo

I investigated linguistic patterns in middle school students' writing to understand their relevant embodied experiences for learning science. Embodied experiences are those limited by the perceptual and motor constraints of the human body. Recent research indicates student understanding of science needs embodied experiences. Recent emphases of science education researchers in the practices of science suggest that students' understanding of systems and their structure, scale, size, representations, and causality are crosscutting concepts that unify all scientific disciplinary areas. To discern the relationship between linguistic patterns and embodied experiences, I relied on Cognitive Linguistics, a field within cognitive sciences that pays attention to language organization and use assuming that language reflects the human cognitive system. Particularly, I investigated the embodied experiences that 268 middle school students learning about water brought to understanding: i) systems and system structure; ii) scale, size and representations; and iii) causality. Using content analysis, I explored students' language in search of patterns regarding linguistic phenomena described within cognitive linguistics: image schemas, conceptual metaphors, event schemas, semantical roles, and force-dynamics. I found several common embodied experiences organizing students' understanding of crosscutting concepts. Perception of boundaries and change in location and perception of spatial organization in the vertical axis are relevant embodied experiences for students' understanding of systems and system structure. Direct object manipulation and perception of size with and without locomotion are relevant for understanding scale, size and representations. Direct applications of force and consequential perception of movement or change in form are relevant for understanding of causality. I discuss implications of these findings for research and science teaching.

Non-Gaussian Methods for Causal Structure Learning.

PubMed

Shimizu, Shohei

2018-05-22

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

Causal Learning with Local Computations

ERIC Educational Resources Information Center

Fernbach, Philip M.; Sloman, Steven A.

2009-01-01

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require…

Causal network in a deafferented non-human primate brain.

PubMed

Balasubramanian, Karthikeyan; Takahashi, Kazutaka; Hatsopoulos, Nicholas G

2015-01-01

De-afferented/efferented neural ensembles can undergo causal changes when interfaced to neuroprosthetic devices. These changes occur via recruitment or isolation of neurons, alterations in functional connectivity within the ensemble and/or changes in the role of neurons, i.e., excitatory/inhibitory. In this work, emergence of a causal network and changes in the dynamics are demonstrated for a deafferented brain region exposed to BMI (brain-machine interface) learning. The BMI was controlling a robot for reach-and-grasp behavior. And, the motor cortical regions used for the BMI were deafferented due to chronic amputation, and ensembles of neurons were decoded for velocity control of the multi-DOF robot. A generalized linear model-framework based Granger causality (GLM-GC) technique was used in estimating the ensemble connectivity. Model selection was based on the AIC (Akaike Information Criterion).

Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

Air Force Research Laboratory Warfighter Readiness Research Division Participation in the 2008 IITSEC

DTIC Science & Technology

2008-12-15

of the underlying behaviors that led to each element being cited. The AFSC Human Factors Database listed all human factors cited in the Life...situations of increased pressure. Through an understanding of the causal factors of human behavior , and by analysis of one’s own behavioral patterns...JTAC training and overall lessons learned from modeling and simulation of the JTAC environment to include behavior scripting, artillery models

Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory

ERIC Educational Resources Information Center

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…

Medial-Frontal Stimulation Enhances Learning in Schizophrenia by Restoring Prediction Error Signaling.

PubMed

Reinhart, Robert M G; Zhu, Julia; Park, Sohee; Woodman, Geoffrey F

2015-09-02

Posterror learning, associated with medial-frontal cortical recruitment in healthy subjects, is compromised in neuropsychiatric disorders. Here we report novel evidence for the mechanisms underlying learning dysfunctions in schizophrenia. We show that, by noninvasively passing direct current through human medial-frontal cortex, we could enhance the event-related potential related to learning from mistakes (i.e., the error-related negativity), a putative index of prediction error signaling in the brain. Following this causal manipulation of brain activity, the patients learned a new task at a rate that was indistinguishable from healthy individuals. Moreover, the severity of delusions interacted with the efficacy of the stimulation to improve learning. Our results demonstrate a causal link between disrupted prediction error signaling and inefficient learning in schizophrenia. These findings also demonstrate the feasibility of nonpharmacological interventions to address cognitive deficits in neuropsychiatric disorders. When there is a difference between what we expect to happen and what we actually experience, our brains generate a prediction error signal, so that we can map stimuli to responses and predict outcomes accurately. Theories of schizophrenia implicate abnormal prediction error signaling in the cognitive deficits of the disorder. Here, we combine noninvasive brain stimulation with large-scale electrophysiological recordings to establish a causal link between faulty prediction error signaling and learning deficits in schizophrenia. We show that it is possible to improve learning rate, as well as the neural signature of prediction error signaling, in patients to a level quantitatively indistinguishable from that of healthy subjects. The results provide mechanistic insight into schizophrenia pathophysiology and suggest a future therapy for this condition. Copyright © 2015 the authors 0270-6474/15/3512232-09$15.00/0.

Cumulative cultural learning: Development and diversity

PubMed Central

2017-01-01

The complexity and variability of human culture is unmatched by any other species. Humans live in culturally constructed niches filled with artifacts, skills, beliefs, and practices that have been inherited, accumulated, and modified over generations. A causal account of the complexity of human culture must explain its distinguishing characteristics: It is cumulative and highly variable within and across populations. I propose that the psychological adaptations supporting cumulative cultural transmission are universal but are sufficiently flexible to support the acquisition of highly variable behavioral repertoires. This paper describes variation in the transmission practices (teaching) and acquisition strategies (imitation) that support cumulative cultural learning in childhood. Examining flexibility and variation in caregiver socialization and children’s learning extends our understanding of evolution in living systems by providing insight into the psychological foundations of cumulative cultural transmission—the cornerstone of human cultural diversity. PMID:28739945

Cumulative cultural learning: Development and diversity.

PubMed

Legare, Cristine H

2017-07-24

The complexity and variability of human culture is unmatched by any other species. Humans live in culturally constructed niches filled with artifacts, skills, beliefs, and practices that have been inherited, accumulated, and modified over generations. A causal account of the complexity of human culture must explain its distinguishing characteristics: It is cumulative and highly variable within and across populations. I propose that the psychological adaptations supporting cumulative cultural transmission are universal but are sufficiently flexible to support the acquisition of highly variable behavioral repertoires. This paper describes variation in the transmission practices (teaching) and acquisition strategies (imitation) that support cumulative cultural learning in childhood. Examining flexibility and variation in caregiver socialization and children's learning extends our understanding of evolution in living systems by providing insight into the psychological foundations of cumulative cultural transmission-the cornerstone of human cultural diversity.

Synthesizing animal and human behavior research via neural network learning theory.

PubMed

Tryon, W W

1995-12-01

Animal and human research have been "divorced" since approximately 1968. Several recent articles have tried to persuade behavior therapists of the merits of animal research. Three reasons are given concerning why disinterest in animal research is so widespread: (1) functional explanations are given for animals, and cognitive explanations are given for humans; (2) serial symbol manipulating models are used to explain human behavior; and (3) human learning was assumed, thereby removing it as something to be explained. Brain-inspired connectionist neural networks, collectively referred to as neural network learning theory (NNLT), are briefly described, and a spectrum of their accomplishments from simple conditioning through speech is outlined. Five benefits that behavior therapists can derive from NNLT are described. They include (a) enhanced professional identity derived from a comprehensive learning theory, (b) improved interdisciplinary collaboration both clinically and scientifically, (c) renewed perceived relevance of animal research, (d) access to plausible proximal causal mechanisms capable of explaining operant conditioning, and (e) an inherently developmental perspective.

A self-agency bias in preschoolers' causal inferences

PubMed Central

Kushnir, Tamar; Wellman, Henry M.; Gelman, Susan A.

2013-01-01

Preschoolers' causal learning from intentional actions – causal interventions – is subject to a self-agency bias. We propose that this bias is evidence-based; it is responsive to causal uncertainty. In the current studies, two causes (one child-controlled, one experimenter-controlled) were associated with one or two effects, first independently, then simultaneously. When initial independent effects were probabilistic, and thus subsequent simultaneous actions were causally ambiguous, children showed a self-agency bias. Children showed no bias when initial effects were deterministic. Further controls establish that children's self-agency bias is not a wholesale preference but rather is influenced by uncertainty in causal evidence. These results demonstrate that children's own experience of action influences their causal learning, and suggest possible benefits in uncertain and ambiguous everyday learning contexts. PMID:19271843

Context and Time in Causal Learning: Contingency and Mood Dependent Effects

PubMed Central

Msetfi, Rachel M.; Wade, Caroline; Murphy, Robin A.

2013-01-01

Defining cues for instrumental causality are the temporal, spatial and contingency relationships between actions and their effects. In this study, we carried out a series of causal learning experiments that systematically manipulated time and context in positive and negative contingency conditions. In addition, we tested participants categorized as non-dysphoric and mildly dysphoric because depressed mood has been shown to affect the processing of all these causal cues. Findings showed that causal judgements made by non-dysphoric participants were contextualized at baseline and were affected by the temporal spacing of actions and effects only with generative, but not preventative, contingency relationships. Participants categorized as dysphoric made less contextualized causal ratings at baseline but were more sensitive than others to temporal manipulations across the contingencies. These effects were consistent with depression affecting causal learning through the effects of slowed time experience on accrued exposure to the context in which causal events took place. Taken together, these findings are consistent with associative approaches to causal judgement. PMID:23691147

Conservative Forgetful Scholars: How People Learn Causal Structure through Sequences of Interventions

ERIC Educational Resources Information Center

Bramley, Neil R.; Lagnado, David A.; Speekenbrink, Maarten

2015-01-01

Interacting with a system is key to uncovering its causal structure. A computational framework for interventional causal learning has been developed over the last decade, but how real causal learners might achieve or approximate the computations entailed by this framework is still poorly understood. Here we describe an interactive computer task in…

Beyond Markov: Accounting for independence violations in causal reasoning.

PubMed

Rehder, Bob

2018-06-01

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

Aging and Retrospective Revaluation of Causal Learning

PubMed Central

Mutter, Sharon A.; Atchley, Anthony R.; Plumlee, Leslie M.

2011-01-01

In a two-stage causal learning task, young and older participants first learned which foods presented in compound were followed by an allergic reaction (e.g., STEAK - BEANS → REACTION) and then the causal efficacy of one food from these compounds was revalued (e.g., BEANS → NO REACTION). In Experiment 1, unrelated food pairs were used and although there were no age differences in compound or single cue – outcome learning, older adults did not retrospectively revalue the causal efficacy of the absent target cues (e.g. STEAK). However, they had weaker within – compound associations for the unrelated foods and this may have prevented them from retrieving the representations of these cues. In Experiment 2, older adults still showed no retrospective revaluation of absent cues even though compound food cues with pre-existing associations were used (e.g., STEAK - POTATO) and they received additional learning trials. Finally, in Experiment 3, older adults revalued the causal efficacy of the target cues when small, unobtrusive icons of these cues were present during single cue revaluation. These findings suggest that age – related deficits in causal learning for absent cues are due to ineffective associative binding and reactivation processes. PMID:21843025

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

PubMed Central

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

2016-01-01

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

Causal inference in economics and marketing.

PubMed

Varian, Hal R

2016-07-05

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

Causal inference in economics and marketing

PubMed Central

Varian, Hal R.

2016-01-01

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

Functional brain networks and white matter underlying theory-of-mind in autism.

PubMed

Kana, Rajesh K; Libero, Lauren E; Hu, Christi P; Deshpande, Hrishikesh D; Colburn, Jeffrey S

2014-01-01

Human beings constantly engage in attributing causal explanations to one's own and to others' actions, and theory-of-mind (ToM) is critical in making such inferences. Although children learn causal attribution early in development, children with autism spectrum disorders (ASDs) are known to have impairments in the development of intentional causality. This functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) study investigated the neural correlates of physical and intentional causal attribution in people with ASDs. In the fMRI scanner, 15 adolescents and adults with ASDs and 15 age- and IQ-matched typically developing peers made causal judgments about comic strips presented randomly in an event-related design. All participants showed robust activation in bilateral posterior superior temporal sulcus at the temporo-parietal junction (TPJ) in response to intentional causality. Participants with ASDs showed lower activation in TPJ, right inferior frontal gyrus and left premotor cortex. Significantly weaker functional connectivity was also found in the ASD group between TPJ and motor areas during intentional causality. DTI data revealed significantly reduced fractional anisotropy in ASD participants in white matter underlying the temporal lobe. In addition to underscoring the role of TPJ in ToM, this study found an interaction between motor simulation and mentalizing systems in intentional causal attribution and its possible discord in autism.

Causal learning with local computations.

PubMed

Fernbach, Philip M; Sloman, Steven A

2009-05-01

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure. Copyright 2009 APA, all rights reserved.

Verification of causal influences of reasoning skills and epistemology on physics conceptual learning

NASA Astrophysics Data System (ADS)

Ding, Lin

2014-12-01

This study seeks to test the causal influences of reasoning skills and epistemologies on student conceptual learning in physics. A causal model, integrating multiple variables that were investigated separately in the prior literature, is proposed and tested through path analysis. These variables include student preinstructional reasoning skills measured by the Classroom Test of Scientific Reasoning, pre- and postepistemological views measured by the Colorado Learning Attitudes about Science Survey, and pre- and postperformance on Newtonian concepts measured by the Force Concept Inventory. Students from a traditionally taught calculus-based introductory mechanics course at a research university participated in the study. Results largely support the postulated causal model and reveal strong influences of reasoning skills and preinstructional epistemology on student conceptual learning gains. Interestingly enough, postinstructional epistemology does not appear to have a significant influence on student learning gains. Moreover, pre- and postinstructional epistemology, although barely different from each other on average, have little causal connection between them.

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

PubMed

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

Social Learning in the Real-World: 'Over-Imitation' Occurs in Both Children and Adults Unaware of Participation in an Experiment and Independently of Social Interaction.

PubMed

Whiten, Andrew; Allan, Gillian; Devlin, Siobahn; Kseib, Natalie; Raw, Nicola; McGuigan, Nicola

2016-01-01

The current study avoided the typical laboratory context to determine instead whether over-imitation-the disposition to copy even visibly, causally unnecessary actions-occurs in a real-world context in which participants are unaware of being in an experiment. We disguised a puzzle-box task as an interactive item available to the public within a science engagement zone of Edinburgh Zoo. As a member of the public approached, a confederate acting as a zoo visitor retrieved a reward from the box using a sequence of actions containing both causally relevant and irrelevant elements. Despite the absence of intentional demonstration, or social pressure to copy, a majority of both child and even adult observers included all causally irrelevant actions in their reproduction. This occurred even though causal irrelevance appeared manifest because of the transparency of the puzzle-box. That over-imitation occurred so readily in a naturalistic context, devoid of social interaction and pressure, suggests that humans are opportunistic social learners throughout the lifespan, copying the actions of other individuals even when these actions are not intentionally demonstrated, and their causal significance is not readily apparent. The disposition to copy comprehensively, even when a mere onlooker, likely provides humans, irrespective of their age, with a powerful mechanism to extract maximal information from the social environment.

Formalizing Neurath's ship: Approximate algorithms for online causal learning.

PubMed

Bramley, Neil R; Dayan, Peter; Griffiths, Thomas L; Lagnado, David A

2017-04-01

Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Virtual HRD and National Culture: An Information Processing Perspective

ERIC Educational Resources Information Center

Chung, Chih-Hung; Angnakoon, Putthachat; Li, Jessica; Allen, Jeff

2016-01-01

Purpose: The purpose of this study is to provide researchers with a better understanding of the cultural impact on information processing in virtual learning environment. Design/methodology/approach: This study uses a causal loop diagram to depict the cultural impact on information processing in the virtual human resource development (VHRD)…

Young Children Overimitate in Third-Party Contexts

ERIC Educational Resources Information Center

Nielsen, Mark; Moore, Chris; Mohamedally, Jumana

2012-01-01

The exhibition of actions that are causally unnecessary to the outcomes with which they are associated is a core feature of human cultural behavior. To enter into the world(s) of their cultural in-group, children must learn to assimilate such unnecessary actions into their own behavioral repertoire. Past research has established the habitual…

Learning about Life and Death in Early Childhood

ERIC Educational Resources Information Center

Slaughter, Virginia; Lyons, Michelle

2003-01-01

Inagaki and Hatano (2002) have argued that young children initially understand biological phenomena in terms of vitalism, a mode of construal in which "life" or "life-force" is the central causal-explanatory concept. This study investigated the development of vitalistic reasoning in young children's concepts of life, the human body and death.…

Data Do Not Speak for Themselves: The Role of Data in Scientific Controversies

ERIC Educational Resources Information Center

Sadler, Troy D.

2007-01-01

This article presents a learning cycle with the aim of helping students understand the evidentiary basis of scientific claims. Students consider data and interpretations as used to support contradictory views in the debate surrounding the causal relationship between human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS).…

The Formation Experiment in the Age of Hypermedia and Distance Learning

ERIC Educational Resources Information Center

Giest, Hartmut

2004-01-01

Searching for an adequate method to investigate human development (especially the development of theoretical thinking) Vygotsky and his collaborators developed the causal genetic method. The basic idea of this method consists in the investigation of psychic functions and structures by their formation under controlled conditions (for instance via a…

Place-People-Practice-Process: Using Sociomateriality in University Physical Spaces Research

ERIC Educational Resources Information Center

Acton, Renae

2017-01-01

Pedagogy is an inherently spatial practice. Implicit in much of the rhetoric of physical space designed for teaching and learning is an ontological position that assumes material space as distinct from human practice, often conceptualising space as causally (and simplistically) impacting upon people's behaviours. An alternative, and growing,…

Building machines that learn and think like people.

PubMed

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Do New Caledonian crows solve physical problems through causal reasoning?

PubMed Central

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

2008-01-01

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

Verification of Causal Influences of Reasoning Skills and Epistemology on Physics Conceptual Learning

ERIC Educational Resources Information Center

Ding, Lin

2014-01-01

This study seeks to test the causal influences of reasoning skills and epistemologies on student conceptual learning in physics. A causal model, integrating multiple variables that were investigated separately in the prior literature, is proposed and tested through path analysis. These variables include student preinstructional reasoning skills…

Functional Brain Networks and White Matter Underlying Theory-of-Mind in Autism

PubMed Central

Kana, Rajesh K.; Libero, Lauren E.; Hu, Christi P.; Deshpande, Hrishikesh D.; Colburn, Jeffrey S.

2014-01-01

Human beings constantly engage in attributing causal explanations to one’s own and to others’ actions, and theory-of-mind (ToM) is critical in making such inferences. Although children learn causal attribution early in development, children with autism spectrum disorders (ASDs) are known to have impairments in the development of intentional causality. This functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) study investigated the neural correlates of physical and intentional causal attribution in people with ASDs. In the fMRI scanner, 15 adolescents and adults with ASDs and 15 age- and IQ-matched typically developing peers made causal judgments about comic strips presented randomly in an event-related design. All participants showed robust activation in bilateral posterior superior temporal sulcus at the temporo-parietal junction (TPJ) in response to intentional causality. Participants with ASDs showed lower activation in TPJ, right inferior frontal gyrus and left premotor cortex. Significantly weaker functional connectivity was also found in the ASD group between TPJ and motor areas during intentional causality. DTI data revealed significantly reduced fractional anisotropy in ASD participants in white matter underlying the temporal lobe. In addition to underscoring the role of TPJ in ToM, this study found an interaction between motor simulation and mentalizing systems in intentional causal attribution and its possible discord in autism. PMID:22977198

Impairments in action-outcome learning in schizophrenia.

PubMed

Morris, Richard W; Cyrzon, Chad; Green, Melissa J; Le Pelley, Mike E; Balleine, Bernard W

2018-03-03

Learning the causal relation between actions and their outcomes (AO learning) is critical for goal-directed behavior when actions are guided by desire for the outcome. This can be contrasted with habits that are acquired by reinforcement and primed by prevailing stimuli, in which causal learning plays no part. Recently, we demonstrated that goal-directed actions are impaired in schizophrenia; however, whether this deficit exists alongside impairments in habit or reinforcement learning is unknown. The present study distinguished deficits in causal learning from reinforcement learning in schizophrenia. We tested people with schizophrenia (SZ, n = 25) and healthy adults (HA, n = 25) in a vending machine task. Participants learned two action-outcome contingencies (e.g., push left to get a chocolate M&M, push right to get a cracker), and they also learned one contingency was degraded by delivery of noncontingent outcomes (e.g., free M&Ms), as well as changes in value by outcome devaluation. Both groups learned the best action to obtain rewards; however, SZ did not distinguish the more causal action when one AO contingency was degraded. Moreover, action selection in SZ was insensitive to changes in outcome value unless feedback was provided, and this was related to the deficit in AO learning. The failure to encode the causal relation between action and outcome in schizophrenia occurred without any apparent deficit in reinforcement learning. This implies that poor goal-directed behavior in schizophrenia cannot be explained by a more primary deficit in reward learning such as insensitivity to reward value or reward prediction errors.

The influence of social information on children's statistical and causal inferences.

PubMed

Sobel, David M; Kirkham, Natasha Z

2012-01-01

Constructivist accounts of learning posit that causal inference is a child-driven process. Recent interpretations of such accounts also suggest that the process children use for causal learning is rational: Children interpret and learn from new evidence in light of their existing beliefs. We argue that such mechanisms are also driven by informative social cues and suggest ways in which such information influences both preschoolers' and infants' inferences. In doing so, we argue that a rational constructivist account should not only focus on describing the child's internal cognitive mechanisms for learning but also on how social information affects the process of learning.

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

PubMed Central

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

PubMed Central

Lötsch, Jörn; Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred

2017-01-01

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence. PMID:28848388

Structure and Strength in Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2005-01-01

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

Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)

NASA Astrophysics Data System (ADS)

Lozano, A. C.

2010-12-01

Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring relationship) by defining a relational graph in which related locations are connected (note that this relational graph, which represents relationships among the different locations, is distinct from the causal graph, which represents causal relationships among the individual variables - e.g. temperature, pressure- within a multivariate time series). We then define a hidden Markov Random Field (hMRF), assigning a hidden state to each node (location), with the state assignment guided by the prior information encoded in the relational graph. Nodes that share the same state in the hMRF model will have the same causal graph. State assignment can thus shed light on unknown relations among locations (e.g. teleconnection). While the model has been described in terms of hard location partitioning to facilitate its exposition, in fact a soft partitioning is maintained throughout learning. This leads to a form of transfer learning, which makes our model applicable even in situations where partitioning the locations might not seem appropriate. We first validate the effectiveness of our methodology on synthetic datasets, and then apply it to actual climate measurement data. The experimental results show that our approach offers a useful alternative to the simulation-based approach for climate modeling and attribution, and has the capability to provide valuable scientific insights from a new perspective.

Failures of explaining away and screening off in described versus experienced causal learning scenarios.

PubMed

Rehder, Bob; Waldmann, Michael R

2017-02-01

Causal Bayes nets capture many aspects of causal thinking that set them apart from purely associative reasoning. However, some central properties of this normative theory routinely violated. In tasks requiring an understanding of explaining away and screening off, subjects often deviate from these principles and manifest the operation of an associative bias that we refer to as the rich-get-richer principle. This research focuses on these two failures comparing tasks in which causal scenarios are merely described (via verbal statements of the causal relations) versus experienced (via samples of data that manifest the intervariable correlations implied by the causal relations). Our key finding is that we obtained stronger deviations from normative predictions in the described conditions that highlight the instructed causal model compared to those that presented data. This counterintuitive finding indicate that a theory of causal reasoning and learning needs to integrate normative principles with biases people hold about causal relations.

Human Capital Development before Age Five. NBER Working Paper No. 15827

ERIC Educational Resources Information Center

Almond, Douglas; Currie, Janet

2010-01-01

This chapter seeks to set out what Economists have learned about the effects of early childhood influences on later life outcomes, and about ameliorating the effects of negative influences. We begin with a brief overview of the theory which illustrates that evidence of a causal relationship between a shock in early childhood and a future outcome…

Contrasting cue-density effects in causal and prediction judgments.

PubMed

Vadillo, Miguel A; Musca, Serban C; Blanco, Fernando; Matute, Helena

2011-02-01

Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.

Students' reasons for preferring teleological explanations

NASA Astrophysics Data System (ADS)

Trommler, Friederike; Gresch, Helge; Hammann, Marcus

2018-01-01

The teleological bias, a major learning obstacle, involves explaining biological phenomena in terms of purposes and goals. To probe the teleological bias, researchers have used acceptance judgement tasks and preference judgement tasks. In the present study, such tasks were used with German high school students (N = 353) for 10 phenomena from human biology, that were explained both teleologically and causally. A sub-sample (n = 26) was interviewed about the reasons for their preferences. The results showed that the students favoured teleological explanations over causal explanations. Although the students explained their preference judgements etiologically (i.e. teleologically and causally), they also referred to a wide range of non-etiological criteria (i.e. familiarity, complexity, relevance and five more criteria). When elaborating on their preference for causal explanations, the students often focused not on the causality of the phenomenon, but on mechanisms whose complexity they found attractive. When explaining their preference for teleological explanations, they often focused not teleologically on purposes and goals, but rather on functions, which they found familiar and relevant. Generally, students' preference judgements rarely allowed for making inferences about causal reasoning and teleological reasoning, an issue that is controversial in the literature. Given that students were largely unaware of causality and teleology, their attention must be directed towards distinguishing between etiological and non-etiological reasoning. Implications for educational practice as well as for future research are discussed.

Culture: copying, compression, and conventionality.

PubMed

Tamariz, Mónica; Kirby, Simon

2015-01-01

Through cultural transmission, repeated learning by new individuals transforms cultural information, which tends to become increasingly compressible (Kirby, Cornish, & Smith, ; Smith, Tamariz, & Kirby, ). Existing diffusion chain studies include in their design two processes that could be responsible for this tendency: learning (storing patterns in memory) and reproducing (producing the patterns again). This paper manipulates the presence of learning in a simple iterated drawing design experiment. We find that learning seems to be the causal factor behind the increase in compressibility observed in the transmitted information, while reproducing is a source of random heritable innovations. Only a theory invoking these two aspects of cultural learning will be able to explain human culture's fundamental balance between stability and innovation. Copyright © 2014 Cognitive Science Society, Inc.

A Quantum Probability Model of Causal Reasoning

PubMed Central

Trueblood, Jennifer S.; Busemeyer, Jerome R.

2012-01-01

People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment. PMID:22593747

Comparing humans and nonhuman great apes in the broken cloth problem: Is their knowledge causal or perceptual?

PubMed

Albiach-Serrano, Anna; Sebastián-Enesco, Carla; Seed, Amanda; Colmenares, Fernando; Call, Josep

2015-11-01

When presented with the broken cloth problem, both human children and nonhuman great apes prefer to pull a continuous cloth over a discontinuous cloth in order to obtain a desired object resting on top. This has been interpreted as evidence that they preferentially attend to the functionally relevant cues of the task (e.g., presence or absence of a gap along the cloth). However, there is controversy regarding whether great apes' behavior is underpinned by causal knowledge, involving abstract concepts (e.g., support, connection), or by perceptual knowledge, based on percepts (e.g., contact, continuity). We presented chimpanzees, orangutans, and 2-, 3-, and 4-year-old children with two versions of the broken cloth problem. The Real condition, made with paper strips, could be solved based on either perceptual cues or causal knowledge. The Painted condition, which looked very similar, could be solved only by attending to perceptual cues. All groups mastered the Real condition, in line with previous results. Older children (3- and 4-year-olds) performed significantly better in this condition than all other groups, but the performance of apes and children did not differ sharply, with 2-year-olds and apes obtaining similar results. In contrast, only 4-year-olds solved the Painted condition. We propose causal knowledge to explain the general good performance of apes and humans in the Real condition compared with the Painted condition. In addition, we suggest that symbolic knowledge might account for 4-year-olds' performance in the Painted condition. Our findings add to the growing literature supporting the idea that learning from arbitrary cues is not a good explanation for the performance of apes and humans on some kinds of physical task. Copyright © 2015 Elsevier Inc. All rights reserved.

Behavioural variation in 172 small-scale societies indicates that social learning is the main mode of human adaptation

PubMed Central

Mathew, Sarah; Perreault, Charles

2015-01-01

The behavioural variation among human societies is vast and unmatched in the animal world. It is unclear whether this variation is due to variation in the ecological environment or to differences in cultural traditions. Underlying this debate is a more fundamental question: is the richness of humans’ behavioural repertoire due to non-cultural mechanisms, such as causal reasoning, inventiveness, reaction norms, trial-and-error learning and evoked culture, or is it due to the population-level dynamics of cultural transmission? Here, we measure the relative contribution of environment and cultural history in explaining the behavioural variation of 172 Native American tribes at the time of European contact. We find that the effect of cultural history is typically larger than that of environment. Behaviours also persist over millennia within cultural lineages. This indicates that human behaviour is not predominantly determined by single-generation adaptive responses, contra theories that emphasize non-cultural mechanisms as determinants of human behaviour. Rather, the main mode of human adaptation is social learning mechanisms that operate over multiple generations. PMID:26085589

Causal Structure Learning over Time: Observations and Interventions

ERIC Educational Resources Information Center

Rottman, Benjamin M.; Keil, Frank C.

2012-01-01

Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent--the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal…

Moral Learning: Conceptual foundations and normative relevance.

PubMed

Railton, Peter

2017-10-01

What is distinctive about a bringing a learning perspective to moral psychology? Part of the answer lies in the remarkable transformations that have taken place in learning theory over the past two decades, which have revealed how powerful experience-based learning can be in the acquisition of abstract causal and evaluative representations, including generative models capable of attuning perception, cognition, affect, and action to the physical and social environment. When conjoined with developments in neuroscience, these advances in learning theory permit a rethinking of fundamental questions about the acquisition of moral understanding and its role in the guidance of behavior. For example, recent research indicates that spatial learning and navigation involve the formation of non-perspectival as well as ego-centric models of the physical environment, and that spatial representations are combined with learned information about risk and reward to guide choice and potentiate further learning. Research on infants provides evidence that they form non-perspectival expected-value representations of agents and actions as well, which help them to navigate the human environment. Such representations can be formed by highly-general mental processes such as causal and empathic simulation, and thus afford a foundation for spontaneous moral learning and action that requires no innate moral faculty and can exhibit substantial autonomy with respect to community norms. If moral learning is indeed integral with the acquisition and updating of casual and evaluative models, this affords a new way of understanding well-known but seemingly puzzling patterns in intuitive moral judgment-including the notorious "trolley problems." Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.

Electrical Stimulation in Hippocampus and Entorhinal Cortex Impairs Spatial and Temporal Memory.

PubMed

Goyal, Abhinav; Miller, Jonathan; Watrous, Andrew J; Lee, Sang Ah; Coffey, Tom; Sperling, Michael R; Sharan, Ashwini; Worrell, Gregory; Berry, Brent; Lega, Bradley; Jobst, Barbara C; Davis, Kathryn A; Inman, Cory; Sheth, Sameer A; Wanda, Paul A; Ezzyat, Youssef; Das, Sandhitsu R; Stein, Joel; Gorniak, Richard; Jacobs, Joshua

2018-05-09

The medial temporal lobe (MTL) is widely implicated in supporting episodic memory and navigation, but its precise functional role in organizing memory across time and space remains elusive. Here we examine the specific cognitive processes implemented by MTL structures (hippocampus and entorhinal cortex) to organize memory by using electrical brain stimulation, leveraging its ability to establish causal links between brain regions and features of behavior. We studied neurosurgical patients of both sexes who performed spatial-navigation and verbal-episodic memory tasks while brain stimulation was applied in various regions during learning. During the verbal memory task, stimulation in the MTL disrupted the temporal organization of encoded memories such that items learned with stimulation tended to be recalled in a more randomized order. During the spatial task, MTL stimulation impaired subjects' abilities to remember items located far away from boundaries. These stimulation effects were specific to the MTL. Our findings thus provide the first causal demonstration in humans of the specific memory processes that are performed by the MTL to encode when and where events occurred. SIGNIFICANCE STATEMENT Numerous studies have implicated the medial temporal lobe (MTL) in encoding spatial and temporal memories, but they have not been able to causally demonstrate the nature of the cognitive processes by which this occurs in real-time. Electrical brain stimulation is able to demonstrate causal links between a brain region and a given function with high temporal precision. By examining behavior in a memory task as subjects received MTL stimulation, we provide the first causal evidence demonstrating the role of the MTL in organizing the spatial and temporal aspects of episodic memory. Copyright © 2018 the authors 0270-6474/18/384471-11$15.00/0.

Causal capture effects in chimpanzees (Pan troglodytes).

PubMed

Matsuno, Toyomi; Tomonaga, Masaki

2017-01-01

Extracting a cause-and-effect structure from the physical world is an important demand for animals living in dynamically changing environments. Human perceptual and cognitive mechanisms are known to be sensitive and tuned to detect and interpret such causal structures. In contrast to rigorous investigations of human causal perception, the phylogenetic roots of this perception are not well understood. In the present study, we aimed to investigate the susceptibility of nonhuman animals to mechanical causality by testing whether chimpanzees perceived an illusion called causal capture (Scholl & Nakayama, 2002). Causal capture is a phenomenon in which a type of bistable visual motion of objects is perceived as causal collision due to a bias from a co-occurring causal event. In our experiments, we assessed the susceptibility of perception of a bistable stream/bounce motion event to a co-occurring causal event in chimpanzees. The results show that, similar to in humans, causal "bounce" percepts were significantly increased in chimpanzees with the addition of a task-irrelevant causal bounce event that was synchronously presented. These outcomes suggest that the perceptual mechanisms behind the visual interpretation of causal structures in the environment are evolutionarily shared between human and nonhuman animals. Copyright © 2016 Elsevier B.V. All rights reserved.

Evolution of Religious Beliefs

ScienceCinema

None

2018-05-11

Humans may be distinguished from all other animals in having beliefs about the causal interaction of physical objects. Causal beliefs are a developmental primitive in human children; animals, by contrast, have very few causal beliefs. The origin of human causal beliefs comes from the evolutionary advantage it gave in relation to complex tool making and use. Causal beliefs gave rise religion and mystical thinking as our ancestors wanted to know the causes of events that affected their lives.

Evolution of Religious Beliefs

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

Humans may be distinguished from all other animals in having beliefs about the causal interaction of physical objects. Causal beliefs are a developmental primitive in human children; animals, by contrast, have very few causal beliefs. The origin of human causal beliefs comes from the evolutionary advantage it gave in relation to complex tool making and use. Causal beliefs gave rise religion and mystical thinking as our ancestors wanted to know the causes of events that affected their lives.

A rational account of pedagogical reasoning: teaching by, and learning from, examples.

PubMed

Shafto, Patrick; Goodman, Noah D; Griffiths, Thomas L

2014-06-01

Much of learning and reasoning occurs in pedagogical situations--situations in which a person who knows a concept chooses examples for the purpose of helping a learner acquire the concept. We introduce a model of teaching and learning in pedagogical settings that predicts which examples teachers should choose and what learners should infer given a teacher's examples. We present three experiments testing the model predictions for rule-based, prototype, and causally structured concepts. The model shows good quantitative and qualitative fits to the data across all three experiments, predicting novel qualitative phenomena in each case. We conclude by discussing implications for understanding concept learning and implications for theoretical claims about the role of pedagogy in human learning. Copyright © 2014 Elsevier Inc. All rights reserved.

Exploring Individual Differences in Preschoolers' Causal Stance

ERIC Educational Resources Information Center

Alvarez, Aubry; Booth, Amy E.

2016-01-01

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

Behavioural variation in 172 small-scale societies indicates that social learning is the main mode of human adaptation.

PubMed

Mathew, Sarah; Perreault, Charles

2015-07-07

The behavioural variation among human societies is vast and unmatched in the animal world. It is unclear whether this variation is due to variation in the ecological environment or to differences in cultural traditions. Underlying this debate is a more fundamental question: is the richness of humans' behavioural repertoire due to non-cultural mechanisms, such as causal reasoning, inventiveness, reaction norms, trial-and-error learning and evoked culture, or is it due to the population-level dynamics of cultural transmission? Here, we measure the relative contribution of environment and cultural history in explaining the behavioural variation of 172 Native American tribes at the time of European contact. We find that the effect of cultural history is typically larger than that of environment. Behaviours also persist over millennia within cultural lineages. This indicates that human behaviour is not predominantly determined by single-generation adaptive responses, contra theories that emphasize non-cultural mechanisms as determinants of human behaviour. Rather, the main mode of human adaptation is social learning mechanisms that operate over multiple generations. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A Cognitive Simulator for Learning the Nature of Human Problem Solving

NASA Astrophysics Data System (ADS)

Miwa, Kazuhisa

Problem solving is understood as a process through which states of problem solving are transferred from the initial state to the goal state by applying adequate operators. Within this framework, knowledge and strategies are given as operators for the search. One of the most important points of researchers' interest in the domain of problem solving is to explain the performance of problem solving behavior based on the knowledge and strategies that the problem solver has. We call the interplay between problem solvers' knowledge/strategies and their behavior the causal relation between mental operations and behavior. It is crucially important, we believe, for novice learners in this domain to understand the causal relation between mental operations and behavior. Based on this insight, we have constructed a learning system in which learners can control mental operations of a computational agent that solves a task, such as knowledge, heuristics, and cognitive capacity, and can observe its behavior. We also introduce this system to a university class, and discuss which findings were discovered by the participants.

Self-regulated learning processes of medical students during an academic learning task.

PubMed

Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

2016-10-01

This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated positively with previous performance. Causal attributions and adaptive inferences measures were associated positively with learning task performance. These findings may inform remediation interventions in the early years of medical school training. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Representation of aversive prediction errors in the human periaqueductal gray

PubMed Central

Roy, Mathieu; Shohamy, Daphna; Daw, Nathaniel; Jepma, Marieke; Wimmer, Elliott; Wager, Tor D.

2014-01-01

Pain is a primary driver of learning and motivated action. It is also a target of learning, as nociceptive brain responses are shaped by learning processes. We combined an instrumental pain avoidance task with an axiomatic approach to assessing fMRI signals related to prediction errors (PEs), which drive reinforcement-based learning. We found that pain PEs were encoded in the periaqueductal gray (PAG), an important structure for pain control and learning in animal models. Axiomatic tests combined with dynamic causal modeling suggested that ventromedial prefrontal cortex, supported by putamen, provides an expected value-related input to the PAG, which then conveys PE signals to prefrontal regions important for behavioral regulation, including orbitofrontal, anterior mid-cingulate, and dorsomedial prefrontal cortices. Thus, pain-related learning involves distinct neural circuitry, with implications for behavior and pain dynamics. PMID:25282614

Learning by Self-Explaining Causal Diagrams in High-School Biology

ERIC Educational Resources Information Center

Cho, Young Hoan; Jonassen, David H.

2012-01-01

Understanding scientific phenomena requires comprehension and application of the underlying causal relationships that describe those phenomena (Carey 2002). The current study examined the roles of self-explanation and meta-level feedback for understanding causal relationships described in a causal diagram. In this study, 63 Korean high-school…

Expectations and Interpretations during Causal Learning

ERIC Educational Resources Information Center

Luhmann, Christian C.; Ahn, Woo-kyoung

2011-01-01

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to…

Preschool Children Learn about Causal Structure from Conditional Interventions

ERIC Educational Resources Information Center

Schulz, Laura E.; Gopnik, Alison; Glymour, Clark

2007-01-01

The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of experimental design and the causal Bayes net formalism. Two studies suggest that preschoolers can use the conditional intervention principle to distinguish causal chains, common cause…

Understanding cognition, choice, and behavior.

PubMed

Corcoran, K J

1995-09-01

Bandura (1995) suggests that a "crusade against the causal efficacy of human thought" exists. The present paper disputes that claim, suggesting that the quest which does exist involves an understanding of self-efficacy. Examined are Bandura's shifting definitions of self-efficacy, his misunderstandings of others' work, and implications of some of his attempts to defend the construct. In the remainder of the paper Rotter's Social Learning Theory is discussed as a model of human choice behavior which recognizes the contributions of both cognitive and behavioral traditions within psychology, and has proven to be of great heuristic value.

Selective effects of explanation on learning during early childhood.

PubMed

Legare, Cristine H; Lombrozo, Tania

2014-10-01

Two studies examined the specificity of effects of explanation on learning by prompting 3- to 6-year-old children to explain a mechanical toy and comparing what they learned about the toy's causal and non-causal properties with children who only observed the toy, both with and without accompanying verbalization. In Study 1, children were experimentally assigned to either explain or observe the mechanical toy. In Study 2, children were classified according to whether the content of their response to an undirected prompt involved explanation. Dependent measures included whether children understood the toy's functional-mechanical relationships, remembered perceptual features of the toy, effectively reconstructed the toy, and (for Study 2) generalized the function of the toy when constructing a new one. Results demonstrate that across age groups, explanation promotes causal learning and generalization but does not improve (and in younger children can even impair) memory for causally irrelevant perceptual details. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Inferring causal molecular networks: empirical assessment through a community-based effort.

PubMed

Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-04-01

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Illusions of causality at the heart of pseudoscience.

PubMed

Matute, Helena; Yarritu, Ion; Vadillo, Miguel A

2011-08-01

Pseudoscience, superstitions, and quackery are serious problems that threaten public health and in which many variables are involved. Psychology, however, has much to say about them, as it is the illusory perceptions of causality of so many people that needs to be understood. The proposal we put forward is that these illusions arise from the normal functioning of the cognitive system when trying to associate causes and effects. Thus, we propose to apply basic research and theories on causal learning to reduce the impact of pseudoscience. We review the literature on the illusion of control and the causal learning traditions, and then present an experiment as an illustration of how this approach can provide fruitful ideas to reduce pseudoscientific thinking. The experiment first illustrates the development of a quackery illusion through the testimony of fictitious patients who report feeling better. Two different predictions arising from the integration of the causal learning and illusion of control domains are then proven effective in reducing this illusion. One is showing the testimony of people who feel better without having followed the treatment. The other is asking participants to think in causal terms rather than in terms of effectiveness. ©2010 The British Psychological Society.

Perceptual learning shapes multisensory causal inference via two distinct mechanisms

PubMed Central

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

2016-01-01

To accurately represent the environment, our brains must integrate sensory signals from a common source while segregating those from independent sources. A reasonable strategy for performing this task is to restrict integration to cues that coincide in space and time. However, because multisensory signals are subject to differential transmission and processing delays, the brain must retain a degree of tolerance for temporal discrepancies. Recent research suggests that the width of this ‘temporal binding window’ can be reduced through perceptual learning, however, little is known about the mechanisms underlying these experience-dependent effects. Here, in separate experiments, we measure the temporal and spatial binding windows of human participants before and after training on an audiovisual temporal discrimination task. We show that training leads to two distinct effects on multisensory integration in the form of (i) a specific narrowing of the temporal binding window that does not transfer to spatial binding and (ii) a general reduction in the magnitude of crossmodal interactions across all spatiotemporal disparities. These effects arise naturally from a Bayesian model of causal inference in which learning improves the precision of audiovisual timing estimation, whilst concomitantly decreasing the prior expectation that stimuli emanate from a common source. PMID:27091411

Perceptual learning shapes multisensory causal inference via two distinct mechanisms.

PubMed

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

2016-04-19

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

Evidence for online processing during causal learning.

PubMed

Liu, Pei-Pei; Luhmann, Christian C

2015-03-01

Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning.

Causal Imprinting in Causal Structure Learning

PubMed Central

Taylor, Eric G.; Ahn, Woo-kyoung

2012-01-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures “causal imprinting.” Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. PMID:22859019

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

PubMed

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

2018-01-01

Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

NASA Astrophysics Data System (ADS)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

Explaining Constrains Causal Learning in Childhood

ERIC Educational Resources Information Center

Walker, Caren M.; Lombrozo, Tania; Williams, Joseph J.; Rafferty, Anna N.; Gopnik, Alison

2017-01-01

Three experiments investigate how self-generated explanation influences children's causal learning. Five-year-olds (N = 114) observed data consistent with two hypotheses and were prompted to explain or to report each observation. In Study 1, when making novel generalizations, explainers were more likely to favor the hypothesis that accounted for…

Effects of Attributional Retraining on Strategy-Based Reading Comprehension in Learning-Disabled Students.

ERIC Educational Resources Information Center

Borkowski, John G.; And Others

1988-01-01

Seventy-five learning-disabled students (10 to 14 years old) received instructions about summarization strategies and about personal causality that were designed to improve reading comprehension. Changes in antecedent attributions about personal causality were not usually altered by this program-specific attributional training, although…

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

PubMed Central

Hagmayer, York; Engelmann, Neele

2014-01-01

Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analyzed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given. PMID:25505432

Conservative forgetful scholars: How people learn causal structure through sequences of interventions.

PubMed

Bramley, Neil R; Lagnado, David A; Speekenbrink, Maarten

2015-05-01

Interacting with a system is key to uncovering its causal structure. A computational framework for interventional causal learning has been developed over the last decade, but how real causal learners might achieve or approximate the computations entailed by this framework is still poorly understood. Here we describe an interactive computer task in which participants were incentivized to learn the structure of probabilistic causal systems through free selection of multiple interventions. We develop models of participants' intervention choices and online structure judgments, using expected utility gain, probability gain, and information gain and introducing plausible memory and processing constraints. We find that successful participants are best described by a model that acts to maximize information (rather than expected score or probability of being correct); that forgets much of the evidence received in earlier trials; but that mitigates this by being conservative, preferring structures consistent with earlier stated beliefs. We explore 2 heuristics that partly explain how participants might be approximating these models without explicitly representing or updating a hypothesis space. (c) 2015 APA, all rights reserved).

Mimesis: Linking Postmodern Theory to Human Behavior

ERIC Educational Resources Information Center

Dybicz, Phillip

2010-01-01

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

Timing and Causality in the Generation of Learned Eyelid Responses

PubMed Central

Sánchez-Campusano, Raudel; Gruart, Agnès; Delgado-García, José M.

2011-01-01

The cerebellum-red nucleus-facial motoneuron (Mn) pathway has been reported as being involved in the proper timing of classically conditioned eyelid responses. This special type of associative learning serves as a model of event timing for studying the role of the cerebellum in dynamic motor control. Here, we have re-analyzed the firing activities of cerebellar posterior interpositus (IP) neurons and orbicularis oculi (OO) Mns in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. The aim was to revisit the hypothesis that the IP neurons (IPns) can be considered a neuronal phase-modulating device supporting OO Mns firing with an emergent timing mechanism and an explicit correlation code during learned eyelid movements. Optimized experimental and computational tools allowed us to determine the different causal relationships (temporal order and correlation code) during and between trials. These intra- and inter-trial timing strategies expanding from sub-second range (millisecond timing) to longer-lasting ranges (interval timing) expanded the functional domain of cerebellar timing beyond motor control. Interestingly, the results supported the above-mentioned hypothesis. The causal inferences were influenced by the precise motor and pre-motor spike timing in the cause-effect interval, and, in addition, the timing of the learned responses depended on cerebellar–Mn network causality. Furthermore, the timing of CRs depended upon the probability of simulated causal conditions in the cause-effect interval and not the mere duration of the inter-stimulus interval. In this work, the close relation between timing and causality was verified. It could thus be concluded that the firing activities of IPns may be related more to the proper performance of ongoing CRs (i.e., the proper timing as a consequence of the pertinent causality) than to their generation and/or initiation. PMID:21941469

A behavioural preparation for the study of human Pavlovian conditioning.

PubMed

Arcediano, F; Ortega, N; Matute, H

1996-08-01

Conditioned suppression is a useful technique for assessing whether subjects have learned a CS-US association, but it is difficult to use in humans because of the need for an aversive US. The purpose of this research was to develop a non-aversive procedure that would produce suppression. Subjects learned to press the space bar of a computer as part of a video game, but they had to stop pressing whenever a visual US appeared, or they would lose points. In Experiment 1, we used an A+/B- discrimination design: The US always followed Stimulus A and never followed Stimulus B. Although no information about the existence of CSs was given to the subjects, suppression ratio results showed a discrimination learning curve-that is, subjects learned to suppress responding in anticipation of the US when Stimulus A was present but not during the presentations of Stimulus B. Experiment 2 explored the potential of this preparation by using two different instruction sets and assessing post-experimental judgements of CS A and CS B in addition to suppression ratios. The results of these experiments suggest that conditioned suppression can be reliably and conveniently used in the human laboratory, providing a bridge between experiments on animal conditioning and experiments on human judgements of causality.

Can Being Scared Cause Tummy Aches? Naive Theories, Ambiguous Evidence, and Preschoolers' Causal Inferences

ERIC Educational Resources Information Center

Schulz, Laura E.; Bonawitz, Elizabeth Baraff; Griffiths, Thomas L.

2007-01-01

Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate…

Learning a Theory of Causality

ERIC Educational Resources Information Center

Goodman, Noah D.; Ullman, Tomer D.; Tenenbaum, Joshua B.

2011-01-01

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be…

Neural Basis of Empathy and Its Dysfunction in Autism Spectrum Disorders (ASD)

DTIC Science & Technology

2013-08-01

representation and social learning (Chang et al., 2013). Therefore, we tested the causality of ACCg neurons in addition to neurons in a series of prefrontal...paradigms, we in parallel trained new monkeys to perform observational social 5 learning tasks, which also depend on sensitivity to the rewarding...experiences of another individual. We tested the causal contribution of ACCg and insula to social learning by investigating whether observer monkeys

Inferring causal molecular networks: empirical assessment through a community-based effort

PubMed Central

Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-01-01

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks. PMID:26901648

Causal imprinting in causal structure learning.

PubMed

Taylor, Eric G; Ahn, Woo-Kyoung

2012-11-01

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

Parental causal attributions and emotions in daily learning situations with the child.

PubMed

Enlund, Emmi; Aunola, Kaisa; Tolvanen, Asko; Nurmi, Jari-Erik

2015-08-01

This study investigated the dynamics between the causal attributions parents reported daily for their children's success in learning situations and parental positive emotions. The sample consisted of 159 mothers and 147 fathers of 162 first graders (83 girls, 79 boys; aged from 6 to 7 years, M = 7.5 years, SD = 3.6 months). Parents filled in a structured diary questionnaire concerning their causal attributions and emotions over 7 successive days in the fall semester and again over 7 successive days in the spring semester. Multilevel analyses showed that both parental causal attributions and positive emotions varied more within parents (between days over the week) than between parents. Furthermore, mothers' positive emotions on a certain day predicted their causal attributions on that same day rather than vice versa. The higher the level of positive emotions parents reported in a specific day, the more they used effort and ability as causal attributions for their offspring's success on that same day. (c) 2015 APA, all rights reserved).

The Development of Causal Categorization

ERIC Educational Resources Information Center

Hayes, Brett K.; Rehder, Bob

2012-01-01

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs.…

Own and Others' Prior Experiences Influence Children's Imitation of Causal Acts

ERIC Educational Resources Information Center

Williamson, Rebecca A.; Meltzoff, Andrew N.

2011-01-01

Young children learn from others' examples, and they do so selectively. We examine whether the efficacy of prior experiences influences children's imitation. Thirty-six-month-olds had initial experience on a causal learning task either by performing the task themselves or by watching an adult perform it. The nature of the experience was…

Constructing Causal Diagrams to Learn Deliberation

ERIC Educational Resources Information Center

Easterday, Matthew W.; Aleven, Vincent; Scheines, Richard; Carver, Sharon M.

2009-01-01

Policy problems like "What should we do about global warming?" are ill-defined in large part because we do not agree on a system to represent them the way we agree Algebra problems should be represented by equations. As a first step toward building a policy deliberation tutor, we investigated: (a) whether causal diagrams help students learn to…

Feedback Both Helps and Hinders Learning: The Causal Role of Prior Knowledge

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany

2016-01-01

Feedback can be a powerful learning tool, but its effects vary widely. Research has suggested that learners' prior knowledge may moderate the effects of feedback; however, no causal link has been established. In Experiment 1, we randomly assigned elementary school children (N = 108) to a condition based on a crossing of 2 factors: induced strategy…

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

PubMed

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

2013-02-01

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

Medication adherence as a learning process: insights from cognitive psychology.

PubMed

Rottman, Benjamin Margolin; Marcum, Zachary A; Thorpe, Carolyn T; Gellad, Walid F

2017-03-01

Non-adherence to medications is one of the largest contributors to sub-optimal health outcomes. Many theories of adherence include a 'value-expectancy' component in which a patient decides to take a medication partly based on expectations about whether it is effective, necessary, and tolerable. We propose reconceptualising this common theme as a kind of 'causal learning' - the patient learns whether a medication is effective, necessary, and tolerable, from experience with the medication. We apply cognitive psychology theories of how people learn cause-effect relations to elaborate this causal-learning challenge. First, expectations and impressions about a medication and beliefs about how a medication works, such as delay of onset, can shape a patient's perceived experience with the medication. Second, beliefs about medications propagate both 'top-down' and 'bottom-up', from experiences with specific medications to general beliefs about medications and vice versa. Third, non-adherence can interfere with learning about a medication, because beliefs, adherence, and experience with a medication are connected in a cyclic learning problem. We propose that by conceptualising non-adherence as a causal-learning process, clinicians can more effectively address a patient's misconceptions and biases, helping the patient develop more accurate impressions of the medication.

Temporal and Statistical Information in Causal Structure Learning

ERIC Educational Resources Information Center

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

2015-01-01

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

Learning models of Human-Robot Interaction from small data

PubMed Central

Zehfroosh, Ashkan; Kokkoni, Elena; Tanner, Herbert G.; Heinz, Jeffrey

2018-01-01

This paper offers a new approach to learning discrete models for human-robot interaction (HRI) from small data. In the motivating application, HRI is an integral part of a pediatric rehabilitation paradigm that involves a play-based, social environment aiming at improving mobility for infants with mobility impairments. Designing interfaces in this setting is challenging, because in order to harness, and eventually automate, the social interaction between children and robots, a behavioral model capturing the causality between robot actions and child reactions is needed. The paper adopts a Markov decision process (MDP) as such a model, and selects the transition probabilities through an empirical approximation procedure called smoothing. Smoothing has been successfully applied in natural language processing (NLP) and identification where, similarly to the current paradigm, learning from small data sets is crucial. The goal of this paper is two-fold: (i) to describe our application of HRI, and (ii) to provide evidence that supports the application of smoothing for small data sets. PMID:29492408

Learning models of Human-Robot Interaction from small data.

PubMed

Zehfroosh, Ashkan; Kokkoni, Elena; Tanner, Herbert G; Heinz, Jeffrey

2017-07-01

This paper offers a new approach to learning discrete models for human-robot interaction (HRI) from small data. In the motivating application, HRI is an integral part of a pediatric rehabilitation paradigm that involves a play-based, social environment aiming at improving mobility for infants with mobility impairments. Designing interfaces in this setting is challenging, because in order to harness, and eventually automate, the social interaction between children and robots, a behavioral model capturing the causality between robot actions and child reactions is needed. The paper adopts a Markov decision process (MDP) as such a model, and selects the transition probabilities through an empirical approximation procedure called smoothing. Smoothing has been successfully applied in natural language processing (NLP) and identification where, similarly to the current paradigm, learning from small data sets is crucial. The goal of this paper is two-fold: (i) to describe our application of HRI, and (ii) to provide evidence that supports the application of smoothing for small data sets.

Critical roles for anterior insula and dorsal striatum in punishment-based avoidance learning.

PubMed

Palminteri, Stefano; Justo, Damian; Jauffret, Céline; Pavlicek, Beth; Dauta, Aurélie; Delmaire, Christine; Czernecki, Virginie; Karachi, Carine; Capelle, Laurent; Durr, Alexandra; Pessiglione, Mathias

2012-12-06

The division of human learning systems into reward and punishment opponent modules is still a debated issue. While the implication of ventral prefrontostriatal circuits in reward-based learning is well established, the neural underpinnings of punishment-based learning remain unclear. To elucidate the causal implication of brain regions that were related to punishment learning in a previous functional neuroimaging study, we tested the effects of brain damage on behavioral performance, using the same task contrasting monetary gains and losses. Cortical and subcortical candidate regions, the anterior insula and dorsal striatum, were assessed in patients presenting brain tumor and Huntington disease, respectively. Both groups exhibited selective impairment of punishment-based learning. Computational modeling suggested complementary roles for these structures: the anterior insula might be involved in learning the negative value of loss-predicting cues, whereas the dorsal striatum might be involved in choosing between those cues so as to avoid the worst. Copyright © 2012 Elsevier Inc. All rights reserved.

The development of causal reasoning.

PubMed

Kuhn, Deanna

2012-05-01

How do inference rules for causal learning themselves change developmentally? A model of the development of causal reasoning must address this question, as well as specify the inference rules. Here, the evidence for developmental changes in processes of causal reasoning is reviewed, with the distinction made between diagnostic causal inference and causal prediction. Also addressed is the paradox of a causal reasoning literature that highlights the competencies of young children and the proneness to error among adults. WIREs Cogn Sci 2012, 3:327-335. doi: 10.1002/wcs.1160 For further resources related to this article, please visit the WIREs website. Copyright © 2012 John Wiley & Sons, Ltd.

Investigating Causality between Global Experience and Global Competency for Undergraduates in Contemporary China's Higher Education: A Transformative Learning Theory Perspective

ERIC Educational Resources Information Center

Li, Jian; Xu, Jinhui

2016-01-01

The purpose of this study is to investigate the causal relationship between global experience and global competency from a transformative learning theory perspective. China society is becoming more and more linguistically and culturally diverse in a global context. Moreover, Chinese students should be knowledgeable about the international issues…

Who Is the Dynamic Duo? How Infants Learn about the Identity of Objects in a Causal Chain

ERIC Educational Resources Information Center

Rakison, David H.; Smith, Gabriel Tobin; Ali, Areej

2016-01-01

Four experiments investigated infants' and adults' knowledge of the identity of objects in a causal sequence of events. In Experiments 1 and 2, 18- and 22-month-olds in the visual habituation procedure were shown a 3-step causal chain event in which the relation between an object's part (dynamic or static) and its causal role was either consistent…

Causal reasoning with forces

PubMed Central

Wolff, Phillip; Barbey, Aron K.

2015-01-01

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611

Human nicotine conditioning requires explicit contingency knowledge: is addictive behaviour cognitively mediated?

PubMed

Hogarth, Lee; Duka, Theodora

2006-03-01

Two seemingly contrary theories describe the learning mechanisms that mediate human addictive behaviour. According to the classical incentive theories of addiction, addictive behaviour is motivated by a Pavlovian conditioned appetitive emotional response elicited by drug-paired stimuli. Expectancy theory, on the other hand, argues that addictive behaviour is mediated by an expectancy of the drug imparted by cognitive knowledge of the Pavlovian (predictive) contingency between stimuli (S+) and the drug and of the instrumental (causal) contingency between instrumental behaviour and the drug. The present paper reviewed human-nicotine-conditioning studies to assess the role of appetitive emotional conditioning and explicit contingency knowledge in mediating addictive behaviour. The studies reviewed here provided evidence for both the emotional conditioning and the expectancy accounts. The first source of evidence is that nicotine-paired S+ elicit an appetitive emotional conditioned response (CR), albeit only in participants who expect nicotine. Furthermore, the magnitude of this emotional state is modulated by nicotine deprivation/satiation. However, the causal status of the emotional response in driving other forms of conditioned behaviour remains undemonstrated. The second source of evidence is that other nicotine CRs, including physiological responses, self-administration, attentional bias and subjective craving, are also dependent on participants possessing explicit knowledge of the Pavlovian contingencies arranged in the experiment. In addition, several of the nicotine CRs can be brought about or modified by instructed contingency knowledge, demonstrating the causal status of this knowledge. Collectively, these data suggest that human nicotine conditioned effects are mediated by an explicit expectancy of the drug coupled with an appetitive emotional response that reflects the positive biological value of the drug. The implication of this conclusion is that treatments designed to modify the expected value of the drug may prove effective.

What Doesn't Go without Saying: Communication, Induction, and Exploration

ERIC Educational Resources Information Center

Muentener, Paul; Schulz, Laura

2012-01-01

Although prior research on the development of causal reasoning has focused on inferential abilities within the individual child, causal learning often occurs in a social and communicative context. In this paper, we review recent research from our laboratory and look at how linguistic communication may influence children's causal reasoning. First,…

Repeated Causal Decision Making

ERIC Educational Resources Information Center

Hagmayer, York; Meder, Bjorn

2013-01-01

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

Spatial separation of target and competitor cues enhances blocking of human causality judgements.

PubMed

Glautier, Steven

2002-04-01

Three experiments were carried out. Each required subjects to make judgements about the causal status of cues following a two-stage blocking procedure. In Stage 1 a competitor cue was consistently paired with an outcome, and in Stage 2 the competitor continued to be paired with the outcome but was accompanied by a target cue. It was predicted that causal judgements for the target would be reduced by the presence of the competitor. In Experiments 1 and 2 the blocking procedure was implemented as a computer simulation of a card game during which subjects had to learn which cards produced the best payouts. The cues that subjects used to make their judgement were colours and symbols that appeared on the backs of the cards. When the target and competitor cues appeared on the same card blocking effects did not emerge, but when they appeared as part of different cards blocking effects were found. Thus, spatial separation of target and competitor cues appeared to facilitate blocking. Experiment 3 replicated the blocking result using spatially separated target and competitor cues.

Teaching towards Historical Expertise. Developing a Pedagogy for Fostering Causal Reasoning in History

ERIC Educational Resources Information Center

Stoel, G. L.; van Drie, J. P.; van Boxtel, C. A. M.

2015-01-01

The present study seeks to develop a pedagogy aimed at fostering a student's ability to reason causally about history. The Model of Domain Learning was used as a framework to align domain-specific content with pedagogical principles. Developing causal historical reasoning was conceptualized as a multidimensional process, in which knowledge of…

Young Children's Understanding of Teaching and Learning and Their Theory of Mind Development: A Causal Analysis from a Cross-Cultural Perspective

PubMed Central

Wang, Zhenlin; Wang, X. Christine; Chui, Wai Yip

2017-01-01

Children's understanding of the concepts of teaching and learning is closely associated with their theory of mind (ToM) ability and vital for school readiness. This study aimed to develop and validate a Preschool Teaching and Learning Comprehension Index (PTLCI) across cultures and examine the causal relationship between children's comprehension of teaching and learning and their mental state understanding. Two hundred and twelve children from 3 to 6 years of age from Hong Kong and the United States participated in study. The results suggested strong construct validity of the PTLCI, and its measurement and structural equivalence within and across cultures. ToM and PTLCI were significantly correlated with a medium effect size, even after controlling for age, and language ability. Hong Kong children outperformed their American counterparts in both ToM and PTLCI. Competing structural equation models suggested that children's performance on the PTLCI causally predicted their ToM across countries. PMID:28559863

The selective power of causality on memory errors.

PubMed

Marsh, Jessecae K; Kulkofsky, Sarah

2015-01-01

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

Models of conceptual understanding in human respiration and strategies for instruction

NASA Astrophysics Data System (ADS)

Rea-Ramirez, Mary Anne

Prior research has indicated that students of all ages show little understanding of respiration beyond breathing in and out and the need for air to survive. This occurs even after instruction with alternative conceptions persisting into adulthood. Whether this is due to specific educational strategies or to the level of difficulty in understanding a complex system is an important question. The purpose of this study was to obtain a deeper understanding of middle school students' development of mental models of human respiration. The study was composed of two major parts, one concerned with documenting and analyzing how students learn, and one concerned with measuring the effect of teaching strategies. This was carried out through a pre-test, "learning aloud" case studies in which students engaged in one-on-one tutoring interviews with the researcher, and a post-test. Transcript data from the intervention and post-test indicates that all students in this study were successful in constructing mental models of a complex concept, respiration, and in successfully applying these mental models to transfer problems. Differences in the pretest and posttest means were on the order of two standard deviations in size. While findings were uncovered in the use of a variety of strategies, possibly most interesting are the new views of analogies as an instructional strategy. Some analogies appear to be effective in supporting construction of visual/spatial features. Providing multiple, simple analogies that allow the student to construct new models in small steps, using student generated analogies, and using analogies to determine prior knowledge may also increase the effectiveness of analogies. Evidence suggested that students were able to extend the dynamic properties of certain analogies to the dynamics of the target conception and that this, in turn, allowed students to use the new models to explain causal relationships and give new function to models. This suggests that construction of causal, dynamic mental models is supported by the use of analogies containing dynamic and causal relationships.

The Effects of Explicit Teaching of Strategies, Second-Order Concepts, and Epistemological Underpinnings on Students' Ability to Reason Causally in History

ERIC Educational Resources Information Center

Stoel, Gerhard L.; van Drie, Jannet P.; van Boxtel, Carla A. M.

2017-01-01

This article reports an experimental study on the effects of explicit teaching on 11th grade students' ability to reason causally in history. Underpinned by the model of domain learning, explicit teaching is conceptualized as multidimensional, focusing on strategies and second-order concepts to generate and verbalize causal explanations and…

Exceptionalist naturalism: Human agency and the causal order.

PubMed

Turri, John

2018-02-01

This paper addresses a fundamental question in folk metaphysics: How do we ordinarily view human agency? According to the transcendence account, we view human agency as standing outside of the causal order and imbued with exceptional powers. According to a naturalistic account, we view human agency as subject to the same physical laws as other objects and completely open to scientific investigation. According to exceptionalist naturalism, the truth lies somewhere in between: We view human agency as fitting broadly within the causal order while still being exceptional in important respects. In this paper, I report seven experiments designed to decide between these three competing theories. Across a variety of contexts and types of action, participants agreed that human agents can resist outcomes described as inevitable, guaranteed, and causally determined. Participants viewed non-human animal agents similarly, whereas they viewed computers, robots, and simple inanimate objects differently. At the same time, participants judged that human actions are caused by many things, including psychological, neurological, and social events. Overall, in folk metaphysics, human and non-human animals are viewed as exceptional parts of the natural world.

Exploring individual differences in preschoolers' causal stance.

PubMed

Alvarez, Aubry; Booth, Amy E

2016-03-01

Preschoolers, as a group, are highly attuned to causality, and this attunement is known to facilitate memory, learning, and problem solving. However, recent work reveals substantial individual variability in the strength of children's "causal stance," as demonstrated by their curiosity about and preference for new causal information. In this study, we explored the coherence and short-term stability of individual differences in children's causal stance. We also began to investigate the origins of this variability, focusing particularly on the potential role of mothers' explanatory talk in shaping the causal stance of their children. Two measures of causal stance correlated with each other, as well as themselves across time. Both also revealed internal consistency of response. The strength of children's causal stance also correlated with mother's responses on the same tasks and the frequency with which mothers emphasized causality during naturalistic joint activities with their children. Implications for theory and practice are discussed. (c) 2016 APA, all rights reserved).

Sufficiency and Necessity Assumptions in Causal Structure Induction

ERIC Educational Resources Information Center

Mayrhofer, Ralf; Waldmann, Michael R.

2016-01-01

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when…

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

PubMed

Hitomi, Yuki; Tokunaga, Katsushi

2017-01-01

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

Calculating and Understanding: Formal Models and Causal Explanations in Science, Common Reasoning and Physics Teaching

NASA Astrophysics Data System (ADS)

Besson, Ugo

2010-03-01

This paper presents an analysis of the different types of reasoning and physical explanation used in science, common thought, and physics teaching. It then reflects on the learning difficulties connected with these various approaches, and suggests some possible didactic strategies. Although causal reasoning occurs very frequently in common thought and daily life, it has long been the subject of debate and criticism among philosophers and scientists. In this paper, I begin by providing a description of some general tendencies of common reasoning that have been identified by didactic research. Thereafter, I briefly discuss the role of causality in science, as well as some different types of explanation employed in the field of physics. I then present some results of a study examining the causal reasoning used by students in solid and fluid mechanics. The differences found between the types of reasoning typical of common thought and those usually proposed during instruction can create learning difficulties and impede student motivation. Many students do not seem satisfied by the mere application of formal laws and functional relations. Instead, they express the need for a causal explanation, a mechanism that allows them to understand how a state of affairs has come about. I discuss few didactic strategies aimed at overcoming these problems, and describe, in general terms, two examples of mechanics teaching sequences which were developed and tested in different contexts. The paper ends with a reflection on the possible role to be played in physics learning by intuitive and imaginative thought, and the use of simple explanatory models based on physical analogies and causal mechanisms.

Causal relationships among academic delay of gratification, motivation, and self-regulated learning in elementary school children.

PubMed

Zhang, Lili; Maruno, Shun'ichi

2010-10-01

Academic delay of gratification refers to the postponement of immediate rewards by students and the pursuit of more important, temporally remote academic goals. A path model was designed to identify the causal relationships among academic delay of gratification and motivation, self-regulated learning strategies (as specified in the Motivated Strategies for Learning Questionnaire), and grades among 386 Chinese elementary school children. Academic delay of gratification was found to be positively related to motivation and metacognition. Cognitive strategy, resource management, and grades mediated these two factors and were indirectly related to academic delay of gratification.

Strengthening of causality assessment of adverse events following immunization in the WHO South East Asia and Western Pacific regions: Lessons from the 2014 SEAR inter-country workshop.

PubMed

MacDonald, Noni E; Guichard, Stephane; Amarasinghe, Ananda; Balakrishnan, Madhava Ram

2015-11-27

Poorly managed AEFI undermine immunization programs. Improved surveillance in SEAR countries means more AEFIs but management varies. SEAR brought countries together to share AEFI experiences, and learn more about causality assessment. Three day 10 country workshop (9 SEAR; 1 WPR). Participants outlined county AEFI experiences, undertook causality assessment for 8 AEFIs using WHO methodology, critiqued the process by questionnaire and had a discussion. All 10 valued AEFI monitoring and causality assessment, and praised the opportunity to share experiences. Participants determined a range of AEFI and causality assessment needs in SEAR such as adapting WHO Algorithm, CIOMS/Brighton definitions, WHO verbal autopsy to fit context, requesting a practical guide--AEFI definition, time interval, rates of AEFI for different vaccines and evidence for vaccine related causes of death under 24h. LMIC need WHO AEFI tools adapted to better fit LMIC. Learning from each other builds capacity. Sharing AEFI experiences, case reviews help LMIC improve practices. Copyright © 2015. Published by Elsevier Ltd.

Knowing Who Dunnit: Infants Identify the Causal Agent in an Unseen Causal Interaction

ERIC Educational Resources Information Center

Saxe, Rebecca; Tzelnic, Tania; Carey, Susan

2007-01-01

Preverbal infants can represent the causal structure of events, including distinguishing the agentive and receptive roles and categorizing entities according to stable causal dispositions. This study investigated how infants combine these 2 kinds of causal inference. In Experiments 1 and 2, 9.5-month-olds used the position of a human hand or a…

Implementation and Assessment of an Intervention to Debias Adolescents against Causal Illusions

PubMed Central

Barberia, Itxaso; Blanco, Fernando; Cubillas, Carmelo P.; Matute, Helena

2013-01-01

Researchers have warned that causal illusions are at the root of many superstitious beliefs and fuel many people’s faith in pseudoscience, thus generating significant suffering in modern society. Therefore, it is critical that we understand the mechanisms by which these illusions develop and persist. A vast amount of research in psychology has investigated these mechanisms, but little work has been done on the extent to which it is possible to debias individuals against causal illusions. We present an intervention in which a sample of adolescents was introduced to the concept of experimental control, focusing on the need to consider the base rate of the outcome variable in order to determine if a causal relationship exists. The effectiveness of the intervention was measured using a standard contingency learning task that involved fake medicines that typically produce causal illusions. Half of the participants performed the contingency learning task before participating in the educational intervention (the control group), and the other half performed the task after they had completed the intervention (the experimental group). The participants in the experimental group made more realistic causal judgments than did those in the control group, which served as a baseline. To the best of our knowledge, this is the first evidence-based educational intervention that could be easily implemented to reduce causal illusions and the many problems associated with them, such as superstitions and belief in pseudoscience. PMID:23967189

Expanding the basic science debate: the role of physics knowledge in interpreting clinical findings.

PubMed

Goldszmidt, Mark; Minda, John Paul; Devantier, Sarah L; Skye, Aimee L; Woods, Nicole N

2012-10-01

Current research suggests a role for biomedical knowledge in learning and retaining concepts related to medical diagnosis. However, learning may be influenced by other, non-biomedical knowledge. We explored this idea using an experimental design and examined the effects of causal knowledge on the learning, retention, and interpretation of medical information. Participants studied a handout about several respiratory disorders and how to interpret respiratory exam findings. The control group received the information in standard "textbook" format and the experimental group was presented with the same information as well as a causal explanation about how sound travels through lungs in both the normal and disease states. Comprehension and memory of the information was evaluated with a multiple-choice exam. Several questions that were not related to the causal knowledge served as control items. Questions related to the interpretation of physical exam findings served as the critical test items. The experimental group outperformed the control group on the critical test items, and our study shows that a causal explanation can improve a student's memory for interpreting clinical details. We suggest an expansion of which basic sciences are considered fundamental to medical education.

How partial reinforcement of food cues affects the extinction and reacquisition of appetitive responses. A new model for dieting success?

PubMed

van den Akker, Karolien; Havermans, Remco C; Bouton, Mark E; Jansen, Anita

2014-10-01

Animals and humans can easily learn to associate an initially neutral cue with food intake through classical conditioning, but extinction of learned appetitive responses can be more difficult. Intermittent or partial reinforcement of food cues causes especially persistent behaviour in animals: after exposure to such learning schedules, the decline in responding that occurs during extinction is slow. After extinction, increases in responding with renewed reinforcement of food cues (reacquisition) might be less rapid after acquisition with partial reinforcement. In humans, it may be that the eating behaviour of some individuals resembles partial reinforcement schedules to a greater extent, possibly affecting dieting success by interacting with extinction and reacquisition. Furthermore, impulsivity has been associated with less successful dieting, and this association might be explained by impulsivity affecting the learning and extinction of appetitive responses. In the present two studies, the effects of different reinforcement schedules and impulsivity on the acquisition, extinction, and reacquisition of appetitive responses were investigated in a conditioning paradigm involving food rewards in healthy humans. Overall, the results indicate both partial reinforcement schedules and, possibly, impulsivity to be associated with worse extinction performance. A new model of dieting success is proposed: learning histories and, perhaps, certain personality traits (impulsivity) can interfere with the extinction and reacquisition of appetitive responses to food cues and they may be causally related to unsuccessful dieting. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

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

2010-09-01

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

Factual and Counterfactual Action-Outcome Mappings Control Choice between Goal-Directed Actions in Rats.

PubMed

Laurent, Vincent; Balleine, Bernard W

2015-04-20

The capacity to extract causal knowledge from the environment allows us to predict future events and to use those predictions to decide on a course of action. Although evidence of such causal reasoning has long been described, recent evidence suggests that using predictive knowledge to guide decision-making in this way is predicated on reasoning about causes in two quite distinct ways: choosing an action can be based on the interaction between predictive information and the consequences of that action, or, alternatively, actions can be selected based on the consequences that they do not produce. The latter counterfactual reasoning is highly adaptive because it allows us to use information about both present and absent events to guide decision-making. Nevertheless, although there is now evidence to suggest that animals other than humans, including rats and birds, can engage in causal reasoning of one kind or another, there is currently no evidence that they use counterfactual reasoning to guide choice. To assess this question, we gave rats the opportunity to learn new action-outcome relationships, after which we probed the structure of this learning by presenting excitatory and inhibitory cues predicting that the specific outcomes of their actions would either occur or would not occur. Whereas the excitors biased choice toward the action delivering the predicted outcome, the inhibitory cues selectively elevated actions predicting the absence of the inhibited outcome, suggesting that rats encoded the counterfactual action-outcome mappings and were able to use them to guide choice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.

PubMed

Soto, Fabian A; Gershman, Samuel J; Niv, Yael

2014-07-01

How do we apply learning from one situation to a similar, but not identical, situation? The principles governing the extent to which animals and humans generalize what they have learned about certain stimuli to novel compounds containing those stimuli vary depending on a number of factors. Perhaps the best studied among these factors is the type of stimuli used to generate compounds. One prominent hypothesis is that different generalization principles apply depending on whether the stimuli in a compound are similar or dissimilar to each other. However, the results of many experiments cannot be explained by this hypothesis. Here, we propose a rational Bayesian theory of compound generalization that uses the notion of consequential regions, first developed in the context of rational theories of multidimensional generalization, to explain the effects of stimulus factors on compound generalization. The model explains a large number of results from the compound generalization literature, including the influence of stimulus modality and spatial contiguity on the summation effect, the lack of influence of stimulus factors on summation with a recovered inhibitor, the effect of spatial position of stimuli on the blocking effect, the asymmetrical generalization decrement in overshadowing and external inhibition, and the conditions leading to a reliable external inhibition effect. By integrating rational theories of compound and dimensional generalization, our model provides the first comprehensive computational account of the effects of stimulus factors on compound generalization, including spatial and temporal contiguity between components, which have posed long-standing problems for rational theories of associative and causal learning. (c) 2014 APA, all rights reserved.

Explaining Compound Generalization in Associative and Causal Learning Through Rational Principles of Dimensional Generalization

PubMed Central

Soto, Fabian A.; Gershman, Samuel J.; Niv, Yael

2014-01-01

How do we apply learning from one situation to a similar, but not identical, situation? The principles governing the extent to which animals and humans generalize what they have learned about certain stimuli to novel compounds containing those stimuli vary depending on a number of factors. Perhaps the best studied among these factors is the type of stimuli used to generate compounds. One prominent hypothesis is that different generalization principles apply depending on whether the stimuli in a compound are similar or dissimilar to each other. However, the results of many experiments cannot be explained by this hypothesis. Here we propose a rational Bayesian theory of compound generalization that uses the notion of consequential regions, first developed in the context of rational theories of multidimensional generalization, to explain the effects of stimulus factors on compound generalization. The model explains a large number of results from the compound generalization literature, including the influence of stimulus modality and spatial contiguity on the summation effect, the lack of influence of stimulus factors on summation with a recovered inhibitor, the effect of spatial position of stimuli on the blocking effect, the asymmetrical generalization decrement in overshadowing and external inhibition, and the conditions leading to a reliable external inhibition effect. By integrating rational theories of compound and dimensional generalization, our model provides the first comprehensive computational account of the effects of stimulus factors on compound generalization, including spatial and temporal contiguity between components, which have posed longstanding problems for rational theories of associative and causal learning. PMID:25090430

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

PubMed Central

Young, Michael E.

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations. PMID:25202293

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

PubMed

Young, Michael E

2014-01-01

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

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

PubMed Central

Rohe, Tim; Noppeney, Uta

2015-01-01

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world. PMID:25710328

Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization.

PubMed

Yang, Jihong; Li, Zheng; Fan, Xiaohui; Cheng, Yiyu

2014-09-22

The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.

Depression and Distortion in the Attribution of Causality

ERIC Educational Resources Information Center

Rizley, Ross

1978-01-01

Two cognitive models of depression have attracted considerable attention recently: Seligman's (1975) learned helplessness model and Beck's (1967) cognitive schema approach. Describes each model and, in two studies, evaluates the assumption that depression is associated with systematic distortion in cognition regarding causal and controlling…

Supporting inquiry learning by promoting normative understanding of multivariable causality

NASA Astrophysics Data System (ADS)

Keselman, Alla

2003-11-01

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

Impaired awareness of action-outcome contingency and causality during healthy ageing and following ventromedial prefrontal cortex lesions.

PubMed

O'Callaghan, Claire; Vaghi, Matilde M; Brummerloh, Berit; Cardinal, Rudolf N; Robbins, Trevor W

2018-02-02

Detecting causal relationships between actions and their outcomes is fundamental to guiding goal-directed behaviour. The ventromedial prefrontal cortex (vmPFC) has been extensively implicated in computing these environmental contingencies, via animal lesion models and human neuroimaging. However, whether the vmPFC is critical for contingency learning, and whether it can occur without subjective awareness of those contingencies, has not been established. To address this, we measured response adaption to contingency and subjective awareness of action-outcome relationships in individuals with vmPFC lesions and healthy elderly subjects. We showed that in both vmPFC damage and ageing, successful behavioural adaptation to variations in action-outcome contingencies was maintained, but subjective awareness of these contingencies was reduced. These results highlight two contexts where performance and awareness have been dissociated, and show that learning response-outcome contingencies to guide behaviour can occur without subjective awareness. Preserved responding in the vmPFC group suggests that this region is not critical for computing action-outcome contingencies to guide behaviour. In contrast, our findings highlight a critical role for the vmPFC in supporting awareness, or metacognitive ability, during learning. We further advance the hypothesis that responding to changing environmental contingencies, whilst simultaneously maintaining conscious awareness of those statistical regularities, is a form of dual-tasking that is impaired in ageing due to reduced prefrontal function. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Neural reactivations during sleep determine network credit assignment

PubMed Central

Gulati, Tanuj; Guo, Ling; Ramanathan, Dhakshin S.; Bodepudi, Anitha; Ganguly, Karunesh

2018-01-01

A fundamental goal of motor learning is to establish neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this “credit–assignment” problem. Using neuroprosthetic learning where we could control the causal relationship between neurons and behavior, here we show that sleep–dependent processing is required for credit-assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Importantly, we found a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non–causal activity. Strikingly, decoupling of spiking to slow–oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep plays an essential role in establishing sparse activation patterns that reflect the causal neuron–behavior relationship. PMID:28692062

Exploring Causal Models of Educational Achievement.

ERIC Educational Resources Information Center

Parkerson, Jo Ann; And Others

1984-01-01

This article evaluates five causal model of educational productivity applied to learning science in a sample of 882 fifth through eighth graders. Each model explores the relationship between achievement and a combination of eight constructs: home environment, peer group, media, ability, social environment, time on task, motivation, and…

Overview of artificial neural networks.

PubMed

Zou, Jinming; Han, Yi; So, Sung-Sau

2008-01-01

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

Repeated causal decision making.

PubMed

Hagmayer, York; Meder, Björn

2013-01-01

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

Towards a Causal Model of Learned Hopelessness for Hong Kong Adolescents.

ERIC Educational Resources Information Center

Au, Chung-Park; Watkins, David

1997-01-01

Interprets a survey of junior secondary students conducted to examine the role of learned hopelessness and academic self-esteem in academic achievement. Finds that learned hopelessness and academic self-esteem play separate mediating roles between prior academic failure and academic achievement; learned hopelessness and academic self-esteem have…

Seeing Is as Good as Doing

ERIC Educational Resources Information Center

Osman, Magda

2008-01-01

Given the privileged status claimed for active learning in a variety of domains (visuomotor learning, causal induction, problem solving, education, skill learning), the present study examines whether action-based learning is a necessary, or a sufficient, means of acquiring the relevant skills needed to perform a task typically described as…

Designing Better Scaffolding in Teaching Complex Systems with Graphical Simulations

NASA Astrophysics Data System (ADS)

Li, Na

Complex systems are an important topic in science education today, but they are usually difficult for secondary-level students to learn. Although graphic simulations have many advantages in teaching complex systems, scaffolding is a critical factor for effective learning. This dissertation study was conducted around two complementary research questions on scaffolding: (1) How can we chunk and sequence learning activities in teaching complex systems? (2) How can we help students make connections among system levels across learning activities (level bridging)? With a sample of 123 seventh-graders, this study employed a 3x2 experimental design that factored sequencing methods (independent variable 1; three levels) with level-bridging scaffolding (independent variable 2; two levels) and compared the effectiveness of each combination. The study measured two dependent variables: (1) knowledge integration (i.e., integrating and connecting content-specific normative concepts and providing coherent scientific explanations); (2) understanding of the deep causal structure (i.e., being able to grasp and transfer the causal knowledge of a complex system). The study used a computer-based simulation environment as the research platform to teach the ideal gas law as a system. The ideal gas law is an emergent chemical system that has three levels: (1) experiential macro level (EM) (e.g., an aerosol can explodes when it is thrown into the fire); (2) abstract macro level (AM) (i.e., the relationships among temperature, pressure and volume); (3) micro level (Mi) (i.e., molecular activity). The sequencing methods of these levels were manipulated by changing the order in which they were delivered with three possibilities: (1) EM-AM-Mi; (2) Mi-AM-EM; (3) AM-Mi-EM. The level-bridging scaffolding variable was manipulated on two aspects: (1) inserting inter-level questions among learning activities; (2) two simulations dynamically linked in the final learning activity. Addressing the first research question, the Experiential macro-Abstract macro-Micro (EM-AM-Mi) sequencing method, following the "concrete to abstract" principle, produced better knowledge integration while the Micro-Abstract macro-Experiential macro (Mi-AM-EM) sequencing method, congruent with the causal direction of the emergent system, produced better understanding of the deep causal structure only when level-bridging scaffolding was provided. The Abstract macro-Micro-Experiential macro (AM-Mi-EM) sequencing method produced worse performance in general, because it did not follow the "concrete to abstract" principle, nor did it align with the causal structure of the emergent system. As to the second research question, the results showed that level-bridging scaffolding was important for both knowledge integration and understanding of the causal structure in learning the ideal gas law system.

Children's Causal Inferences from Conflicting Testimony and Observations

ERIC Educational Resources Information Center

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

2016-01-01

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

Foreign Language Listening Anxiety and Listening Performance: Conceptualizations and Causal Relationships

ERIC Educational Resources Information Center

Zhang, Xian

2013-01-01

This study used structural equation modeling to explore the possible causal relations between foreign language (English) listening anxiety and English listening performance. Three hundred participants learning English as a foreign language (FL) completed the foreign language listening anxiety scale (FLLAS) and IELTS test twice with an interval of…

Time and Order Effects on Causal Learning

ERIC Educational Resources Information Center

Alvarado, Angelica; Jara, Elvia; Vila, Javier; Rosas, Juan M.

2006-01-01

Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2…

Extending Attribution Theory: Considering Students' Perceived Control of the Attribution Process

ERIC Educational Resources Information Center

Fishman, Evan J.; Husman, Jenefer

2017-01-01

Research in attribution theory has shown that students' causal thinking profoundly affects their learning and motivational outcomes. Very few studies, however, have explored how students' attribution-related beliefs influence the causal thought process. The present study used the perceived control of the attribution process (PCAP) model to examine…

Learning What Works in Sensory Disabilities: Establishing Causal Inference

ERIC Educational Resources Information Center

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

2015-01-01

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

Dissociations among judgments do not reflect cognitive priority: an associative explanation of memory for frequency information in contingency learning.

PubMed

Vadillo, Miguel A; Luque, David

2013-03-01

Previous research on causal learning has usually made strong claims about the relative complexity and temporal priority of some processes over others based on evidence about dissociations between several types of judgments. In particular, it has been argued that the dissociation between causal judgments and trial-type frequency information is incompatible with the general cognitive architecture proposed by associative models. In contrast with this view, we conduct an associative analysis of this process showing that this need not be the case. We conclude that any attempt to gain a better insight on the cognitive architecture involved in contingency learning cannot rely solely on data about these dissociations.

Architecture of Explanatory Inference in the Human Prefrontal Cortex

PubMed Central

Barbey, Aron K.; Patterson, Richard

2011-01-01

Causal reasoning is a ubiquitous feature of human cognition. We continuously seek to understand, at least implicitly and often explicitly, the causal scenarios in which we live, so that we may anticipate what will come next, plan a potential response and envision its outcome, decide among possible courses of action in light of their probable outcomes, make midstream adjustments in our goal-related activities as our situation changes, and so on. A considerable body of research shows that the lateral prefrontal cortex (PFC) is crucial for causal reasoning, but also that there are significant differences in the manner in which ventrolateral PFC, dorsolateral PFC, and anterolateral PFC support causal reasoning. We propose, on the basis of research on the evolution, architecture, and functional organization of the lateral PFC, a general framework for understanding its roles in the many and varied sorts of causal reasoning carried out by human beings. Specifically, the ventrolateral PFC supports the generation of basic causal explanations and inferences; dorsolateral PFC supports the evaluation of these scenarios in light of some given normative standard (e.g., of plausibility or correctness in light of real or imagined causal interventions); and anterolateral PFC supports explanation and inference at an even higher level of complexity, coordinating the processes of generation and evaluation with further cognitive processes, and especially with computations of hedonic value and emotional implications of possible behavioral scenarios – considerations that are often critical both for understanding situations causally and for deciding about our own courses of action. PMID:21845182

Assessing Learned Helplessness in Poor Readers.

ERIC Educational Resources Information Center

Winograd, Peter; Niquette, Garland

1988-01-01

Feelings of helplessness can impact on learning to read. This research review illustrates problems in assessing learned helplessness, including instrumentation inadequacies, lack of comprehensive causal schemes, context specificity, etc. Observations of and discussions with the child are recommended in the assessment process. Guidelines for…

How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs

ERIC Educational Resources Information Center

Joksimovic, Srecko; Poquet, Oleksandra; Kovanovic, Vitomir; Dowell, Nia; Mills, Caitlin; Gaševic, Dragan; Dawson, Shane; Graesser, Arthur C.; Brooks, Christopher

2018-01-01

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis…

Inferential reasoning by exclusion in children (Homo sapiens).

PubMed

Hill, Andrew; Collier-Baker, Emma; Suddendorf, Thomas

2012-08-01

The cups task is the most widely adopted forced-choice paradigm for comparative studies of inferential reasoning by exclusion. In this task, subjects are presented with two cups, one of which has been surreptitiously baited. When the empty cup is shaken or its interior shown, it is possible to infer by exclusion that the alternative cup contains the reward. The present study extends the existing body of comparative work to include human children (Homo sapiens). Like chimpanzees (Pan troglodytes) that were tested with the same equipment and near-identical procedures, children aged three to five made apparent inferences using both visual and auditory information, although the youngest children showed the least-developed ability in the auditory modality. However, unlike chimpanzees, children of all ages used causally irrelevant information in a control test designed to examine the possibility that their apparent auditory inferences were the product of contingency learning (the duplicate cups test). Nevertheless, the children's ability to reason by exclusion was corroborated by their performance on a novel verbal disjunctive syllogism test, and we found preliminary evidence consistent with the suggestion that children used their causal-logical understanding to reason by exclusion in the cups task, but subsequently treated the duplicate cups information as symbolic or communicative, rather than causal. Implications for future comparative research are discussed. 2012 APA, all rights reserved

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

PubMed

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

PubMed Central

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. PMID:26177025

Guidelines for investigating causality of sequence variants in human disease

PubMed Central

MacArthur, D. G.; Manolio, T. A.; Dimmock, D. P.; Rehm, H. L.; Shendure, J.; Abecasis, G. R.; Adams, D. R.; Altman, R. B.; Antonarakis, S. E.; Ashley, E. A.; Barrett, J. C.; Biesecker, L. G.; Conrad, D. F.; Cooper, G. M.; Cox, N. J.; Daly, M. J.; Gerstein, M. B.; Goldstein, D. B.; Hirschhorn, J. N.; Leal, S. M.; Pennacchio, L. A.; Stamatoyannopoulos, J. A.; Sunyaev, S. R.; Valle, D.; Voight, B. F.; Winckler, W.; Gunter, C.

2014-01-01

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development. PMID:24759409

Guidelines for investigating causality of sequence variants in human disease.

PubMed

MacArthur, D G; Manolio, T A; Dimmock, D P; Rehm, H L; Shendure, J; Abecasis, G R; Adams, D R; Altman, R B; Antonarakis, S E; Ashley, E A; Barrett, J C; Biesecker, L G; Conrad, D F; Cooper, G M; Cox, N J; Daly, M J; Gerstein, M B; Goldstein, D B; Hirschhorn, J N; Leal, S M; Pennacchio, L A; Stamatoyannopoulos, J A; Sunyaev, S R; Valle, D; Voight, B F; Winckler, W; Gunter, C

2014-04-24

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

Learning effects of active involvement of secondary school students in scientific research within the Sparkling Science project "FlussAu:WOW!"

NASA Astrophysics Data System (ADS)

Poppe, Michaela; Zitek, Andreas; Scheikl, Sigrid; Heidenreich, Andrea; Kurz, Roman; Schrittwieser, Martin; Muhar, Susanne

2014-05-01

Due to immense technological and economic developments, human activities producing greenhouse gases, destructing ecosystems, changing landscapes and societies are influencing the world to such a degree, that the environment and human well-being are significantly affected. This results in a need to educate citizens towards a scientific understanding of complex socio-environmental systems. The OECD programme for international student assessment (PISA - http://www.pisa.oecd.org) investigated in detail the science competencies of 15-year-old students in 2006. The report documented that teenagers in OECD countries are mostly well aware of environmental issues but often know little about their causes or options to tackle these challenges in the future. For the integration of science with school learning and involving young people actively into scientific research Sparkling Science projects are funded by the Federal Ministry of Science and Research in Austria. Within the Sparkling Science Project "FlussAu:WOW!" (http://www.sparklingscience.at/de/projekte/574-flussau-wow-/) scientists work together with 15 to 18-year-old students of two Austrian High Schools over two years to assess the functions and processes in near natural and anthropogenically changed river floodplains. Within the first year of collaboration students, teachers and scientists elaborated on abiotic, biotic and spatial indicators for assessing and evaluating the ecological functionality of riverine systems. After a theoretical introduction students formulated research questions, hypotheses and planned and conducted field work in two different floodplain areas in Lower Austria. From the second year on, students are going to develop qualitative models on processes in river floodplain systems by means of the learning software "DynaLearn". The "DynaLearn" software is an engaging, interactive, hierarchically structured learning environment that was developed within the EU-FP7 project "DynaLearn" (http://www.dynalearn.eu) to capture and simulate qualitative causal relationships across disciplines and scales. In "FlussAu:WOW!" students work in groups of two and are guided to think about processes and interactions of hydrological, biological, ecological, spatial and societal elements within a river catchment. They can develop their own causal models and scenarios (e.g., hydrological changes in river run off due to landscape changes in the upper catchment) but also can compare their conceptions to expert models that will be provided. As main benefit, the models help students to reflect their own conceptions in the light of scientific knowledge but also scientists learn about the viewpoints and conceptions young students might have from their environment. The comparison of pre- and post-tests conducted within the "FlussAu:WOW!" project showed that students increased significantly their factual knowledge on different processes in river systems during the first year. Questions regarding functions, processes and elements of riverine landscapes were answered more extensively. This can be ascribed to students` active involvement in scientific research. However, the causal understanding still showed room for improvement, which will be tackled during the next qualitative modelling exercises. Summarizing, involvement of secondary school students in research projects is an effective means to increase scientific literacy when active participation with reflective integration are combined. Ensuring that young people are proficient in system knowledge and understanding makes it more likely that environmental and sustainable considerations are soundly addressed in the future.

The Effect of the Total Quality Management on Organizational Innovation in Higher Education Mediated by Organizational Learning

ERIC Educational Resources Information Center

Aminbeidokhti, Aliakbar; Jamshidi, Laleh; Mohammadi Hoseini, Ahmad

2016-01-01

Many scientists have suggested that both total quality management (TQM) and organizational learning can separately and effectively reinforce innovation. But is there any relationship between TQM and organizational learning? This study has two main purposes: (1) determining the causal relationship between TQM, organizational learning and…

The Globalisation of (Educational) Language rights

NASA Astrophysics Data System (ADS)

Skutnabb-Kangas, Tove

2001-07-01

Languages are today being murdered faster than ever before in human history: 90% of the world's oral languages may be dead or moribund (no longer learned by children) in a hundred years' time. The media and the educational systems are the most important direct agents in language murder. Behind them are the real culprits, the global economic, military and political systems. Linguistic human rights might be one way of promoting conflict prevention and self-determination, preventing linguistic genocide, and maintaining linguistic diversity and biodiversity (which are correlationally and also causally related). The most basic linguistic human rights for maintenance of linguistic diversity, specifically the right to mother tongue medium education, are not protected by the present provisions in human rights law. Linguistically, formal education is today often 'forcibly transferring children of one group to another group' (one of the definitions of genocide in the UN Genocide Convention). Human rights are supposed to act as correctives to the 'free market'. Despite good intentions, forces behind economic globalisation have instead given brutal market forces free range.

Learning to Understand the Forms of Causality Implicit in Scientifically Accepted Explanations

ERIC Educational Resources Information Center

Grotzer, Tina A.

2003-01-01

Considerable research illuminates the development of causal understanding. However, the research base is hardly a coherent whole. Some is based in research on children's understanding of particular science concepts. Some grows out of social psychology and considers how one attributes intentions and behaviors. Some comes from the developmental…

An Analysis of the Ontological Causal Relation in Physics and Its Educational Implications

ERIC Educational Resources Information Center

Cheong, Yong Wook

2016-01-01

An ontological causal relation is a quantified relation between certain interactions and changes in corresponding properties. Key ideas in physics, such as Newton's second law and the first law of thermodynamics, are representative examples of these relations. In connection with the teaching and learning of these relations, this study investigated…

Predicting Teacher Value-Added Results in Non-Tested Subjects Based on Confounding Variables: A Multinomial Logistic Regression

ERIC Educational Resources Information Center

Street, Nathan Lee

2017-01-01

Teacher value-added measures (VAM) are designed to provide information regarding teachers' causal impact on the academic growth of students while controlling for exogenous variables. While some researchers contend VAMs successfully and authentically measure teacher causality on learning, others suggest VAMs cannot adequately control for exogenous…

Within-Teacher Variation of Causal Attributions of Low Achieving Students

ERIC Educational Resources Information Center

Jager, Lieke; Denessen, Eddie

2015-01-01

In teacher research, causal attributions of low achievement have been proven to be predictive of teachers' efforts to provide optimal learning contexts for all students. In most studies, however, attributions have been studied as a between-teacher variable rather than a within-teacher variable assuming that teachers' responses to low achievement…

The construction of causal schemes: learning mechanisms at the knowledge level.

PubMed

diSessa, Andrea A

2014-06-01

This work uses microgenetic study of classroom learning to illuminate (1) the role of pre-instructional student knowledge in the construction of normative scientific knowledge, and (2) the learning mechanisms that drive change. Three enactments of an instructional sequence designed to lead to a scientific understanding of thermal equilibration are used as data sources. Only data from a scaffolded student inquiry preceding introduction of a normative model were used. Hence, the study involves nearly autonomous student learning. In two classes, students developed stable and socially shared explanations ("causal schemes") for understanding thermal equilibration. One case resulted in a near-normative understanding, while the other resulted in a non-normative "alternative conception." The near-normative case seems to be a particularly clear example wherein the constructed causal scheme is a composition of previously documented naïve conceptions. Detailed prior description of these naive elements allows a much better than usual view of the corresponding details of change during construction of the new scheme. A list of candidate mechanisms that can account for observed change is presented. The non-normative construction seems also to be a composition, albeit of a different structural form, using a different (although similar) set of naïve elements. This article provides one of very few high-resolution process analyses showing the productive use of naïve knowledge in learning. © 2014 Cognitive Science Society, Inc.

Bayes and blickets: Effects of knowledge on causal induction in children and adults

PubMed Central

Griffiths, Thomas L.; Sobel, David M.; Tenenbaum, Joshua B.; Gopnik, Alison

2011-01-01

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. PMID:21972897

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

PubMed

Sella, Nadir; Verny, Louis; Uguzzoni, Guido; Affeldt, Séverine; Isambert, Hervé

2018-07-01

We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. MIIC online can be freely accessed at https://miic.curie.fr. Supplementary data are available at Bioinformatics online.

Causal discovery in the geosciences-Using synthetic data to learn how to interpret results

NASA Astrophysics Data System (ADS)

Ebert-Uphoff, Imme; Deng, Yi

2017-02-01

Causal discovery algorithms based on probabilistic graphical models have recently emerged in geoscience applications for the identification and visualization of dynamical processes. The key idea is to learn the structure of a graphical model from observed spatio-temporal data, thus finding pathways of interactions in the observed physical system. Studying those pathways allows geoscientists to learn subtle details about the underlying dynamical mechanisms governing our planet. Initial studies using this approach on real-world atmospheric data have shown great potential for scientific discovery. However, in these initial studies no ground truth was available, so that the resulting graphs have been evaluated only by whether a domain expert thinks they seemed physically plausible. The lack of ground truth is a typical problem when using causal discovery in the geosciences. Furthermore, while most of the connections found by this method match domain knowledge, we encountered one type of connection for which no explanation was found. To address both of these issues we developed a simulation framework that generates synthetic data of typical atmospheric processes (advection and diffusion). Applying the causal discovery algorithm to the synthetic data allowed us (1) to develop a better understanding of how these physical processes appear in the resulting connectivity graphs, and thus how to better interpret such connectivity graphs when obtained from real-world data; (2) to solve the mystery of the previously unexplained connections.

Can model-free reinforcement learning explain deontological moral judgments?

PubMed

Ayars, Alisabeth

2016-05-01

Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account-e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework. Copyright © 2016 Elsevier B.V. All rights reserved.

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

PubMed

Perales, José C; Shanks, David R

2003-08-01

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

Children's Concepts of How People Get Babies

ERIC Educational Resources Information Center

Bernstein, Anne C.; Cowan, Philip A.

1975-01-01

Twenty children, 3-12 years old, were given a newly constructed interview on their concepts of human reproduction (social causality), in conjunction with Piaget-type tasks assessing physical conservation-identity, physical causality, and a new social identity task. The children's concepts of human reproduction appeared to proceed through a…

Non-Western students' causal reasoning about biologically adaptive changes in humans, other animals and plants: instructional and curricular implications

NASA Astrophysics Data System (ADS)

Mbajiorgu, Ngozika; Anidu, Innocent

2017-06-01

Senior secondary school students (N = 360), 14- to 18-year-olds, from the Igbo culture of eastern Nigeria responded to a questionnaire requiring them to give causal explanations of biologically adaptive changes in humans, other animals and plants. A student subsample (n = 36) was, subsequently, selected for in-depth interviews. Significant differences were found between prompts within the prompt categories, suggesting item feature effects. However, the most coherent pattern was found within the plant category as patterns differed for the mechanistic proximate (MP) reasoning category only. Patterns also differed highly significantly between the prompt categories, with patterns for teleology, MP, mechanistic ultimate and don't know categories similar for plants and other animals but different for the human category. Both urban and rural students recognise commonalities in causality between the three prompt categories, in that their preferences for causal explanations were similar across four reasoning categories. The rural students, however, were more likely than their urban counterparts to give multiple causal explanations in the span of a single response and less likely to attribute causal agency to God. Two factors, religious belief and language, for all the students; and one factor, ecological closeness to nature, for rural students were suspected to have produced these patterns.

Action, outcome, and value: a dual-system framework for morality.

PubMed

Cushman, Fiery

2013-08-01

Dual-system approaches to psychology explain the fundamental properties of human judgment, decision making, and behavior across diverse domains. Yet, the appropriate characterization of each system is a source of debate. For instance, a large body of research on moral psychology makes use of the contrast between "emotional" and "rational/cognitive" processes, yet even the chief proponents of this division recognize its shortcomings. Largely independently, research in the computational neurosciences has identified a broad division between two algorithms for learning and choice derived from formal models of reinforcement learning. One assigns value to actions intrinsically based on past experience, while another derives representations of value from an internally represented causal model of the world. This division between action- and outcome-based value representation provides an ideal framework for a dual-system theory in the moral domain.

Working memory training promotes general cognitive abilities in genetically heterogeneous mice.

PubMed

Light, Kenneth R; Kolata, Stefan; Wass, Christopher; Denman-Brice, Alexander; Zagalsky, Ryan; Matzel, Louis D

2010-04-27

In both humans and mice, the efficacy of working memory capacity and its related process, selective attention, are each strongly predictive of individuals' aggregate performance in cognitive test batteries [1-9]. Because working memory is taxed during most cognitive tasks, the efficacy of working memory may have a causal influence on individuals' performance on tests of "intelligence" [10, 11]. Despite the attention this has received, supporting evidence has been largely correlational in nature (but see [12]). Here, genetically heterogeneous mice were assessed on a battery of five learning tasks. Animals' aggregate performance across the tasks was used to estimate their general cognitive abilities, a trait that is in some respects analogous to intelligence [13, 14]. Working memory training promoted an increase in animals' selective attention and their aggregate performance on these tasks. This enhancement of general cognitive performance by working memory training was attenuated if its selective attention demands were reduced. These results provide evidence that the efficacy of working memory capacity and selective attention may be causally related to an animal's general cognitive performance and provide a framework for behavioral strategies to promote those abilities. Furthermore, the pattern of behavior reported here reflects a conservation of the processes that regulate general cognitive performance in humans and infrahuman animals. Copyright © 2010 Elsevier Ltd. All rights reserved.

Newly postulated neurodevelopmental risks of pediatric anesthesia.

PubMed

Hays, Stephen R; Deshpande, Jayant K

2011-04-01

Recent animal and human studies have raised concern that exposure to anesthetic agents in children may cause neuronal damage and be associated with adverse neurodevelopmental outcomes. Exposure of young animals to anesthetic agents above threshold doses and durations during a critical neurodevelopmental window in the absence of concomitant painful stimuli causes widespread neuronal apoptosis and subsequent abnormal behaviors. The relevance of such animal data to humans is unknown. Untreated neonatal pain and stress also are associated with enhanced neuronal death and subsequent maladaptive behaviors, which can be prevented by exposure to these same anesthetic agents. Retrospective observational human studies have suggested a dose-dependent association between multiple anesthetic exposures in early childhood and subsequent learning disability, the causality of which is unknown. Ongoing prospective investigations are underway, the results of which may clarify if and what neurodevelopmental risks are associated with pediatric anesthesia. No change in current practice is yet indicated.

Performance in Object-Choice Aesop's Fable Tasks Are Influenced by Object Biases in New Caledonian Crows but not in Human Children.

PubMed

Miller, Rachael; Jelbert, Sarah A; Taylor, Alex H; Cheke, Lucy G; Gray, Russell D; Loissel, Elsa; Clayton, Nicola S

2016-01-01

The ability to reason about causality underlies key aspects of human cognition, but the extent to which non-humans understand causality is still largely unknown. The Aesop's Fable paradigm, where objects are inserted into water-filled tubes to obtain out-of-reach rewards, has been used to test casual reasoning in birds and children. However, success on these tasks may be influenced by other factors, specifically, object preferences present prior to testing or arising during pre-test stone-dropping training. Here, we assessed this 'object-bias' hypothesis by giving New Caledonian crows and 5-10 year old children two object-choice Aesop's Fable experiments: sinking vs. floating objects, and solid vs. hollow objects. Before each test, we assessed subjects' object preferences and/or trained them to prefer the alternative object. Both crows and children showed pre-test object preferences, suggesting that birds in previous Aesop's Fable studies may also have had initial preferences for objects that proved to be functional on test. After training to prefer the non-functional object, crows, but not children, performed more poorly on these two object-choice Aesop's Fable tasks than subjects in previous studies. Crows dropped the non-functional objects into the tube on their first trials, indicating that, unlike many children, they do not appear to have an a priori understanding of water displacement. Alternatively, issues with inhibition could explain their performance. The crows did, however, learn to solve the tasks over time. We tested crows further to determine whether their eventual success was based on learning about the functional properties of the objects, or associating dropping the functional object with reward. Crows inserted significantly more rewarded, non-functional objects than non-rewarded, functional objects. These findings suggest that the ability of New Caledonian crows to produce performances rivaling those of young children on object-choice Aesop's Fable tasks is partly due to pre-existing object preferences.

Reflections of a Vaccinologist: Lessons Learned About What We Can Do to Improve Trust in Vaccines and Vaccine Programsa.

PubMed

Halsey, Neal A

2017-03-01

Public trust can be improved by learning from past mistakes, by establishing a standing forum for review of new concerns as they arise, and by maintaining a robust vaccine safety system. Developing standard guidelines for reporting causality assessment in case reports would help educate physicians and prevent future unnecessary concerns based on false assumptions of causal relationships. © The Author 2015. Published by Oxford University Press on behalf of The Journal of the Pediatric Infectious Diseases Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Phonological and Semantic Knowledge Are Causal Influences on Learning to Read Words in Chinese

ERIC Educational Resources Information Center

Zhou, Lulin; Duff, Fiona J.; Hulme, Charles

2015-01-01

We report a training study that assesses whether teaching the pronunciation and meaning of spoken words improves Chinese children's subsequent attempts to learn to read the words. Teaching the pronunciations of words helps children to learn to read those same words, and teaching the pronunciations and meanings improves learning still further.…

The Academic Procrastination in Junior High School Students' Mathematics Learning: A Qualitative Study

ERIC Educational Resources Information Center

Asri, Dahlia Novarianing; Setyosari, Punaji; Hitipeuw, Imanuel; Chusniyah, Tutut

2017-01-01

Among the main causes of low learning achievement in mathematics learning is a delayed behavior to do tasks, commonly called academic procrastination. The objectives of this research are to describe and to explain the causal factors and consequences of academic procrastination in learning mathematics for junior high school students. This research…

Life Strain, Social Control, Social Learning, and Delinquency: The Effects of Gender, Age, and Family SES Among Chinese Adolescents.

PubMed

Bao, Wan-Ning; Haas, Ain; Xie, Yunping

2016-09-01

Very few studies have examined the pathways to delinquency and causal factors for demographic subgroups of adolescents in a different culture. This article explores the effects of gender, age, and family socioeconomic status (SES) in an integrated model of strain, social control, social learning, and delinquency among a sample of Chinese adolescents. ANOVA is used to check for significant differences between categories of demographic groups on the variables in the integrated model, and the differential effects of causal factors in the theoretical path models are examined. Further tests of interaction effects are conducted to compare path coefficients between "high-risk" youths (i.e., male, mid-teen, and low family SES adolescents) and other subgroups. The findings identified similar pathways to delinquency across subgroups and clarified the salience of causal factors for male, mid-teen, and low SES adolescents in a different cultural context. © The Author(s) 2015.

Regression Discontinuity Design: A Guide for Strengthening Causal Inference in HRD

ERIC Educational Resources Information Center

Chambers, Silvana

2016-01-01

Purpose: Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about…

A biologically inspired neural model for visual and proprioceptive integration including sensory training.

PubMed

Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

2013-12-01

Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model simulation.

Calculating and Understanding: Formal Models and Causal Explanations in Science, Common Reasoning and Physics Teaching

ERIC Educational Resources Information Center

Besson, Ugo

2010-01-01

This paper presents an analysis of the different types of reasoning and physical explanation used in science, common thought, and physics teaching. It then reflects on the learning difficulties connected with these various approaches, and suggests some possible didactic strategies. Although causal reasoning occurs very frequently in common thought…

When Russians Learn English: How the Semantics of Causation May Change

ERIC Educational Resources Information Center

Wolff, Phillip; Ventura, Tatyana

2009-01-01

We examined how the semantics of causal expressions in Russian and English might differ and how these differences might lead to changes in the way second language learners understand causal expressions in their first language. According to the dynamics model of causation (Wolff, 2007), expressions of causation based on CAUSE verbs (make, force)…

Testing Causal Impacts of a School-Based SEL Intervention Using Instrumental Variable Techniques

ERIC Educational Resources Information Center

Torrente, Catalina; Nathanson, Lori; Rivers, Susan; Brackett, Marc

2015-01-01

Children's social-emotional skills, such as conflict resolution and emotion regulation, have been linked to a number of highly regarded academic and social outcomes. The current study presents preliminary results from a causal test of the theory of change of RULER, a universal school-based approach to social and emotional learning (SEL).…

Causal Imprinting in Causal Structure Learning

ERIC Educational Resources Information Center

Taylor, Eric G.; Ahn, Woo-kyoung

2012-01-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C…

Hypergraphics for History Teaching--Barriers for Causal Reasoning about History Accounts

ERIC Educational Resources Information Center

Mendez, Jose M.; Montanero, Manuel

2008-01-01

This paper investigates the uses of various kinds of hypermedia format for history learning, which specifically emphasizes on the role of causal reasoning about history accounts. Three different groups in the last school year of Secondary Education (aged 16) studied the same materials about the Discovery of America in three different formats: (a)…

[The relevance of ethology for psychiatry].

PubMed

Brüne, M

1998-07-01

Darwin's evolutionary theory was the starting point for ethology, associated with an impact on scientific psychiatry. Psychiatry and ethology have common scientific and methodological prerequisites: inductive and deductive methods and "gestalt theory" as a basis for observing and describing behaviour patterns with subsequent causal analysis. There have been early endeavours to anchor ethological thinking in psychiatry but this tendency did not prevail for the following reasons: on the one hand, the methodology of ethology was immature or not applicable to man, whereas on the other hand the dominating experiential phenomenological school of Karl Jaspers and Kurt Schneider stressed the privileged position of human thinking, perception, and feeling. These fundamental categories of human existence did not appear amenable to any causal ethological analysis. Psychiatry and evolutionary biology were linked in an atrocious manner during the Nazi regime, both being abused for propaganda purposes and genocide. More recently, there is a "reconciliation" of both disciplines. In psychiatric nosology, operational, behaviour-oriented diagnostic systems have been introduced; ethology has opened up for theories of learning; new subsections like human ethology and sociobiology have evolved. The seeming incompatibility of (behavioural) biological psychiatry and experiential phenomenological psychopathology may be overcome on the basis of Konrad Lorenz' evolutionary epistemology. The functional analysis of human feeling and behaviour in psychotic disorders on the basis of Jackson's theory of the evolution and dissolution of the nervous system may serve as an example. The significance of an "ethological psychiatry" for diagnostic and therapeutical processes of psychiatric disorders derive from prognostic possibilities and the analysis of non-verbal communication in therapist-patient-interactions, but have not yet been systematically investigated.

Understanding the Causal Path between Action, Learning, and Solutions: Maximizing the Power of Action Learning to Achieve Great Results

ERIC Educational Resources Information Center

Leonard, H. Skipton

2015-01-01

Clients and practitioners alike are often confused about the ultimate purpose of action learning (AL). Because of the title of the method, many believe the primary goal of AL is to generate learning. This article clarifies the relationship between action, learning, and solutions. It also provides historical evidence to support the conclusion that…

Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons.

PubMed

Keysers, Christian; Perrett, David I; Gazzola, Valeria

2014-04-01

Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

An architecture for an autonomous learning robot

NASA Technical Reports Server (NTRS)

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.

Flood regimes in a changing world: What do we know?

NASA Astrophysics Data System (ADS)

Bloeschl, G.

2015-12-01

There has been a surprisingly large number of major floods in the last years around the world which suggests that floods may have increased and will continue to increase in the next decades. However, the realism of such changes is still hotly discussed in the literature. In this presentation I will argue that a fresh look is needed at the flood change problem in terms of the causal factors including river training, land use changes and climate variability. Analysing spatial patterns of dynamic flood characteristics helps learn form the rich diversity of flood processes across the landscape. I will present a number of examples across Europe to illustrate the range of flood generation processes and the causal factors of changes in the flood regime. On the basis of these examples, I will demonstrate how comparative hydrology can assist in learning from the differences of flood characteristics between catchments both for present and future conditions. Focus on the interactions of the natural and human water system will be instrumental in making meaningful statements about future floods in a changing world. References Hall et al. (2014) Understanding Flood Regime Changes in Europe: A state of the art assessment. Hydrol. Earth Sys. Sc., 18, 2735-2772. Blöschl et al. (2015) Increasing river floods: fiction or reality? Wiley Interdisciplinary Reviews: Water. doi: 10.1002/wat2.1079

Dopamine prediction errors in reward learning and addiction: from theory to neural circuitry

PubMed Central

Keiflin, Ronald; Janak, Patricia H.

2015-01-01

Summary Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error-signaling and addiction can be formulated and tested. PMID:26494275

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

PubMed

Varshney, Kush R; Alemzadeh, Homa

2017-09-01

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. We discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

Young children overimitate in third-party contexts.

PubMed

Nielsen, Mark; Moore, Chris; Mohamedally, Jumana

2012-05-01

The exhibition of actions that are causally unnecessary to the outcomes with which they are associated is a core feature of human cultural behavior. To enter into the world(s) of their cultural in-group, children must learn to assimilate such unnecessary actions into their own behavioral repertoire. Past research has established the habitual tendency of children to adopt the redundant actions of adults demonstrated directly to them. Here we document how young children will do so even when such actions are modeled to a third person regardless of whether children are presented with the test apparatus by the demonstrating, and assumedly expert, adult or by the observing, and assumedly naive, adult (Experiment 1), whether or not children had opportunity to discover how the apparatus works prior to modeling (Experiment 1), and whether or not children's attention was drawn to the demonstration while they were otherwise occupied (Experiment 2). These results emphasize human children's readiness to acquire behavior that is in keeping with what others do, regardless of the apparent efficiency of the actions employed, and in so doing to participate in cultural learning. Copyright © 2012 Elsevier Inc. All rights reserved.

STAMP-Based HRA Considering Causality Within a Sociotechnical System: A Case of Minuteman III Missile Accident.

PubMed

Rong, Hao; Tian, Jin

2015-05-01

The study contributes to human reliability analysis (HRA) by proposing a method that focuses more on human error causality within a sociotechnical system, illustrating its rationality and feasibility by using a case of the Minuteman (MM) III missile accident. Due to the complexity and dynamics within a sociotechnical system, previous analyses of accidents involving human and organizational factors clearly demonstrated that the methods using a sequential accident model are inadequate to analyze human error within a sociotechnical system. System-theoretic accident model and processes (STAMP) was used to develop a universal framework of human error causal analysis. To elaborate the causal relationships and demonstrate the dynamics of human error, system dynamics (SD) modeling was conducted based on the framework. A total of 41 contributing factors, categorized into four types of human error, were identified through the STAMP-based analysis. All factors are related to a broad view of sociotechnical systems, and more comprehensive than the causation presented in the accident investigation report issued officially. Recommendations regarding both technical and managerial improvement for a lower risk of the accident are proposed. The interests of an interdisciplinary approach provide complementary support between system safety and human factors. The integrated method based on STAMP and SD model contributes to HRA effectively. The proposed method will be beneficial to HRA, risk assessment, and control of the MM III operating process, as well as other sociotechnical systems. © 2014, Human Factors and Ergonomics Society.

Volitional control of the anterior insula in criminal psychopaths using real-time fMRI neurofeedback: a pilot study

PubMed Central

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman; Ruiz, Sergio; Birbaumer, Niels

2014-01-01

This pilot study aimed to explore whether criminal psychopaths can learn volitional regulation of the left anterior insula with real-time fMRI neurofeedback. Our previous studies with healthy volunteers showed that learned control of the blood oxygenation-level dependent (BOLD) signal was specific to the target region, and not a result of general arousal and global unspecific brain activation, and also that successful regulation modulates emotional responses, specifically to aversive picture stimuli but not neutral stimuli. In this pilot study, four criminal psychopaths were trained to regulate the anterior insula by employing negative emotional imageries taken from previous episodes in their lives, in conjunction with contingent feedback. Only one out of the four participants learned to increase the percent differential BOLD in the up-regulation condition across training runs. Subjects with higher Psychopathic Checklist-Revised (PCL:SV) scores were less able to increase the BOLD signal in the anterior insula than their lower PCL:SV counterparts. We investigated functional connectivity changes in the emotional network due to learned regulation of the successful participant, by employing multivariate Granger Causality Modeling (GCM). Learning to up-regulate the left anterior insula not only increased the number of connections (causal density) in the emotional network in the single successful participant but also increased the difference between the number of outgoing and incoming connections (causal flow) of the left insula. This pilot study shows modest potential for training psychopathic individuals to learn to control brain activity in the anterior insula. PMID:25352793

Using the Aesop's fable paradigm to investigate causal understanding of water displacement by New Caledonian crows.

PubMed

Jelbert, Sarah A; Taylor, Alex H; Cheke, Lucy G; Clayton, Nicola S; Gray, Russell D

2014-01-01

Understanding causal regularities in the world is a key feature of human cognition. However, the extent to which non-human animals are capable of causal understanding is not well understood. Here, we used the Aesop's fable paradigm--in which subjects drop stones into water to raise the water level and obtain an out of reach reward--to assess New Caledonian crows' causal understanding of water displacement. We found that crows preferentially dropped stones into a water-filled tube instead of a sand-filled tube; they dropped sinking objects rather than floating objects; solid objects rather than hollow objects, and they dropped objects into a tube with a high water level rather than a low one. However, they failed two more challenging tasks which required them to attend to the width of the tube, and to counter-intuitive causal cues in a U-shaped apparatus. Our results indicate that New Caledonian crows possess a sophisticated, but incomplete, understanding of the causal properties of displacement, rivalling that of 5-7 year old children.

Using the Aesop's Fable Paradigm to Investigate Causal Understanding of Water Displacement by New Caledonian Crows

PubMed Central

Jelbert, Sarah A.; Taylor, Alex H.; Cheke, Lucy G.; Clayton, Nicola S.; Gray, Russell D.

2014-01-01

Understanding causal regularities in the world is a key feature of human cognition. However, the extent to which non-human animals are capable of causal understanding is not well understood. Here, we used the Aesop's fable paradigm – in which subjects drop stones into water to raise the water level and obtain an out of reach reward – to assess New Caledonian crows' causal understanding of water displacement. We found that crows preferentially dropped stones into a water-filled tube instead of a sand-filled tube; they dropped sinking objects rather than floating objects; solid objects rather than hollow objects, and they dropped objects into a tube with a high water level rather than a low one. However, they failed two more challenging tasks which required them to attend to the width of the tube, and to counter-intuitive causal cues in a U-shaped apparatus. Our results indicate that New Caledonian crows possess a sophisticated, but incomplete, understanding of the causal properties of displacement, rivalling that of 5–7 year old children. PMID:24671252

The Predictive Power of Fifth Graders' Learning Styles on Their Mathematical Reasoning and Spatial Ability

ERIC Educational Resources Information Center

Danisman, Sahin; Erginer, Ergin

2017-01-01

The purpose of this study was to examine fifth graders' mathematical reasoning and spatial ability, to identify a correlation with their learning styles, and to determine the predictive power of their learning styles on their mathematical learning profiles. This causal study was conducted with 97 fifth graders (60 females, 61.9% and 37 males,…

From animal cruelty to serial murder: applying the graduation hypothesis.

PubMed

Wright, Jeremy; Hensley, Christopher

2003-02-01

Although serial murder has been recorded for centuries, limited academic attention has been given to this important topic. Scholars have attempted to examine the causality and motivations behind the rare phenomenon of serial murder. However, scant research exists which delves into the childhood characteristics of serial murderers. Using social learning theory, some of these studies present supporting evidence for a link between childhood animal cruelty and adult aggression toward humans. Based on five case studies of serial murderers, we contribute to the existing literature by exploring the possible link between childhood cruelty toward animals and serial murder with the application of the graduation hypothesis.

Aging and Integration of Contingency Evidence in Causal Judgment

PubMed Central

Mutter, Sharon A.; Plumlee, Leslie F.

2009-01-01

Age differences in causal judgment are consistently greater for preventative/negative relationships than for generative/positive relationships. We used a feature analytic procedure (Mandel & Lehman, 1998) to determine whether this effect might be due to differences in young and older adults’ integration of contingency evidence during causal induction. To reduce the impact of age-related changes in learning/memory we presented contingency evidence for preventative, non-contingent, and generative relationships in summary form and to induce participants to integrate greater or lesser amounts of this evidence, we varied the meaningfulness of the causal context. Young adults showed greater flexibility in their integration processes than older adults. In an abstract causal context, there were no age differences in causal judgment or integration, but in meaningful contexts, young adults’ judgments for preventative relationships were more accurate than older adults’ and they assigned more weight to the contingency evidence confirming these relationships. These differences were mediated by age-related changes in processing speed. The decline in this basic cognitive resource may place boundaries on the amount or the type of evidence that older adults can integrate for causal judgment. PMID:20025406

How Does Anxiety Affect Second Language Learning? A Reply to Sparks and Ganschow.

ERIC Educational Resources Information Center

MacIntyre, Peter D.

1995-01-01

Advocates that language anxiety can play a significant causal role in creating individual differences in both language learning and communication. This paper studies the role of anxiety in the language learning process and concludes that the linguistic coding deficit hypothesis errs in assigning epiphenomenal status to language anxiety. (57…

Parents and Siblings As Early Resources for Young Children's Learning in Mexican-Descent Families.

ERIC Educational Resources Information Center

Perez-Granados, Deanne R.; Callanan, Maureen A.

1997-01-01

Interviews with parents from 50 Mexican-descent families revealed that parents encouraged their preschool children to ask questions about science and causal relationships; older and younger siblings learned different skills from one another; and children learned through observation and imitation. Discusses issues of "match" between home…

The Effect of Teaching Experience on Service-Learning Beliefs of Dental Hygiene Educators

ERIC Educational Resources Information Center

Burch, Sharlee Shirley

2013-01-01

The purpose of this non-experimental causal-comparative study was to determine if service-learning teaching experience affects dental hygiene faculty perceptions of service-learning benefits and barriers in the United States. Dental hygiene educators from entry-level dental hygiene education programs in the United States completed the Web-based…

Integrating User Interface and Personal Innovativeness into the TAM for Mobile Learning in Cyber University

ERIC Educational Resources Information Center

Joo, Young Ju; Lee, Hyeon Woo; Ham, Yookyoung

2014-01-01

This study aims to add new variables, namely user interface, personal innovativeness, and satisfaction in learning, to Davis's technology acceptance model and also examine whether learners are willing to adopt mobile learning. Thus, this study attempted to explain the structural causal relationships among user interface, personal…

Social Learning, Reinforcement and Crime: Evidence from Three European Cities

ERIC Educational Resources Information Center

Tittle, Charles R.; Antonaccio, Olena; Botchkovar, Ekaterina

2012-01-01

This study reports a cross-cultural test of Social Learning Theory using direct measures of social learning constructs and focusing on the causal structure implied by the theory. Overall, the results strongly confirm the main thrust of the theory. Prior criminal reinforcement and current crime-favorable definitions are highly related in all three…

Magnetic stimulation of visual cortex impairs perceptual learning.

PubMed

Baldassarre, Antonello; Capotosto, Paolo; Committeri, Giorgia; Corbetta, Maurizio

2016-12-01

The ability to learn and process visual stimuli more efficiently is important for survival. Previous neuroimaging studies have shown that perceptual learning on a shape identification task differently modulates activity in both frontal-parietal cortical regions and visual cortex (Sigman et al., 2005;Lewis et al., 2009). Specifically, fronto-parietal regions (i.e. intra parietal sulcus, pIPS) became less activated for trained as compared to untrained stimuli, while visual regions (i.e. V2d/V3 and LO) exhibited higher activation for familiar shape. Here, after the intensive training, we employed transcranial magnetic stimulation over both visual occipital and parietal regions, previously shown to be modulated, to investigate their causal role in learning the shape identification task. We report that interference with V2d/V3 and LO increased reaction times to learned stimuli as compared to pIPS and Sham control condition. Moreover, the impairment observed after stimulation over the two visual regions was positive correlated. These results strongly support the causal role of the visual network in the control of the perceptual learning. Copyright © 2016 Elsevier Inc. All rights reserved.

The scope and limits of overimitation in the transmission of artefact culture

PubMed Central

Lyons, Derek E.; Damrosch, Diana H.; Lin, Jennifer K.; Macris, Deanna M.; Keil, Frank C.

2011-01-01

Children are generally masterful imitators, both rational and flexible in their reproduction of others' actions. After observing an adult operating an unfamiliar object, however, young children will frequently overimitate, reproducing not only the actions that were causally necessary but also those that were clearly superfluous. Why does overimitation occur? We argue that when children observe an adult intentionally acting on a novel object, they may automatically encode all of the adult's actions as causally meaningful. This process of automatic causal encoding (ACE) would generally guide children to accurate beliefs about even highly opaque objects. In situations where some of an adult's intentional actions were unnecessary, however, it would also lead to persistent overimitation. Here, we undertake a thorough examination of the ACE hypothesis, reviewing prior evidence and offering three new experiments to further test the theory. We show that children will persist in overimitating even when doing so is costly (underscoring the involuntary nature of the effect), but also that the effect is constrained by intentionality in a manner consistent with its posited learning function. Overimitation may illuminate not only the structure of children's causal understanding, but also the social learning processes that support our species' artefact-centric culture. PMID:21357238

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

PubMed

White, Peter A

2009-09-01

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

What Does Quantum Physics Have to Do with Behavior Disorders?

ERIC Educational Resources Information Center

Center, David B.

This paper argues that human agency as a causal factor in behavior must be considered in any model of behavior and behavior disorders. Since human agency is historically tied to the issue of consciousness, to argue that consciousness plays a causal role in behavior requires a plausible explanation of consciousness. This paper proposes that…

Hebbian Learning is about contingency not contiguity and explains the emergence of predictive mirror neurons

PubMed Central

Keysers, Christian; Perrett, David I.; Gazzola, Valeria

2015-01-01

Hebbian Learning should not be reduced to contiguity since it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: through Hebbian Learning mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization. PMID:24775162

The influence of the number of relevant causes on the processing of covariation information in causal reasoning.

PubMed

Kim, Kyungil; Markman, Arthur B; Kim, Tae Hoon

2016-11-01

Research on causal reasoning has focused on the influence of covariation between candidate causes and effects on causal judgments. We suggest that the type of covariation information to which people attend is affected by the task being performed. For this, we manipulated the test questions for the evaluation of contingency information and observed its influence on both contingency learning and subsequent causal selections. When people select one cause related to an effect, they focus on conditional contingencies assuming the absence of alternative causes. When people select two causes related to an effect, they focus on conditional contingencies assuming the presence of alternative causes. We demonstrated this use of contingency information in four experiments.

SAN-RL: combining spreading activation networks and reinforcement learning to learn configurable behaviors

NASA Technical Reports Server (NTRS)

White, J.; Gaines, D. M.; Wilkes, M.; Kusumalnukool, K.; Thongchai, S.; Kawamura, K.

2001-01-01

This approach provides the agent with a causal structure, the spreading activation network, relating goals to the actions that can achieve those goals. This enables the agent to select actions relative to the goal priorities.

Dietary enrichment counteracts age-associated cognitive dysfunction in canines.

PubMed

Milgram, N W; Zicker, S C; Head, E; Muggenburg, B A; Murphey, H; Ikeda-Douglas, C J; Cotman, C W

2002-01-01

Advanced age is accompanied by cognitive decline indicative of central nervous system dysfunction. One possibly critical causal factor is oxidative stress. Accordingly, we studied the effects of dietary antioxidants and age in a canine model of aging that parallels the key features of cognitive decline and neuropathology in humans. Old and young animals were placed on either a standard control food, or a food enriched with a broad spectrum of antioxidants and mitochondrial enzymatic cofactors. After 6 months of treatment, the animals were tested on four increasingly difficult oddity discrimination learning problems. The old animals learned more slowly than the young, making significantly more errors. However, this age-associated decline was reduced in the animals fed the enriched food, particularly on the more difficult tasks. These results indicate that maintenance on foods fortified with complex mixtures of antioxidants can partially counteract the deleterious effects of aging on cognition. Copyright 2002 Elsevier Science Inc.

The association between human papillomavirus and oropharyngeal squamous cell Carcinoma: Reviewed according to the Bradford Hill criteria for causality.

PubMed

Walvik, Lena; Svensson, Amanda Björk; Friborg, Jeppe; Lajer, Christel Bræmer

2016-12-01

There is emerging evidence of the association between human papillomavirus and a subset of head and neck cancers. However, the role of human papillomavirus as a causal factor is still debated. This review addresses the association between human papillomavirus and oropharyngeal squamous cell carcinoma using the Bradford Hill criteria. The strength of the association is supported by, detection of human papillomavirus infection and antibodies prior to oropharyngeal squamous cell carcinoma. This is furthermore reinforced by the absence of human papillomavirus DNA in healthy tonsils. The association is geographically consistent throughout the economically developed world. The presence and integration of high-risk human papillomavirus genome in tonsillar tumours, and expression of viral oncogenes, are specific and plausible. Analogous to human papillomavirus in cervical cancer, the rising incidence in human papillomavirus positive oropharyngeal squamous cell carcinoma is associated with sexual behaviour. These associations have been repeatedly observed and are in accordance with our current knowledge. The time relation between cause and effect remains the main challenge, due to the lack of well-defined premalignant lesions. However, a causal relationship between human papillomavirus infection and oropharyngeal squamous cell carcinoma seems evident. Copyright © 2016 Elsevier Ltd. All rights reserved.

"Model age-based" and "copy when uncertain" biases in children's social learning of a novel task.

PubMed

Wood, Lara A; Harrison, Rachel A; Lucas, Amanda J; McGuigan, Nicola; Burdett, Emily R R; Whiten, Andrew

2016-10-01

Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N=140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children's continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of model. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Nitric Oxide and the Biological Cascades Underlying Increased Neurogenesis, Enhanced Learning Ability, and Academic Ability as an Effect of Increased Bouts of Physical Activity

PubMed Central

HUNT, SAMUEL J.; NAVALTA, JAMES W.

2012-01-01

The consummate principle underlying all physiological research is corporeal adaptation at every level of the organism observed. With respect to humans, the body learns to function based on the external stimuli from the environment, beginning in the womb, throughout the developmental stages of life. Nitric Oxide (NO) appears to be the governor of the plasticity of several systems in mammals implicit in their proper development. It is the purpose of this review to describe the physiological pathways that lead to plasticity of not only the vasculature but also of the brain and how physical activity plays a key role in those alterations by initiating the mechanism that triggers NO production. Further, this review hopes to show a connection between these changes and learning, comprising both motor learning and cognitive learning. This review will show how NO plays a significant role in vascularization and neurogenesis, necessary to enhance the mind-body connection and comprehensive physical performance and adaptation. It is our belief that this review effectively demonstrates, using a multidisciplinary approach, the causal mechanisms underlying the increases in neurogenesis as related to improved learning and academic performance as a result of adequate bouts of physical activity of a vigorous nature. PMID:27182387

Human Papilloma Viruses and Breast Cancer - Assessment of Causality.

PubMed

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

2016-01-01

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

Human Papilloma Viruses and Breast Cancer – Assessment of Causality

PubMed Central

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

2016-01-01

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

Challenges in Requirements Engineering: A Research Agenda for Conceptual Modeling

NASA Astrophysics Data System (ADS)

March, Salvatore T.; Allen, Gove N.

Domains for which information systems are developed deal primarily with social constructions—conceptual objects and attributes created by human intentions and for human purposes. Information systems play an active role in these domains. They document the creation of new conceptual objects, record and ascribe values to their attributes, initiate actions within the domain, track activities performed, and infer conclusions based on the application of rules that govern how the domain is affected when socially-defined and identified causal events occur. Emerging applications of information technologies evaluate such business rules, learn from experience, and adapt to changes in the domain. Conceptual modeling grammars aimed at representing their system requirements must include conceptual objects, socially-defined events, and the rules pertaining to them. We identify challenges to conceptual modeling research and pose an ontology of the artificial as a step toward meeting them.

Cognitive Load and Learning Effects of Having Students Organize Pictures and Words in Multimedia Environments: The Role of Student Interactivity and Feedback

ERIC Educational Resources Information Center

Moreno, Roxana; Valdez, Alfred

2005-01-01

The cognitive load and learning effects of dual-code and interactivity--two multimedia methods intended to promote meaningful learning--were examined. In Experiment 1, college students learned about the causal chain of events leading to the process of lightning formation with a set of words and corresponding pictures (Group WP), pictures (Group…

Catecholaminergic Regulation of Learning Rate in a Dynamic Environment.

PubMed

Jepma, Marieke; Murphy, Peter R; Nassar, Matthew R; Rangel-Gomez, Mauricio; Meeter, Martijn; Nieuwenhuis, Sander

2016-10-01

Adaptive behavior in a changing world requires flexibly adapting one's rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the 'learning rate'). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG-an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex-predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables-capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief-on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants' baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change.

Catecholaminergic Regulation of Learning Rate in a Dynamic Environment

PubMed Central

Jepma, Marieke; Nassar, Matthew R.; Rangel-Gomez, Mauricio; Meeter, Martijn; Nieuwenhuis, Sander

2016-01-01

Adaptive behavior in a changing world requires flexibly adapting one’s rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the ‘learning rate’). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG—an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex—predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables—capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief—on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants’ baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change. PMID:27792728

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

PubMed

Cox, L A; Ricci, P F

2005-04-01

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

Non-Western Students' Causal Reasoning about Biologically Adaptive Changes in Humans, Other Animals and Plants: Instructional and Curricular Implications

ERIC Educational Resources Information Center

Mbajiorgu, Ngozika; Anidu, Innocent

2017-01-01

Senior secondary school students (N = 360), 14- to 18-year-olds, from the Igbo culture of eastern Nigeria responded to a questionnaire requiring them to give causal explanations of biologically adaptive changes in humans, other animals and plants. A student subsample (n = 36) was, subsequently, selected for in-depth interviews. Significant…

Obesity and infection: reciprocal causality.

PubMed

Hainer, V; Zamrazilová, H; Kunešová, M; Bendlová, B; Aldhoon-Hainerová, I

2015-01-01

Associations between different infectious agents and obesity have been reported in humans for over thirty years. In many cases, as in nosocomial infections, this relationship reflects the greater susceptibility of obese individuals to infection due to impaired immunity. In such cases, the infection is not related to obesity as a causal factor but represents a complication of obesity. In contrast, several infections have been suggested as potential causal factors in human obesity. However, evidence of a causal linkage to human obesity has only been provided for adenovirus 36 (Adv36). This virus activates lipogenic and proinflammatory pathways in adipose tissue, improves insulin sensitivity, lipid profile and hepatic steatosis. The E4orf1 gene of Adv36 exerts insulin senzitizing effects, but is devoid of its pro-inflammatory modalities. The development of a vaccine to prevent Adv36-induced obesity or the use of E4orf1 as a ligand for novel antidiabetic drugs could open new horizons in the prophylaxis and treatment of obesity and diabetes. More experimental and clinical studies are needed to elucidate the mutual relations between infection and obesity, identify additional infectious agents causing human obesity, as well as define the conditions that predispose obese individuals to specific infections.

Learning Disabilities.

ERIC Educational Resources Information Center

Neuwirth, Sharyn

This booklet uses hypothetical case examples to illustrate the definition, causal theories, and specific types of learning disabilities (LD). The cognitive and language performance of students with LD is compared to standard developmental milestones, and common approaches to the identification and education of children with LD are outlined.…

The Effect of Contextualized Conversational Feedback in a Complex Open-Ended Learning Environment

ERIC Educational Resources Information Center

Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

2013-01-01

Betty's Brain is an open-ended learning environment in which students learn about science topics by teaching a virtual agent named Betty through the construction of a visual causal map that represents the relevant science phenomena. The task is complex, and success requires the use of metacognitive strategies that support knowledge acquisition,…

Academic Risk Factors and Deficits of Learned Hopelessness: A Longitudinal Study of Hong Kong Secondary School Students

ERIC Educational Resources Information Center

Au, Raymond C. P.; Watkins, David A.; Hattie, John A. C.

2010-01-01

The aim of the present study is to explore a causal model of academic achievement and learning-related personal variables by testing the nature of relationships between learned hopelessness, its risk factors and hopelessness deficits as proposed in major theories in this area. The model investigates affective-motivational characteristics of…

Expanding the Basic Science Debate: The Role of Physics Knowledge in Interpreting Clinical Findings

ERIC Educational Resources Information Center

Goldszmidt, Mark; Minda, John Paul; Devantier, Sarah L.; Skye, Aimee L.; Woods, Nicole N.

2012-01-01

Current research suggests a role for biomedical knowledge in learning and retaining concepts related to medical diagnosis. However, learning may be influenced by other, non-biomedical knowledge. We explored this idea using an experimental design and examined the effects of causal knowledge on the learning, retention, and interpretation of medical…

How and What Do Medical Students Learn in Clerkships? Experience Based Learning (ExBL)

ERIC Educational Resources Information Center

Dornan, Tim; Tan, Naomi; Boshuizen, Henny; Gick, Rachel; Isba, Rachel; Mann, Karen; Scherpbier, Albert; Spencer, John; Timmins, Elizabeth

2014-01-01

Clerkship education has been called a "black box" because so little is known about what, how, and under which conditions students learn. Our aim was to develop a blueprint for education in ambulatory and inpatient settings, and in single encounters, traditional rotations, or longitudinal experiences. We identified 548 causal links…

Learning More from Educational Intervention Studies: Estimating Complier Average Causal Effects in a Relevance Intervention

ERIC Educational Resources Information Center

Nagengast, Benjamin; Brisson, Brigitte M.; Hulleman, Chris S.; Gaspard, Hanna; Häfner, Isabelle; Trautwein, Ulrich

2018-01-01

An emerging literature demonstrates that relevance interventions, which ask students to produce written reflections on how what they are learning relates to their lives, improve student learning outcomes. As part of a randomized evaluation of a relevance intervention (N = 1,978 students from 82 ninth-grade classes), we used Complier Average Causal…

What Can We Learn about Effective Early Mathematics Teaching? A Framework for Estimating Causal Effects Using Longitudinal Survey Data

ERIC Educational Resources Information Center

Guarino, Cassandra; Dieterle, Steven G.; Bargagliotti, Anna E.; Mason, William M.

2013-01-01

This study investigates the impact of teacher characteristics and instructional strategies on the mathematics achievement of students in kindergarten and first grade and tackles the question of how best to use longitudinal survey data to elicit causal inference in the face of potential threats to validity due to nonrandom assignment to treatment.…

Does Teaching Students How to Explicitly Model the Causal Structure of Systems Improve Their Understanding of These Systems?

ERIC Educational Resources Information Center

Jensen, Eva

2014-01-01

If students really understand the systems they study, they would be able to tell how changes in the system would affect a result. This demands that the students understand the mechanisms that drive its behaviour. The study investigates potential merits of learning how to explicitly model the causal structure of systems. The approach and…

The Special Status of Actions in Causal Reasoning in Rats

ERIC Educational Resources Information Center

Leising, Kenneth J.; Wong, Jared; Waldmann, Michael R.; Blaisdell, Aaron P.

2008-01-01

A. P. Blaisdell, K. Sawa, K. J. Leising, and M. R. Waldmann (2006) reported evidence for causal reasoning in rats. After learning through Pavlovian observation that Event A (a light) was a common cause of Events X (an auditory stimulus) and F (food), rats predicted F in the test phase when they observed Event X as a cue but not when they generated…

Hemispheric dissociation of reward processing in humans: insights from deep brain stimulation.

PubMed

Palminteri, Stefano; Serra, Giulia; Buot, Anne; Schmidt, Liane; Welter, Marie-Laure; Pessiglione, Mathias

2013-01-01

Rewards have various effects on human behavior and multiple representations in the human brain. Behaviorally, rewards notably enhance response vigor in incentive motivation paradigms and bias subsequent choices in instrumental learning paradigms. Neurally, rewards affect activity in different fronto-striatal regions attached to different motor effectors, for instance in left and right hemispheres for the two hands. Here we address the question of whether manipulating reward-related brain activity has local or general effects, with respect to behavioral paradigms and motor effectors. Neuronal activity was manipulated in a single hemisphere using unilateral deep brain stimulation (DBS) in patients with Parkinson's disease. Results suggest that DBS amplifies the representation of reward magnitude within the targeted hemisphere, so as to affect the behavior of the contralateral hand specifically. These unilateral DBS effects on behavior include both boosting incentive motivation and biasing instrumental choices. Furthermore, using computational modeling we show that DBS effects on incentive motivation can predict DBS effects on instrumental learning (or vice versa). Thus, we demonstrate the feasibility of causally manipulating reward-related neuronal activity in humans, in a manner that is specific to a class of motor effectors but that generalizes to different computational processes. As these findings proved independent from therapeutic effects on parkinsonian motor symptoms, they might provide insight into DBS impact on non-motor disorders, such as apathy or hypomania. Copyright © 2013 Elsevier Ltd. All rights reserved.

An algorithm for direct causal learning of influences on patient outcomes.

PubMed

Rathnam, Chandramouli; Lee, Sanghoon; Jiang, Xia

2017-01-01

This study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association study (GWAS) datasets and compared its performance to classic causal learning algorithms. The DCL algorithm learns the causes of a single target from passive data using Bayesian-scoring, instead of using independence checks, and a novel deletion algorithm. We generate 14,400 simulated datasets and measure the number of datasets for which DCL correctly and partially predicts the direct causes. We then compare its performance with the constraint-based path consistency (PC) and conservative PC (CPC) algorithms, the Bayesian-score based fast greedy search (FGS) algorithm, and the partial ancestral graphs algorithm fast causal inference (FCI). In addition, we extend our comparison of all five algorithms to both a real GWAS dataset and real breast cancer datasets over various time-points in order to observe how effective they are at predicting the causal influences of Alzheimer's disease and breast cancer survival. DCL consistently outperforms FGS, PC, CPC, and FCI in discovering the parents of the target for the datasets simulated using a simple network. Overall, DCL predicts significantly more datasets correctly (McNemar's test significance: p<0.0001) than any of the other algorithms for these network types. For example, when assessing overall performance (simple and complex network results combined), DCL correctly predicts approximately 1400 more datasets than the top FGS method, 1600 more datasets than the top CPC method, 4500 more datasets than the top PC method, and 5600 more datasets than the top FCI method. Although FGS did correctly predict more datasets than DCL for the complex networks, and DCL correctly predicted only a few more datasets than CPC for these networks, there is no significant difference in performance between these three algorithms for this network type. However, when we use a more continuous measure of accuracy, we find that all the DCL methods are able to better partially predict more direct causes than FGS and CPC for the complex networks. In addition, DCL consistently had faster runtimes than the other algorithms. In the application to the real datasets, DCL identified rs6784615, located on the NISCH gene, and rs10824310, located on the PRKG1 gene, as direct causes of late onset Alzheimer's disease (LOAD) development. In addition, DCL identified ER category as a direct predictor of breast cancer mortality within 5 years, and HER2 status as a direct predictor of 10-year breast cancer mortality. These predictors have been identified in previous studies to have a direct causal relationship with their respective phenotypes, supporting the predictive power of DCL. When the other algorithms discovered predictors from the real datasets, these predictors were either also found by DCL or could not be supported by previous studies. Our results show that DCL outperforms FGS, PC, CPC, and FCI in almost every case, demonstrating its potential to advance causal learning. Furthermore, our DCL algorithm effectively identifies direct causes in the LOAD and Metabric GWAS datasets, which indicates its potential for clinical applications. Copyright © 2016 Elsevier B.V. All rights reserved.

Causal Cognition, Force Dynamics and Early Hunting Technologies

PubMed Central

Gärdenfors, Peter; Lombard, Marlize

2018-01-01

With this contribution we analyze ancient hunting technologies as one way to explore the development of causal cognition in the hominin lineage. Building on earlier work, we separate seven grades of causal thinking. By looking at variations in force dynamics as a central element in causal cognition, we analyze the thinking required for different hunting technologies such as stabbing spears, throwing spears, launching atlatl darts, shooting arrows with a bow, and the use of poisoned arrows. Our interpretation demonstrates that there is an interplay between the extension of human body through technology and expanding our cognitive abilities to reason about causes. It adds content and dimension to the trend of including embodied cognition in evolutionary studies and in the interpretation of the archeological record. Our method could explain variation in technology sets between archaic and modern human groups. PMID:29483885

Evaluating a Computational Model of Social Causality and Responsibility

DTIC Science & Technology

2006-01-01

Evaluating a Computational Model of Social Causality and Responsibility Wenji Mao University of Southern California Institute for Creative...empirically evaluate a computa- tional model of social causality and responsibility against human social judgments. Results from our experimental...developed a general computational model of social cau- sality and responsibility [10, 11] that formalizes the factors people use in reasoning about

Causal gene identification using combinatorial V-structure search.

PubMed

Cai, Ruichu; Zhang, Zhenjie; Hao, Zhifeng

2013-07-01

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

The Reformulated Model of Learned Helplessness: An Empirical Test.

ERIC Educational Resources Information Center

Rothblum, Esther D.; Green, Leon

Abramson, Seligman and Teasdale's reformulated model of learned helplessness hypothesized that an attribution of causality intervenes between the perception of noncontingency and the future expectation of future noncontingency. To test this model, relationships between attribution and performance under failure, success, and control conditions were…

Agency, time, and causality

PubMed Central

Widlok, Thomas

2014-01-01

Cognitive Scientists interested in causal cognition increasingly search for evidence from non-Western Educational Industrial Rich Democratic people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition. PMID:25414683

Mechanistic models versus machine learning, a fight worth fighting for the biological community?

PubMed

Baker, Ruth E; Peña, Jose-Maria; Jayamohan, Jayaratnam; Jérusalem, Antoine

2018-05-01

Ninety per cent of the world's data have been generated in the last 5 years ( Machine learning: the power and promise of computers that learn by example Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by the development of mechanistic models focused on the causality of input-output relationships. However, the vast majority is aimed at supporting statistical or correlation studies that bypass the need for causality and focus exclusively on prediction. Along these lines, there has been a vast increase in the use of machine learning models, in particular in the biomedical and clinical sciences, to try and keep pace with the rate of data generation. Recent successes now beg the question of whether mechanistic models are still relevant in this area. Said otherwise, why should we try to understand the mechanisms of disease progression when we can use machine learning tools to directly predict disease outcome? © 2018 The Author(s).

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Adapting to an Uncertain World: Cognitive Capacity and Causal Reasoning with Ambiguous Observations

PubMed Central

Shou, Yiyun; Smithson, Michael

2015-01-01

Ambiguous causal evidence in which the covariance of the cause and effect is partially known is pervasive in real life situations. Little is known about how people reason about causal associations with ambiguous information and the underlying cognitive mechanisms. This paper presents three experiments exploring the cognitive mechanisms of causal reasoning with ambiguous observations. Results revealed that the influence of ambiguous observations manifested by missing information on causal reasoning depended on the availability of cognitive resources, suggesting that processing ambiguous information may involve deliberative cognitive processes. Experiment 1 demonstrated that subjects did not ignore the ambiguous observations in causal reasoning. They also had a general tendency to treat the ambiguous observations as negative evidence against the causal association. Experiment 2 and Experiment 3 included a causal learning task requiring a high cognitive demand in which paired stimuli were presented to subjects sequentially. Both experiments revealed that processing ambiguous or missing observations can depend on the availability of cognitive resources. Experiment 2 suggested that the contribution of working memory capacity to the comprehensiveness of evidence retention was reduced when there were ambiguous or missing observations. Experiment 3 demonstrated that an increase in cognitive demand due to a change in the task format reduced subjects’ tendency to treat ambiguous-missing observations as negative cues. PMID:26468653

Ketamine Effects on Memory Reconsolidation Favor a Learning Model of Delusions

PubMed Central

Gardner, Jennifer M.; Piggot, Jennifer S.; Turner, Danielle C.; Everitt, Jessica C.; Arana, Fernando Sergio; Morgan, Hannah L.; Milton, Amy L.; Lee, Jonathan L.; Aitken, Michael R. F.; Dickinson, Anthony; Everitt, Barry J.; Absalom, Anthony R.; Adapa, Ram; Subramanian, Naresh; Taylor, Jane R.; Krystal, John H.; Fletcher, Paul C.

2013-01-01

Delusions are the persistent and often bizarre beliefs that characterise psychosis. Previous studies have suggested that their emergence may be explained by disturbances in prediction error-dependent learning. Here we set up complementary studies in order to examine whether such a disturbance also modulates memory reconsolidation and hence explains their remarkable persistence. First, we quantified individual brain responses to prediction error in a causal learning task in 18 human subjects (8 female). Next, a placebo-controlled within-subjects study of the impact of ketamine was set up on the same individuals. We determined the influence of this NMDA receptor antagonist (previously shown to induce aberrant prediction error signal and lead to transient alterations in perception and belief) on the evolution of a fear memory over a 72 hour period: they initially underwent Pavlovian fear conditioning; 24 hours later, during ketamine or placebo administration, the conditioned stimulus (CS) was presented once, without reinforcement; memory strength was then tested again 24 hours later. Re-presentation of the CS under ketamine led to a stronger subsequent memory than under placebo. Moreover, the degree of strengthening correlated with individual vulnerability to ketamine's psychotogenic effects and with prediction error brain signal. This finding was partially replicated in an independent sample with an appetitive learning procedure (in 8 human subjects, 4 female). These results suggest a link between altered prediction error, memory strength and psychosis. They point to a core disruption that may explain not only the emergence of delusional beliefs but also their persistence. PMID:23776445

Two heads are better than one, but how much? Evidence that people's use of causal integration rules does not always conform to normative standards.

PubMed

Vadillo, Miguel A; Ortega-Castro, Nerea; Barberia, Itxaso; Baker, A G

2014-01-01

Many theories of causal learning and causal induction differ in their assumptions about how people combine the causal impact of several causes presented in compound. Some theories propose that when several causes are present, their joint causal impact is equal to the linear sum of the individual impact of each cause. However, some recent theories propose that the causal impact of several causes needs to be combined by means of a noisy-OR integration rule. In other words, the probability of the effect given several causes would be equal to the sum of the probability of the effect given each cause in isolation minus the overlap between those probabilities. In the present series of experiments, participants were given information about the causal impact of several causes and then they were asked what compounds of those causes they would prefer to use if they wanted to produce the effect. The results of these experiments suggest that participants actually use a variety of strategies, including not only the linear and the noisy-OR integration rules, but also averaging the impact of several causes.

A pilot randomised controlled trial to assess the utility of an e-learning package that trains users in adverse drug reaction causality.

PubMed

Conroy, Elizabeth J; Kirkham, Jamie J; Bellis, Jennifer R; Peak, Matthew; Smyth, Rosalind L; Williamson, Paula R; Pirmohamed, Munir

2015-12-01

Causality assessment of adverse drug reactions (ADRs) by healthcare professionals is often informal which can lead to inconsistencies in practice. The Liverpool Causality Assessment Tool (LCAT) offers a systematic approach. An interactive, web-based, e-learning package, the Liverpool ADR Causality Assessment e-learning Package (LACAeP), was designed to improve causality assessment using the LCAT. This study aimed to (1) get feedback on usability and usefulness on the LACAeP, identify areas for improvement and development, and generate data on effect size to inform a larger scale study; and (2) test the usability and usefulness of the LCAT. A pilot, single-blind, parallel-group, randomised controlled trial hosted by the University of Liverpool was undertaken. Participants were paediatric medical trainees at specialty training level 1+ within the Mersey and North-West England Deaneries. Participants were randomised (1 : 1) access to the LACAeP or no training. The primary efficacy outcome was score by correct classification, predefined by a multidisciplinary panel of experts. Following participation, feedback on both the LCAT and the LACAeP was obtained, via a built in survey, from participants. Of 57 randomised, 35 completed the study. Feedback was mainly positive although areas for improvement were identified. Seventy-four per cent of participants found the LCAT easy to use and 78% found the LACAeP training useful. Sixty-one per cent would be unlikely to recommend the training. Scores ranged from 4 to 13 out of 20. The LACAeP increased scores by 1.3, but this was not significant. Improving the LACAeP before testing it in an appropriately powered trial, informed by the differences observed, is required. Rigorous evaluation will enable a quality resource that will be of value in healthcare professional training. © 2015 The Authors. International Journal of Pharmacy Practice published by John Wiley & Sons Ltd on behalf of Royal Pharmaceutical Society.

A pilot randomised controlled trial to assess the utility of an e‐learning package that trains users in adverse drug reaction causality

PubMed Central

Kirkham, Jamie J.; Bellis, Jennifer R.; Peak, Matthew; Smyth, Rosalind L.; Williamson, Paula R.; Pirmohamed, Munir

2015-01-01

Abstract Objectives Causality assessment of adverse drug reactions (ADRs) by healthcare professionals is often informal which can lead to inconsistencies in practice. The Liverpool Causality Assessment Tool (LCAT) offers a systematic approach. An interactive, web‐based, e‐learning package, the Liverpool ADR Causality Assessment e‐learning Package (LACAeP), was designed to improve causality assessment using the LCAT. This study aimed to (1) get feedback on usability and usefulness on the LACAeP, identify areas for improvement and development, and generate data on effect size to inform a larger scale study; and (2) test the usability and usefulness of the LCAT. Methods A pilot, single‐blind, parallel‐group, randomised controlled trial hosted by the University of Liverpool was undertaken. Participants were paediatric medical trainees at specialty training level 1+ within the Mersey and North‐West England Deaneries. Participants were randomised (1 : 1) access to the LACAeP or no training. The primary efficacy outcome was score by correct classification, predefined by a multidisciplinary panel of experts. Following participation, feedback on both the LCAT and the LACAeP was obtained, via a built in survey, from participants. Key findings Of 57 randomised, 35 completed the study. Feedback was mainly positive although areas for improvement were identified. Seventy‐four per cent of participants found the LCAT easy to use and 78% found the LACAeP training useful. Sixty‐one per cent would be unlikely to recommend the training. Scores ranged from 4 to 13 out of 20. The LACAeP increased scores by 1.3, but this was not significant. Conclusions Improving the LACAeP before testing it in an appropriately powered trial, informed by the differences observed, is required. Rigorous evaluation will enable a quality resource that will be of value in healthcare professional training. PMID:26032626

Computational validation of the motor contribution to speech perception.

PubMed

Badino, Leonardo; D'Ausilio, Alessandro; Fadiga, Luciano; Metta, Giorgio

2014-07-01

Action perception and recognition are core abilities fundamental for human social interaction. A parieto-frontal network (the mirror neuron system) matches visually presented biological motion information onto observers' motor representations. This process of matching the actions of others onto our own sensorimotor repertoire is thought to be important for action recognition, providing a non-mediated "motor perception" based on a bidirectional flow of information along the mirror parieto-frontal circuits. State-of-the-art machine learning strategies for hand action identification have shown better performances when sensorimotor data, as opposed to visual information only, are available during learning. As speech is a particular type of action (with acoustic targets), it is expected to activate a mirror neuron mechanism. Indeed, in speech perception, motor centers have been shown to be causally involved in the discrimination of speech sounds. In this paper, we review recent neurophysiological and machine learning-based studies showing (a) the specific contribution of the motor system to speech perception and (b) that automatic phone recognition is significantly improved when motor data are used during training of classifiers (as opposed to learning from purely auditory data). Copyright © 2014 Cognitive Science Society, Inc.

The brain ages optimally to model its environment: evidence from sensory learning over the adult lifespan.

PubMed

Moran, Rosalyn J; Symmonds, Mkael; Dolan, Raymond J; Friston, Karl J

2014-01-01

The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses--including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects--from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.

Does puberty mark a transition in sensitive periods for plasticity in the associative neocortex?

PubMed Central

Piekarski, David J.; Johnson, Carolyn; Boivin, Josiah R.; Thomas, A. Wren; Lin, Wan Chen; Delevich, Kristen; Galarce, Ezequiel; Wilbrecht, Linda

2016-01-01

Postnatal brain development is studded with sensitive periods during which experience dependent plasticity is enhanced. This enables rapid learning from environmental inputs and reorganization of cortical circuits that matches behavior with environmental contingencies. Significant headway has been achieved in characterizing and understanding sensitive period biology in primary sensory cortices, but relatively little is known about sensitive period biology in associative neocortex. One possible mediator is the onset of puberty, which marks the transition to adolescence, when animals shift their behavior toward gaining independence and exploring their social world. Puberty onset correlates with reduced behavioral plasticity in some domains and enhanced plasticity in others, and therefore may drive the transition from juvenile to adolescent brain function. Pubertal onset is also occurring earlier in developed nations, particularly in unserved populations, and earlier puberty is associated with vulnerability for substance use, depression and anxiety. In the present article we review the evidence that supports a causal role for puberty in developmental changes in the function and neurobiology of the associative neocortex. We also propose a model for how pubertal hormones may regulate sensitive period plasticity in associative neocortex. We conclude that the evidence suggests puberty onset may play a causal role in some aspects of associative neocortical development, but that further research that manipulates puberty and measures gonadal hormones is required. We argue that further work of this kind is urgently needed to determine how earlier puberty may negatively impact human health and learning potential. PMID:27590721

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

PubMed

Sakurai, S

1989-12-01

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

Combining Causal Model and Focus Group Discussions Experiences Learned from a Socio-Anthropological Research on the Differing Perceptions of Caretakers and Health Professionals on Children's Health (Bolivia/Peru)

ERIC Educational Resources Information Center

Lefevre, Pierre; de Suremain, Charles-Edouard; Rubin de Celis, Emma; Sejas, Edgar

2004-01-01

The paper discusses the utility of constructing causal models in focus groups. This was experienced as a complement to an in-depth ethnographic research on the differing perceptions of caretakers and health professionals on child's growth and development in Peru and Bolivia. The rational, advantages, difficulties and necessary adaptations of…

The Effects of Affirmative Quality Feedback on Low Socio-Economic Students' Zone of Proximal Development Reading Gains (ZPDRL): A Causal-Comparative Study

ERIC Educational Resources Information Center

Prescott, Sharon H.

2010-01-01

The purpose of this study was to explore upper elementary reading classes in a low socio-economic area to determine the effects frequent praise, both academically and socially, have on the zone of proximal development in reading (ZPD[subscript RL], Renaissance Learning, 2006). A causal-comparative study was utilized by observing two groups of…

Understanding and Managing Causality of Change in Socio-Technical Systems 3

DTIC Science & Technology

2012-01-06

influence, and (4) management and control. The questions are listed below. Dynamics and Context  What can be learned from patterns of causal...has proven to be an insufficient method to determine existence of behavioral and performance patterns . Cognitive work analysis, on the other hand...to provide a point of comparison, including Victorian bushfires, Queensland and Victorian floods, and the mine collapse in Chile. Privacy, Threats

On the Role of Discipline-Related Self-Concept in Deep and Surface Approaches to Learning among University Students

ERIC Educational Resources Information Center

Platow, Michael J.; Mavor, Kenneth I.; Grace, Diana M.

2013-01-01

The current research examined the role that students' discipline-related self-concepts may play in their deep and surface approaches to learning, their overall learning outcomes, and continued engagement in the discipline itself. Using a cross-lagged panel design of first-year university psychology students, a causal path was observed in which…

Technology Integration in Education: An Examination of Technology Adoption in Teaching and Learning by Secondary Teachers in Minnesota

ERIC Educational Resources Information Center

Cherry, Jennifer E.

2014-01-01

The purpose of this study was to explore possible causal factors for level of teachers' adoption of technology in teaching and learning. Furthering the understanding of the factors related to teachers' technology adoption may facilitate increased levels of technology integration in the teaching and learning process. Based on previous research and…

The Impact of Project-Based Learning on Minority Student Achievement: Implications for School Redesign

ERIC Educational Resources Information Center

Cervantes, Bernadine; Hemmer, Lynn; Kouzekanani, Kamiar

2015-01-01

Project-Based Learning (PBL) serves as an instructional approach to classroom teaching and learning that is designed to engage students in the investigation of real-world problems to create meaningful and relevant educational experiences. The causal-comparative study compared 7th and 8th students who had utilized the PBL with a comparison group in…

The Bayesian Revolution Approaches Psychological Development

ERIC Educational Resources Information Center

Shultz, Thomas R.

2007-01-01

This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and…

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

NASA Technical Reports Server (NTRS)

Ancel, Ersin; Shih, Ann T.

2012-01-01

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

History as narrative: the nature and quality of historical understanding for students with LD.

PubMed

Espin, Christine A; Cevasco, Jazmin; van den Broek, Paul; Baker, Scott; Gersten, Russell

2007-01-01

In this study, we examine the nature and quality of students' comprehension of history. Specifically, we explore whether cognitive-psychological theories developed to capture the comprehension of narrative text can be used to capture the comprehension of history. Participants were 36 students with learning disabilities who had taken part in an earlier study designed to investigate the effects of an interactive instructional intervention in history. The results of the original study supported the effectiveness of the intervention in terms of amount recalled. The results of the present study reveal that historical understanding can be characterized as the construction of meaning through the creation of a causal network of events. The study of history within a causal network framework has implications for understanding the nature and quality of students' learning of history, and for potentially identifying sources of failure in learning.

Misconceived causal explanations for emergent processes.

PubMed

Chi, Michelene T H; Roscoe, Rod D; Slotta, James D; Roy, Marguerite; Chase, Catherine C

2012-01-01

Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes' necks get longer over generations, or how ink dropped into water appears to "flow"). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. Copyright © 2011 Cognitive Science Society, Inc.

Associative sequence learning: the role of experience in the development of imitation and the mirror system

PubMed Central

Catmur, Caroline; Walsh, Vincent; Heyes, Cecilia

2009-01-01

A core requirement for imitation is a capacity to solve the correspondence problem; to map observed onto executed actions, even when observation and execution yield sensory inputs in different modalities and coordinate frames. Until recently, it was assumed that the human capacity to solve the correspondence problem is innate. However, it is now becoming apparent that, as predicted by the associative sequence learning model, experience, and especially sensorimotor experience, plays a critical role in the development of imitation. We review evidence from studies of non-human animals, children and adults, focusing on research in cognitive neuroscience that uses training and naturally occurring variations in expertise to examine the role of experience in the formation of the mirror system. The relevance of this research depends on the widely held assumption that the mirror system plays a causal role in generating imitative behaviour. We also report original data supporting this assumption. These data show that theta-burst transcranial magnetic stimulation of the inferior frontal gyrus, a classical mirror system area, disrupts automatic imitation of finger movements. We discuss the implications of the evidence reviewed for the evolution, development and intentional control of imitation. PMID:19620108

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

PubMed Central

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

2011-01-01

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

Explaining prompts children to privilege inductively rich properties.

PubMed

Walker, Caren M; Lombrozo, Tania; Legare, Cristine H; Gopnik, Alison

2014-11-01

Four experiments with preschool-aged children test the hypothesis that engaging in explanation promotes inductive reasoning on the basis of shared causal properties as opposed to salient (but superficial) perceptual properties. In Experiments 1a and 1b, 3- to 5-year-old children prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. Experiment 2 replicated this effect of explanation in a case of label extension (i.e., categorization). Experiment 3 demonstrated that explanation improves memory for clusters of causally relevant (non-perceptual) features, but impairs memory for superficial (perceptual) features, providing evidence that effects of explanation are selective in scope and apply to memory as well as inference. In sum, our data support the proposal that engaging in explanation influences children's reasoning by privileging inductively rich, causal properties. Copyright © 2014 Elsevier B.V. All rights reserved.

Recognising discourse causality triggers in the biomedical domain.

PubMed

Mihăilă, Claudiu; Ananiadou, Sophia

2013-12-01

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.

[FROM STATISTICAL ASSOCIATIONS TO SCIENTIFIC CAUSALITY].

PubMed

Golan, Daniel; Linn, Shay

2015-06-01

The pathogenesis of most chronic diseases is complex and probably involves the interaction of multiple genetic and environmental risk factors. One way to learn about disease triggers is from statistically significant associations in epidemiological studies. However, associations do not necessarily prove causation. Associations can commonly result from bias, confounding and reverse causation. Several paradigms for causality inference have been developed. Henle-Koch postulates are mainly applied for infectious diseases. Austin Bradford Hill's criteria may serve as a practical tool to weigh the evidence regarding the probability that a single new risk factor for a given disease is indeed causal. These criteria are irrelevant for estimating the causal relationship between exposure to a risk factor and disease whenever biological causality has been previously established. Thus, it is highly probable that past exposure of an individual to definite carcinogens is related to his cancer, even without proving an association between this exposure and cancer in his group. For multifactorial diseases, Rothman's model of interacting sets of component causes can be applied.

Predicting change in epistemological beliefs, reflective thinking and learning styles: a longitudinal study.

PubMed

Phan, Huy P

2008-03-01

Although extensive research has examined epistemological beliefs, reflective thinking and learning approaches, very few studies have looked at these three theoretical frameworks in their totality. This research tested two separate structural models of epistemological beliefs, learning approaches, reflective thinking and academic performance among tertiary students over a period of 12 months. Participants were first-year Arts (N=616; 271 females, 345 males) and second-year Mathematics (N=581; 241 females, 341 males) university students. Students' epistemological beliefs were measured with the Schommer epistemological questionnaire (EQ, Schommer, 1990). Reflective thinking was measured with the reflective thinking questionnaire (RTQ, Kember et al., 2000). Student learning approaches were measured with the revised study process questionnaire (R-SPQ-2F, Biggs, Kember, & Leung, 2001). LISREL 8 was used to test two structural equation models - the cross-lag model and the causal-mediating model. In the cross-lag model involving Arts students, structural equation modelling showed that epistemological beliefs influenced student learning approaches rather than the contrary. In the causal-mediating model involving Mathematics students, the results indicate that both epistemological beliefs and learning approaches predicted reflective thinking and academic performance. Furthermore, learning approaches mediated the effect of epistemological beliefs on reflective thinking and academic performance. Results of this study are significant as they integrated the three theoretical frameworks within the one study.

What Is the Long-Run Impact of Learning Mathematics during Preschool?

ERIC Educational Resources Information Center

Watts, Tyler W.; Duncan, Greg J.; Clements, Douglas H.; Sarama, Julie

2018-01-01

The current study estimated the causal links between preschool mathematics learning and late elementary school mathematics achievement using variation in treatment assignment to an early mathematics intervention as an instrument for preschool mathematics change. Estimates indicate (n = 410) that a standard deviation of intervention-produced change…

Argumentation for Learning: Well-Trodden Paths and Unexplored Territories

ERIC Educational Resources Information Center

Asterhan, Christa S. C.; Schwarz, Baruch B.

2016-01-01

There is increasing consensus among psycho-educational scholars about argumentation as a means to improve student knowledge and understanding of subject matter. In this article, we argue that, notwithstanding a strong theoretical rationale, causal evidence is not abundant, definitions of the objects of study (argumentation, learning) are often not…

Children Monitor Individuals' Expertise for Word Learning

ERIC Educational Resources Information Center

Sobel, David M.; Corriveau, Kathleen H.

2010-01-01

Two experiments examined preschoolers' ability to learn novel words using others' expertise about objects' nonobvious properties. In Experiment 1, 4-year-olds (n = 24) endorsed individuals' labels for objects based on their differing causal knowledge about those objects. Experiment 2 examined the robustness of this inference and its development.…

High School Students' Attributions of Success in English Language Learning

ERIC Educational Resources Information Center

Bouchaib, Benzehaf; Ahmadou, Bouylmani; Abdelkader, Sabil

2018-01-01

Research into students' attributional causes for success in language acquisition is currently receiving considerable attention. Situated within Weiner's attribution theory (1992), the present study aims to research factors contributing to success in foreign language learning with specific focus on the role of perceived causal attributions. The…

The Effect of Socioeconomic Status on Specific Learning Disability Eligibility Decisions

ERIC Educational Resources Information Center

Kollister, Susan

2017-01-01

Public schools provide services for students with disabilities. Inaccurate disability diagnosis may result in inferior educational services or long-lasting educational struggles. The purpose of this quantitative causal comparative study was to determine if a difference existed between the decisions made for specific learning disability eligibility…

Administrative Assistants' Informal Learning and Related Factors

ERIC Educational Resources Information Center

Cho, Hyun Jung; Kim, Jin-Mo

2016-01-01

Purpose: The purpose of this study is to identify the causal relationship among informal learning, leader-member exchange (LMX), empowerment, job characteristics and job self-efficacy and the impact on administrative assistants in corporations. The study aims at providing information for administrative assistants who have worked with their current…

A short educational intervention diminishes causal illusions and specific paranormal beliefs in undergraduates.

PubMed

Barberia, Itxaso; Tubau, Elisabet; Matute, Helena; Rodríguez-Ferreiro, Javier

2018-01-01

Cognitive biases such as causal illusions have been related to paranormal and pseudoscientific beliefs and, thus, pose a real threat to the development of adequate critical thinking abilities. We aimed to reduce causal illusions in undergraduates by means of an educational intervention combining training-in-bias and training-in-rules techniques. First, participants directly experienced situations that tend to induce the Barnum effect and the confirmation bias. Thereafter, these effects were explained and examples of their influence over everyday life were provided. Compared to a control group, participants who received the intervention showed diminished causal illusions in a contingency learning task and a decrease in the precognition dimension of a paranormal belief scale. Overall, results suggest that evidence-based educational interventions like the one presented here could be used to significantly improve critical thinking skills in our students.

Causality in cancer epidemiology.

PubMed

Lagiou, Pagona; Adami, Hans-Olov; Trichopoulos, Dimitrios

2005-01-01

In this review, issues of causality in epidemiologic research with emphasis on the aetiology of human cancer are considered. Principles of assessing causation in epidemiological studies of cancer are distinguished into those concerning an individual study, several studies and a particular person. Strengths and weaknesses of various approaches of documenting carcinogenicity in humans are examined and lists of major established causes of human cancer are presented. The review concludes with estimates of mortality from cancer around the world that can be attributed to specific factors under the light of the current scientific knowledge.

Performance in Object-Choice Aesop’s Fable Tasks Are Influenced by Object Biases in New Caledonian Crows but not in Human Children

PubMed Central

Taylor, Alex H.; Cheke, Lucy G.; Gray, Russell D.; Loissel, Elsa; Clayton, Nicola S.

2016-01-01

The ability to reason about causality underlies key aspects of human cognition, but the extent to which non-humans understand causality is still largely unknown. The Aesop’s Fable paradigm, where objects are inserted into water-filled tubes to obtain out-of-reach rewards, has been used to test casual reasoning in birds and children. However, success on these tasks may be influenced by other factors, specifically, object preferences present prior to testing or arising during pre-test stone-dropping training. Here, we assessed this ‘object-bias’ hypothesis by giving New Caledonian crows and 5–10 year old children two object-choice Aesop’s Fable experiments: sinking vs. floating objects, and solid vs. hollow objects. Before each test, we assessed subjects’ object preferences and/or trained them to prefer the alternative object. Both crows and children showed pre-test object preferences, suggesting that birds in previous Aesop’s Fable studies may also have had initial preferences for objects that proved to be functional on test. After training to prefer the non-functional object, crows, but not children, performed more poorly on these two object-choice Aesop’s Fable tasks than subjects in previous studies. Crows dropped the non-functional objects into the tube on their first trials, indicating that, unlike many children, they do not appear to have an a priori understanding of water displacement. Alternatively, issues with inhibition could explain their performance. The crows did, however, learn to solve the tasks over time. We tested crows further to determine whether their eventual success was based on learning about the functional properties of the objects, or associating dropping the functional object with reward. Crows inserted significantly more rewarded, non-functional objects than non-rewarded, functional objects. These findings suggest that the ability of New Caledonian crows to produce performances rivaling those of young children on object-choice Aesop’s Fable tasks is partly due to pre-existing object preferences. PMID:27936242

Mirror neurons: functions, mechanisms and models.

PubMed

Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael A

2013-04-12

Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a "mirror neuron system," putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Introspective Reasoning Models for Multistrategy Case-Based and Explanation

DTIC Science & Technology

1997-03-10

symptoms and diseases to causal 30 principles about diseases and first-principle analysis grounded in basic science. Based on research in process...the symptoms of the failure to conclusion that the process which posts learning goals a causal explanation of the failure. Secondl,,. the learner...the vernacular, a "jones" is a drug habit accompanied the faucet for water. Therefore, the story can end with by withdrawal symptoms . The verb "to jones

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

PubMed Central

Valente, Bruno D.; Morota, Gota; Peñagaricano, Francisco; Gianola, Daniel; Weigel, Kent; Rosa, Guilherme J. M.

2015-01-01

The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability. PMID:25908318

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

PubMed Central

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

2011-01-01

Recent studies have shown strong temporal correlations between past climate changes and societal crises. However, the specific causal mechanisms underlying this relation have not been addressed. We explored quantitative responses of 14 fine-grained agro-ecological, socioeconomic, and demographic variables to climate fluctuations from A.D. 1500–1800 in Europe. Results show that cooling from A.D. 1560–1660 caused successive agro-ecological, socioeconomic, and demographic catastrophes, leading to the General Crisis of the Seventeenth Century. We identified a set of causal linkages between climate change and human crisis. Using temperature data and climate-driven economic variables, we simulated the alternation of defined “golden” and “dark” ages in Europe and the Northern Hemisphere during the past millennium. Our findings indicate that climate change was the ultimate cause, and climate-driven economic downturn was the direct cause, of large-scale human crises in preindustrial Europe and the Northern Hemisphere. PMID:21969578

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

PubMed

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

2011-10-18

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

Causal Model Progressions as a Foundation for Intelligent Learning Environments.

DTIC Science & Technology

1987-11-01

Foundation for Intelligent Learning Environments 3Barbara Y. White and John R. Frederiksen ~DTIC Novemr1987 ELECTE November1987 JUNO 9 88 Approved I )’I...Learning Environments 12. PERSONAL AUTHOR(S? Barbara Y. White and John R. Frederiksen 13a. TYPE OF REPORT 13b TIME COVERED 14. DATE OF REPORT (Year...architecture of a new type of learning environment that incorporates features of microworlds and of intelligent tutorng systems. The environment is based on

Human agency in social cognitive theory.

PubMed

Bandura, A

1989-09-01

The present article examines the nature and function of human agency within the conceptual model of triadic reciprocal causation. In analyzing the operation of human agency in this interactional causal structure, social cognitive theory accords a central role to cognitive, vicarious, self-reflective, and self-regulatory processes. The issues addressed concern the psychological mechanisms through which personal agency is exercised, the hierarchical structure of self-regulatory systems, eschewal of the dichotomous construal of self as agent and self as object, and the properties of a nondualistic but nonreductional conception of human agency. The relation of agent causality to the fundamental issues of freedom and determinism is also analyzed.

Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system.

PubMed

Shappell, Scott; Detwiler, Cristy; Holcomb, Kali; Hackworth, Carla; Boquet, Albert; Wiegmann, Douglas A

2007-04-01

The aim of this study was to extend previous examinations of aviation accidents to include specific aircrew, environmental, supervisory, and organizational factors associated with two types of commercial aviation (air carrier and commuter/ on-demand) accidents using the Human Factors Analysis and Classification System (HFACS). HFACS is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS can be reliably used to identify human factors trends associated with military and general aviation accidents. Using data obtained from both the National Transportation Safety Board and the Federal Aviation Administration, 6 pilot-raters classified aircrew, supervisory, organizational, and environmental causal factors associated with 1020 commercial aviation accidents that occurred over a 13-year period. The majority of accident causal factors were attributed to aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. Comparisons were made between HFACS causal categories and traditional situational variables such as visual conditions, injury severity, and regional differences. These data will provide support for the continuation, modification, and/or development of interventions aimed at commercial aviation safety. HFACS provides a tool for assessing human factors associated with accidents and incidents.

Causal Entropies – a measure for determining changes in the temporal organization of neural systems

PubMed Central

Waddell, Jack; Dzakpasu, Rhonda; Booth, Victoria; Riley, Brett; Reasor, Jonathan; Poe, Gina; Zochowski, Michal

2009-01-01

We propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network, which is thought to be a hallmark of activity-dependent synaptic modifications during learning. The measure, called Causal Entropy, is based on the time-adaptive detection of asymmetries in the relative temporal patterning between neuronal pairs. We characterize properties of the measure on both simulated data and experimental multiunit recordings of hippocampal neurons from the awake, behaving rat, and show that the metric can more readily detect those asymmetries than standard cross correlation-based techniques, especially since the temporal sensitivity of causal entropy can detect such changes rapidly and dynamically. PMID:17275095

Antibiotics and antibiotic resistance in agroecosystems: State of the science

USDA-ARS?s Scientific Manuscript database

We propose a simple causal model depicting relationships involved in dissemination of antibiotics and antibiotic resistance in agroecosystems and potential effects on human health, functioning of natural ecosystems, and agricultural productivity. Available evidence for each causal link is briefly su...

Reflecting on explanatory ability: A mechanism for detecting gaps in causal knowledge.

PubMed

Johnson, Dan R; Murphy, Meredith P; Messer, Riley M

2016-05-01

People frequently overestimate their understanding-with a particularly large blind-spot for gaps in their causal knowledge. We introduce a metacognitive approach to reducing overestimation, termed reflecting on explanatory ability (REA), which is briefly thinking about how well one could explain something in a mechanistic, step-by-step, causally connected manner. Nine experiments demonstrated that engaging in REA just before estimating one's understanding substantially reduced overestimation. Moreover, REA reduced overestimation with nearly the same potency as generating full explanations, but did so 20 times faster (although only for high complexity objects). REA substantially reduced overestimation by inducing participants to quickly evaluate an object's inherent causal complexity (Experiments 4-7). REA reduced overestimation by also fostering step-by-step, causally connected processing (Experiments 2 and 3). Alternative explanations for REA's effects were ruled out including a general conservatism account (Experiments 4 and 5) and a covert explanation account (Experiment 8). REA's overestimation-reduction effect generalized beyond objects (Experiments 1-8) to sociopolitical policies (Experiment 9). REA efficiently detects gaps in our causal knowledge with implications for improving self-directed learning, enhancing self-insight into vocational and academic abilities, and even reducing extremist attitudes. (c) 2016 APA, all rights reserved).

Prioritizing causal disease genes using unbiased genomic features.

PubMed

Deo, Rahul C; Musso, Gabriel; Tasan, Murat; Tang, Paul; Poon, Annie; Yuan, Christiana; Felix, Janine F; Vasan, Ramachandran S; Beroukhim, Rameen; De Marco, Teresa; Kwok, Pui-Yan; MacRae, Calum A; Roth, Frederick P

2014-12-03

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits. To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM. Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

Experiencing your brain: neurofeedback as a new bridge between neuroscience and phenomenology

PubMed Central

Bagdasaryan, Juliana; Quyen, Michel Le Van

2013-01-01

Neurophenomenology is a scientific research program aimed to combine neuroscience with phenomenology in order to study human experience. Nevertheless, despite several explicit implementations, the integration of first-person data into the experimental protocols of cognitive neuroscience still faces a number of epistemological and methodological challenges. Notably, the difficulties to simultaneously acquire phenomenological and neuroscientific data have limited its implementation into research projects. In our paper, we propose that neurofeedback paradigms, in which subjects learn to self-regulate their own neural activity, may offer a pragmatic way to integrate first-person and third-person descriptions. Here, information from first- and third-person perspectives is braided together in the iterative causal closed loop, creating experimental situations in which they reciprocally constrain each other. In real-time, the subject is not only actively involved in the process of data acquisition, but also assisted to directly influence the neural data through conscious experience. Thus, neurofeedback may help to gain a deeper phenomenological-physiological understanding of downward causations whereby conscious activities have direct causal effects on neuronal patterns. We discuss possible mechanisms that could mediate such effects and indicate a number of directions for future research. PMID:24187537

Rapprochement of Rotter's Social Learning Theory with Self-Esteem Constructs.

ERIC Educational Resources Information Center

Burke, Joy Patricia

1983-01-01

Offers rapprochement of Rotter's (1954) social learning theory with self-esteem and related constructs. Self constructs are defined and combined into a conceptual framework indicating the impact of their interrelations within a self-esteem system. An attribution model is used to clarify the impact of causal internalization on self-esteem.…

Different Behavioral and Eye Movement Patterns of Dyslexic Readers with and without Attentional Deficits during Single Word Reading

ERIC Educational Resources Information Center

Thaler, Verena; Urton, Karolina; Heine, Angela; Hawelka, Stefan; Engl, Verena; Jacobs, Arthur M.

2009-01-01

Comorbidity of learning disabilities is a very common phenomenon which is intensively studied in genetics, neuropsychology, prevalence studies and causal deficit research. In studies on the behavioral manifestation of learning disabilities, however, comorbidity is often neglected. In the present study, we systematically examined the reading…

Preparing Students for Future Learning with Teachable Agents

ERIC Educational Resources Information Center

Chin, Doris B.; Dohmen, Ilsa M.; Cheng, Britte H.; Oppezzo, Marily A.; Chase, Catherine C.; Schwartz, Daniel L.

2010-01-01

Over the past several years, the authors have been developing an instructional technology, called Teachable Agents (TA), which draws on the social metaphor of teaching to help students learn. Students teach a computer character, their "agent," by creating a concept map of nodes connected by qualitative causal links. The authors hypothesize that…

Exploring the Visuospatial Challenge of Learning about Day and Night and the Sun's Path

ERIC Educational Resources Information Center

Heywood, David; Parker, Joan; Rowlands, Mark

2013-01-01

The role of visualization and model-based reasoning has become increasingly significant in science education across a range of contexts. It is generally recognized that supporting learning in developing causal explanations for observed astronomical events presents considerable pedagogic challenge. Understanding the Sun's apparent movement…

Multiple Goals Perspective in Adolescent Students with Learning Difficulties

ERIC Educational Resources Information Center

Nunez, Jose Carlos; Gonzalez-Pienda, Julio Antonio; Rodriguez, Celestino; Valle, Antonio; Gonzalez-Cabanach, Ramon; Rosario, Pedro

2011-01-01

In the present work, the hypothesis of the existence of diverse motivational profiles in students with learning difficulties (LD) and the differential implications for intervention in the classroom are analyzed. Various assessment scales (academic goals, self-concept, and causal attributions) were administered to a sample of 259 students with LD,…

Organizational Change, Leadership and Learning: Culture as Cognitive Process.

ERIC Educational Resources Information Center

Lakomski, Gabriele

2001-01-01

Examines the claim that it is necessary to change an organization's culture in order to bring about organizational change. Considers the purported causal relationship between the role of the leader and organizational learning and develops the notion of culture as cognitive process based on research in cultural anthropology and cognitive science.…

Small Learning Communities Sense of Belonging to Reach At-Risk Students of Promise

ERIC Educational Resources Information Center

Hackney, Debbie

2011-01-01

The research design is a quantitative causal comparative method. The Florida Comprehensive Assessment Test (FCAT) which measures student scores included assessments in mathematics and reading. The design study called for an examination of how type of small learning community (SLC) or the type non-SLC high school environment affected student…

An Application of Attribution Theory to Developing Self-Esteem in Learning Disabled Adolescents.

ERIC Educational Resources Information Center

Tollefson, Nona; And Others

The effect of an attribution retraining program intended to teach 35 learning disabled (LD) junior high school students to attribute achievement outcomes to the internal factor of effort was examined in the study. The research was concerned with LD adolescents' perceptions of personal (internal) and environmental (external) causality as…

Spatio-Temporal Story Mapping Animation Based On Structured Causal Relationships Of Historical Events

NASA Astrophysics Data System (ADS)

Inoue, Y.; Tsuruoka, K.; Arikawa, M.

2014-04-01

In this paper, we proposed a user interface that displays visual animations on geographic maps and timelines for depicting historical stories by representing causal relationships among events for time series. We have been developing an experimental software system for the spatial-temporal visualization of historical stories for tablet computers. Our proposed system makes people effectively learn historical stories using visual animations based on hierarchical structures of different scale timelines and maps.

A short educational intervention diminishes causal illusions and specific paranormal beliefs in undergraduates

PubMed Central

Tubau, Elisabet; Matute, Helena

2018-01-01

Cognitive biases such as causal illusions have been related to paranormal and pseudoscientific beliefs and, thus, pose a real threat to the development of adequate critical thinking abilities. We aimed to reduce causal illusions in undergraduates by means of an educational intervention combining training-in-bias and training-in-rules techniques. First, participants directly experienced situations that tend to induce the Barnum effect and the confirmation bias. Thereafter, these effects were explained and examples of their influence over everyday life were provided. Compared to a control group, participants who received the intervention showed diminished causal illusions in a contingency learning task and a decrease in the precognition dimension of a paranormal belief scale. Overall, results suggest that evidence-based educational interventions like the one presented here could be used to significantly improve critical thinking skills in our students. PMID:29385184

Academic procrastination: the relationship between causal attribution styles and behavioral postponement.

PubMed

Badri Gargari, Rahim; Sabouri, Hossein; Norzad, Fatemeh

2011-01-01

This research was conducted to study the relationship between attribution and academic procrastination in University Students. The subjects were 203 undergraduate students, 55 males and 148 females, selected from English and French language and literature students of Tabriz University. Data were gathered through Procrastination Assessment Scale-student (PASS) and Causal Dimension Scale (CDA) and were analyzed by multiple regression analysis (stepwise). The results showed that there was a meaningful and negative relation between the locus of control and controllability in success context and academic procrastination. Besides, a meaningful and positive relation was observed between the locus of control and stability in failure context and procrastination. It was also found that 17% of the variance of procrastination was accounted by linear combination of attributions. We believe that causal attribution is a key in understanding procrastination in academic settings and is used by those who have the knowledge of Causal Attribution styles to organize their learning.

Causality attribution biases oculomotor responses.

PubMed

Badler, Jeremy; Lefèvre, Philippe; Missal, Marcus

2010-08-04

When viewing one object move after being struck by another, humans perceive that the action of the first object "caused" the motion of the second, not that the two events occurred independently. Although established as a perceptual and linguistic concept, it is not yet known whether the notion of causality exists as a fundamental, preattentional "Gestalt" that can influence predictive motor processes. Therefore, eye movements of human observers were measured while viewing a display in which a launcher impacted a tool to trigger the motion of a second "reaction" target. The reaction target could move either in the direction predicted by transfer of momentum after the collision ("causal") or in a different direction ("noncausal"), with equal probability. Control trials were also performed with identical target motion, either with a 100 ms time delay between the collision and reactive motion, or without the interposed tool. Subjects made significantly more predictive movements (smooth pursuit and saccades) in the causal direction during standard trials, and smooth pursuit latencies were also shorter overall. These trends were reduced or absent in control trials. In addition, pursuit latencies in the noncausal direction were longer during standard trials than during control trials. The results show that causal context has a strong influence on predictive movements.

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

PubMed

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

2017-11-13

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

Free energy, precision and learning: the role of cholinergic neuromodulation

PubMed Central

Moran, Rosalyn J.; Campo, Pablo; Symmonds, Mkael; Stephan, Klaas E.; Dolan, Raymond J.; Friston, Karl J.

2014-01-01

Acetylcholine (ACh) is a neuromodulatory transmitter implicated in perception and learning under uncertainty. This study combined computational simulations and pharmaco-electroencephalography in humans, to test a formulation of perceptual inference based upon the free energy principle. This formulation suggests that acetylcholine enhances the precision of bottom-up synaptic transmission in cortical hierarchies by optimising the gain of supragranular pyramidal cells. Simulations of a mismatch negativity paradigm predicted a rapid trial-by-trial suppression of evoked sensory prediction error (PE) responses that is attenuated by cholinergic neuromodulation. We confirmed this prediction empirically with a placebo-controlled study of cholinesterase inhibition. Furthermore – using dynamic causal modelling – we found that drug-induced differences in PE responses could be explained by gain modulation in supragranular pyramidal cells in primary sensory cortex. This suggests that acetylcholine adaptively enhances sensory precision by boosting bottom-up signalling when stimuli are predictable, enabling the brain to respond optimally under different levels of environmental uncertainty. PMID:23658161

Weight-of-evidence evaluation of short-term ozone exposure and cardiovascular effects.

PubMed

Goodman, Julie E; Prueitt, Robyn L; Sax, Sonja N; Lynch, Heather N; Zu, Ke; Lemay, Julie C; King, Joseph M; Venditti, Ferdinand J

2014-10-01

There is a relatively large body of research on the potential cardiovascular (CV) effects associated with short-term ozone exposure (defined by EPA as less than 30 days in duration). We conducted a weight-of-evidence (WoE) analysis to assess whether it supports a causal relationship using a novel WoE framework adapted from the US EPA's National Ambient Air Quality Standards causality framework. Specifically, we synthesized and critically evaluated the relevant epidemiology, controlled human exposure, and experimental animal data and made a causal determination using the same categories proposed by the Institute of Medicine report Improving the Presumptive Disability Decision-making Process for Veterans ( IOM 2008). We found that the totality of the data indicates that the results for CV effects are largely null across human and experimental animal studies. The few statistically significant associations reported in epidemiology studies of CV morbidity and mortality are very small in magnitude and likely attributable to confounding, bias, or chance. In experimental animal studies, the reported statistically significant effects at high exposures are not observed at lower exposures and thus not likely relevant to current ambient ozone exposures in humans. The available data also do not support a biologically plausible mechanism for CV effects of ozone. Overall, the current WoE provides no convincing case for a causal relationship between short-term exposure to ambient ozone and adverse effects on the CV system in humans, but the limitations of the available studies preclude definitive conclusions regarding a lack of causation. Thus, we categorize the strength of evidence for a causal relationship between short-term exposure to ozone and CV effects as "below equipoise."

Neural theory for the perception of causal actions.

PubMed

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

2012-07-01

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

Linking Microbiota to Human Diseases: A Systems Biology Perspective.

PubMed

Wu, Hao; Tremaroli, Valentina; Bäckhed, Fredrik

2015-12-01

The human gut microbiota encompasses a densely populated ecosystem that provides essential functions for host development, immune maturation, and metabolism. Alterations to the gut microbiota have been observed in numerous diseases, including human metabolic diseases such as obesity, type 2 diabetes (T2D), and irritable bowel syndrome, and some animal experiments have suggested causality. However, few studies have validated causality in humans and the underlying mechanisms remain largely to be elucidated. We discuss how systems biology approaches combined with new experimental technologies may disentangle some of the mechanistic details in the complex interactions of diet, microbiota, and host metabolism and may provide testable hypotheses for advancing our current understanding of human-microbiota interaction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Does puberty mark a transition in sensitive periods for plasticity in the associative neocortex?

PubMed

Piekarski, David J; Johnson, Carolyn M; Boivin, Josiah R; Thomas, A Wren; Lin, Wan Chen; Delevich, Kristen; M Galarce, Ezequiel; Wilbrecht, Linda

2017-01-01

Postnatal brain development is studded with sensitive periods during which experience dependent plasticity is enhanced. This enables rapid learning from environmental inputs and reorganization of cortical circuits that matches behavior with environmental contingencies. Significant headway has been achieved in characterizing and understanding sensitive period biology in primary sensory cortices, but relatively little is known about sensitive period biology in associative neocortex. One possible mediator is the onset of puberty, which marks the transition to adolescence, when animals shift their behavior toward gaining independence and exploring their social world. Puberty onset correlates with reduced behavioral plasticity in some domains and enhanced plasticity in others, and therefore may drive the transition from juvenile to adolescent brain function. Pubertal onset is also occurring earlier in developed nations, particularly in unserved populations, and earlier puberty is associated with vulnerability for substance use, depression and anxiety. In the present article we review the evidence that supports a causal role for puberty in developmental changes in the function and neurobiology of the associative neocortex. We also propose a model for how pubertal hormones may regulate sensitive period plasticity in associative neocortex. We conclude that the evidence suggests puberty onset may play a causal role in some aspects of associative neocortical development, but that further research that manipulates puberty and measures gonadal hormones is required. We argue that further work of this kind is urgently needed to determine how earlier puberty may negatively impact human health and learning potential. This article is part of a Special Issue entitled SI: Adolescent plasticity. Copyright © 2016 Elsevier B.V. All rights reserved.

Causal Reasoning in Medicine: Analysis of a Protocol.

ERIC Educational Resources Information Center

Kuipers, Benjamin; Kassirer, Jerome P.

1984-01-01

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

Human niche construction in interdisciplinary focus

PubMed Central

Kendal, Jeremy; Tehrani, Jamshid J.; Odling-Smee, John

2011-01-01

Niche construction is an endogenous causal process in evolution, reciprocal to the causal process of natural selection. It works by adding ecological inheritance, comprising the inheritance of natural selection pressures previously modified by niche construction, to genetic inheritance in evolution. Human niche construction modifies selection pressures in environments in ways that affect both human evolution, and the evolution of other species. Human ecological inheritance is exceptionally potent because it includes the social transmission and inheritance of cultural knowledge, and material culture. Human genetic inheritance in combination with human cultural inheritance thus provides a basis for gene–culture coevolution, and multivariate dynamics in cultural evolution. Niche construction theory potentially integrates the biological and social aspects of the human sciences. We elaborate on these processes, and provide brief introductions to each of the papers published in this theme issue. PMID:21320894

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

PubMed

Lee, Dong-Gi; Shin, Hyunjung

2017-05-18

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

Dissociation between judgments and outcome-expectancy measures in covariation learning: a signal detection theory approach.

PubMed

Perales, José C; Catena, Andrés; Shanks, David R; González, José A

2005-09-01

A number of studies using trial-by-trial learning tasks have shown that judgments of covariation between a cue c and an outcome o deviate from normative metrics. Parameters based on trial-by-trial predictions were estimated from signal detection theory (SDT) in a standard causal learning task. Results showed that manipulations of P(c) when contingency (deltaP) was held constant did not affect participants' ability to predict the appearance of the outcome (d') but had a significant effect on response criterion (c) and numerical causal judgments. The association between criterion c and judgment was further demonstrated in 2 experiments in which the criterion was directly manipulated by linking payoffs to the predictive responses made by learners. In all cases, the more liberal the criterion c was, the higher judgments were. The results imply that the mechanisms underlying the elaboration of judgments and those involved in the elaboration of predictive responses are partially dissociable.

The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models

DTIC Science & Technology

2011-10-27

through (25), (26) and (27), rather than going through (23) ( van der Laan and Rubin, 2006). 29 values, though disparities in parameters may not...graphs. Epidemiology 22 378–381. Petersen, M., Sinisi, S. and van der Laan, M. (2006). Estimation of direct causal effects. Epidemiology 17 276–284...and J. Halpern, eds.). College Publications, UK, 415–444. van der Laan, M. J. and Rubin, D. (2006). Targeted maximum likelihood learning. The

Causal Claims and the Operational Environment: An Analysis of Conventional and Emergent Causality as Applied to the Systems in 2007-2008 Iraq

DTIC Science & Technology

2012-06-08

Douglas Ollivant, Surge, Iraq, Petraeus, COIN 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18 . NUMBER OF PAGES 19a. NAME OF...3See, New York Times, “Iraq 5 Years In,” http://www.nytimes.com/interactive/ 2008/03/ 18 /world...Government Printing Office, 26 September 2011), 5-2. 18 Open-systems or force fields are generally defined as any human or non-human entity that

Building on prior knowledge without building it in.

PubMed

Hansen, Steven S; Lampinen, Andrew K; Suri, Gaurav; McClelland, James L

2017-01-01

Lake et al. propose that people rely on "start-up software," "causal models," and "intuitive theories" built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.

The Number Line Is a Critical Spatial-Numerical Representation: Evidence from a Fraction Intervention

ERIC Educational Resources Information Center

Hamdan, Noora; Gunderson, Elizabeth A.

2017-01-01

Children's ability to place fractions on a number line strongly correlates with math achievement. But does the number line play a causal role in fraction learning or does it simply index more advanced fraction knowledge? The number line may be a particularly effective representation for fraction learning because its properties align with the…

Progressions of Qualitative Models as a Foundation for Intelligent Learning Environments. Report No. 6277.

ERIC Educational Resources Information Center

White, Barbara Y.; Frederiksen, John R.

This report discusses the importance of presenting qualitative, causally consistent models in the initial stages of learning so that students can gain an understanding of basic electrical circuit concepts and principles that builds on their preexisting ways of reasoning about physical phenomena, and it argues that tutoring environments must help…

Cognitive Factors That Influence Children's Learning from a Multimedia Science Lesson

ERIC Educational Resources Information Center

Anggoro, Florencia K.; Stein, Nancy L.; Jee, Benjamin D.

2012-01-01

The present study examined the cognitive factors that influence children's physical science learning from a multimedia instruction. Using a causally coherent text and visual models, we taught 4th- and 7th-grade children about the observable and molecular properties of the three states of water. We manipulated whether the text was read by a tutor…

Some Learning Disabilities of Socially Disadvantaged Puerto Rican and Negro Children.

ERIC Educational Resources Information Center

Cohen, S. Alan

The findings of several tests are used to describe some learning disabilities and patterns common in lower-class Puerto Rican and Negro children. In particular, perceptual dysfunction is pointed to as a major causal factor in the reading problems of the disadvantaged. In one urban slum school, 40 percent of first graders showed serious dysfunction…

Effects of Computer-Based Early-Reading Academic Learning Time on Early-Reading Achievement: A Dose-Response Approach

ERIC Educational Resources Information Center

Heuston, Edward Benjamin Hull

2010-01-01

Academic learning time (ALT) has long had the theoretical underpinnings sufficient to claim a causal relationship with academic achievement, but to this point empirical evidence has been lacking. This dearth of evidence has existed primarily due to difficulties associated with operationalizing ALT in traditional educational settings. Recent…

Classification Agreement Analysis of Cross-Battery Assessment in the Identification of Specific Learning Disorders in Children and Youth

ERIC Educational Resources Information Center

Kranzler, John H.; Floyd, Randy G.; Benson, Nicholas; Zaboski, Brian; Thibodaux, Lia

2016-01-01

The Cross-Battery Assessment (XBA) approach to identifying a specific learning disorder (SLD) is based on the postulate that deficits in cognitive abilities in the presence of otherwise average general intelligence are causally related to academic achievement weaknesses. To examine this postulate, we conducted a classification agreement analysis…

Effectiveness of Blended Learning in a Rural Alternative Education School Setting

ERIC Educational Resources Information Center

Skelton, Robin Renee' Gossage

2017-01-01

The purpose of this non-experimental, causal-comparative study was to examine the impact of a blended learning format on the academic achievement of at-risk 9-12 grade students in a rural Northeast Georgia school system. After obtaining IRB approval and district curriculum director and superintendent approval, data was obtained for evaluation.…

Too Scared to Learn? The Academic Consequences of Feeling Unsafe at School. Working Paper #02-13

ERIC Educational Resources Information Center

Lacoe, Johanna

2013-01-01

A safe environment is a prerequisite for productive learning. This paper represents the first large-scale analysis of how feelings of safety at school affect educational outcomes. Using a unique longitudinal dataset of survey responses from New York City middle school students, the paper provides insight into the causal relationship between…

Learning How the Electron Transport Chain Works: Independent and Interactive Effects of Instructional Strategies and Learners' Characteristics

ERIC Educational Resources Information Center

Darabi, Aubteen; Arrastia-Lloyd, Meagan C.; Nelson, David W.; Liang, Xinya; Farrell, Jennifer

2015-01-01

In order to develop an expert-like mental model of complex systems, causal reasoning is essential. This study examines the differences between forward and backward instructional strategies in terms of efficiency, students' learning and progression of their mental models of the electronic transport chain in an undergraduate metabolism course…

An Action Research Study on the Effect of Interactive Technology and Active Learning on Student Performance

ERIC Educational Resources Information Center

Bear, Teresa J.

2013-01-01

This quantitative action science research study utilized a causal-comparative experimental research design in order to determine if the use of student response systems (clickers), as an active learning strategy in a community college course, improved student performance in the course. Students in the experimental group (n = 26) used clickers to…

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

NASA Astrophysics Data System (ADS)

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

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

Presenting the Students’ Academic Achievement Causal Model based on Goal Orientation

PubMed Central

NASIRI, EBRAHIM; POUR-SAFAR, ALI; TAHERI, MAHDOKHT; SEDIGHI PASHAKY, ABDULLAH; ASADI LOUYEH, ATAOLLAH

2017-01-01

Introduction: Several factors play a role in academic achievement, individual's excellence and capability to do actions and tasks that the learner is in charge of in learning areas. The main goal of this study was to present academic achievement causal model based on the dimensions of goal orientation and learning approaches among the students of Medical Science and Dentistry courses in Guilan University of Medical Sciences in 2013. Methods: This study is based on a cross-sectional model. The participants included 175 first and second students of the Medical and Dentistry schools in Guilan University of Medical Sciences selected by random cluster sampling [121 persons (69%) Medical Basic Science students and 54 (30.9%) Dentistry students]. The measurement tool included the Goal Orientation Scale of Bouffard and Study Process Questionnaire of Biggs) and the students’ Grade Point Average. The study data were analyzed using Pearson correlation coefficient and structural equations modeling. SPSS 14 and Amos were used to analyze the data. Results: The results indicated a significant relationship between goal orientation and learning strategies (P<0.05). In addition, the results revealed that a significant relationship exists between learning strategies[Deep Learning (r=0.37, P<0.05), Surface Learning (r=-0.21,P<0.05)], and academic achievement.The suggested model of research is fitted to the data of the research. Conclusion: Results showed that the students' academic achievement model fits with experimental data, so it can be used in learning principles which lead to students’ achievement in learning. PMID:28979914

Causality in medicine: the case of tumours and viruses.

PubMed Central

Vonka, V

2000-01-01

Clarification of the aetiology of chronic human diseases such as atherosclerosis or cancer is one of the dominant topics in contemporary medical research. It is believed that identification of the causal factors will enable more efficient prevention and diagnosis of these diseases and, in some instances, also permit more effective therapy. The task is difficult because of the multistep and multifactorial origin of these diseases. A special case in contemporary aetiological studies is definition of the role of viruses in the pathogenesis of human cancer. Virus-associated cancer develops only in a small minority of infected subjects, which implies that, if the virus does play a role in the pathogenesis of the malignancy, other factors must also be involved. In this paper the author attempts to review the present methodological approaches to aetiological studies of chronic diseases, discusses the role of criteria for identifying causal relationships and proposes guidelines that might help to determine the role of viruses in human cancer. PMID:11205344

Quantum Interactive Dualism: An Alternative to Materialism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stapp, Henry P

2005-06-01

Materialism rest implicitly upon the general conception of nature promoted by Galileo and Newton during the seventeenth century. It features the causal closure of the physical: The course of physically described events for all time is fixed by laws that refer exclusively to the physically describeable features of nature, and initial conditions on these feature. No reference to subjective thoughts or feeling of human beings enter. That simple conception of nature was found during the first quarter of the twentieth century to be apparently incompatible with the empirical facts. The founders of quantum theory created a new fundamental physical theory,more » quantum theory, which introduced crucially into the causal structure certain conscious choices made by human agents about how they will act. These conscious human choices are ''free'' in the sense that they are not fixed by the known laws. But they can influence the course of physically described events. Thus the principle of the causal closure of the physical fails. Applications in psycho-neuro-dynamics are described.« less

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

PubMed Central

Zaitlen, Noah A.; Ye, Chun Jimmie; Witte, John S.

2016-01-01

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature. PMID:27197206

A Longitudinal Analysis of the Causal Factors in Major Maritime Accidents in the USA and Canada (1996-2006)

NASA Technical Reports Server (NTRS)

Johnson, C. W.; Holloway, C, M.

2007-01-01

Accident reports provide important insights into the causes and contributory factors leading to particular adverse events. In contrast, this paper provides an analysis that extends across the findings presented over ten years investigations into maritime accidents by both the US National Transportation Safety Board (NTSB) and Canadian Transportation Safety Board (TSB). The purpose of the study was to assess the comparative frequency of a range of causal factors in the reporting of adverse events. In order to communicate our findings, we introduce J-H graphs as a means of representing the proportion of causes and contributory factors associated with human error, equipment failure and other high level classifications in longitudinal studies of accident reports. Our results suggest the proportion of causal and contributory factors attributable to direct human error may be very much smaller than has been suggested elsewhere in the human factors literature. In contrast, more attention should be paid to wider systemic issues, including the managerial and regulatory context of maritime operations.

Perspectives on object manipulation and action grammar for percussive actions in primates

PubMed Central

Hayashi, Misato

2015-01-01

The skill of object manipulation is a common feature of primates including humans, although there are species-typical patterns of manipulation. Object manipulation can be used as a comparative scale of cognitive development, focusing on its complexity. Nut cracking in chimpanzees has the highest hierarchical complexity of tool use reported in non-human primates. An analysis of the patterns of object manipulation in naive chimpanzees after nut-cracking demonstrations revealed the cause of difficulties in learning nut-cracking behaviour. Various types of behaviours exhibited within a nut-cracking context can be examined in terms of the application of problem-solving strategies, focusing on their basis in causal understanding or insightful intentionality. Captive chimpanzees also exhibit complex forms of combinatory manipulation, which is the precursor of tool use. A new notation system of object manipulation was invented to assess grammatical rules in manipulative actions. The notation system of action grammar enabled direct comparisons to be made between primates including humans in a variety of object-manipulation tasks, including percussive-tool use. PMID:26483528

Inborn Errors of Fructose Metabolism. What Can We Learn from Them?

PubMed

Tran, Christel

2017-04-03

Fructose is one of the main sweetening agents in the human diet and its ingestion is increasing globally. Dietary sugar has particular effects on those whose capacity to metabolize fructose is limited. If intolerance to carbohydrates is a frequent finding in children, inborn errors of carbohydrate metabolism are rare conditions. Three inborn errors are known in the pathway of fructose metabolism; (1) essential or benign fructosuria due to fructokinase deficiency; (2) hereditary fructose intolerance; and (3) fructose-1,6-bisphosphatase deficiency. In this review the focus is set on the description of the clinical symptoms and biochemical anomalies in the three inborn errors of metabolism. The potential toxic effects of fructose in healthy humans also are discussed. Studies conducted in patients with inborn errors of fructose metabolism helped to understand fructose metabolism and its potential toxicity in healthy human. Influence of fructose on the glycolytic pathway and on purine catabolism is the cause of hypoglycemia, lactic acidosis and hyperuricemia. The discovery that fructose-mediated generation of uric acid may have a causal role in diabetes and obesity provided new understandings into pathogenesis for these frequent diseases.

Inborn Errors of Fructose Metabolism. What Can We Learn from Them?

PubMed Central

Tran, Christel

2017-01-01

Fructose is one of the main sweetening agents in the human diet and its ingestion is increasing globally. Dietary sugar has particular effects on those whose capacity to metabolize fructose is limited. If intolerance to carbohydrates is a frequent finding in children, inborn errors of carbohydrate metabolism are rare conditions. Three inborn errors are known in the pathway of fructose metabolism; (1) essential or benign fructosuria due to fructokinase deficiency; (2) hereditary fructose intolerance; and (3) fructose-1,6-bisphosphatase deficiency. In this review the focus is set on the description of the clinical symptoms and biochemical anomalies in the three inborn errors of metabolism. The potential toxic effects of fructose in healthy humans also are discussed. Studies conducted in patients with inborn errors of fructose metabolism helped to understand fructose metabolism and its potential toxicity in healthy human. Influence of fructose on the glycolytic pathway and on purine catabolism is the cause of hypoglycemia, lactic acidosis and hyperuricemia. The discovery that fructose-mediated generation of uric acid may have a causal role in diabetes and obesity provided new understandings into pathogenesis for these frequent diseases. PMID:28368361

Perspectives on object manipulation and action grammar for percussive actions in primates.

PubMed

Hayashi, Misato

2015-11-19

The skill of object manipulation is a common feature of primates including humans, although there are species-typical patterns of manipulation. Object manipulation can be used as a comparative scale of cognitive development, focusing on its complexity. Nut cracking in chimpanzees has the highest hierarchical complexity of tool use reported in non-human primates. An analysis of the patterns of object manipulation in naive chimpanzees after nut-cracking demonstrations revealed the cause of difficulties in learning nut-cracking behaviour. Various types of behaviours exhibited within a nut-cracking context can be examined in terms of the application of problem-solving strategies, focusing on their basis in causal understanding or insightful intentionality. Captive chimpanzees also exhibit complex forms of combinatory manipulation, which is the precursor of tool use. A new notation system of object manipulation was invented to assess grammatical rules in manipulative actions. The notation system of action grammar enabled direct comparisons to be made between primates including humans in a variety of object-manipulation tasks, including percussive-tool use. © 2015 The Author(s).

A Bird’s Eye View of Human Language Evolution

PubMed Central

Berwick, Robert C.; Beckers, Gabriël J. L.; Okanoya, Kazuo; Bolhuis, Johan J.

2012-01-01

Comparative studies of linguistic faculties in animals pose an evolutionary paradox: language involves certain perceptual and motor abilities, but it is not clear that this serves as more than an input–output channel for the externalization of language proper. Strikingly, the capability for auditory–vocal learning is not shared with our closest relatives, the apes, but is present in such remotely related groups as songbirds and marine mammals. There is increasing evidence for behavioral, neural, and genetic similarities between speech acquisition and birdsong learning. At the same time, researchers have applied formal linguistic analysis to the vocalizations of both primates and songbirds. What have all these studies taught us about the evolution of language? Is the comparative study of an apparently species-specific trait like language feasible? We argue that comparative analysis remains an important method for the evolutionary reconstruction and causal analysis of the mechanisms underlying language. On the one hand, common descent has been important in the evolution of the brain, such that avian and mammalian brains may be largely homologous, particularly in the case of brain regions involved in auditory perception, vocalization, and auditory memory. On the other hand, there has been convergent evolution of the capacity for auditory–vocal learning, and possibly for structuring of external vocalizations, such that apes lack the abilities that are shared between songbirds and humans. However, significant limitations to this comparative analysis remain. While all birdsong may be classified in terms of a particularly simple kind of concatenation system, the regular languages, there is no compelling evidence to date that birdsong matches the characteristic syntactic complexity of human language, arising from the composition of smaller forms like words and phrases into larger ones. PMID:22518103

Neurobiology of aggressive behavior.

PubMed

Delgado, J M

1976-10-30

Causality, neurological mechanisms, and behavioral manifestations may be heterogeneous in different forms of aggressive behavior, but some elements are shared by all forms of violence, including the necessity of sensory inputs, the coding and decoding of information according to acquired frames of reference, and the activation of pre-established patterns of response. Understanding and prevention of violence requires a simultaneous study of its social, cultural, and economic aspects, at parity with an investigation of its neurological mechanisms. Part of the latter information may be obtained through animal experimentation, preferably in non-human primates. Feline predatory behavior has no equivalent in man, and therefore its hypothalamic representation probably does not exist in the human brain. Codes of information, frames of reference for sensory perception, axis to evaluate threats, and formulas for aggressive performance are not established genetically but must be learned individually. We are born with the capacity to learn aggressive behavior, but not with established patterns of violence. Mechanisms for fighting which are acquired by individual experience may be triggered in a similar way by sensory cues, volition, and by electrical stimulation of specific cerebral areas. In monkeys, aggressive responses may be modified by changing the hierarchical position of the stimulated animal, indicating the physiological quality of the neurological mechanisms electrically activated.

The gut microbiota and obesity: from correlation to causality.

PubMed

Zhao, Liping

2013-09-01

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

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

USGS Publications Warehouse

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

2013-01-01

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

Food Additives and Hyperkinesis

ERIC Educational Resources Information Center

Wender, Ester H.

1977-01-01

The hypothesis that food additives are causally associated with hyperkinesis and learning disabilities in children is reviewed, and available data are summarized. Available from: American Medical Association 535 North Dearborn Street Chicago, Illinois 60610. (JG)

Children's science learning: A core skills approach.

PubMed

Tolmie, Andrew K; Ghazali, Zayba; Morris, Suzanne

2016-09-01

Research has identified the core skills that predict success during primary school in reading and arithmetic, and this knowledge increasingly informs teaching. However, there has been no comparable work that pinpoints the core skills that underlie success in science. The present paper attempts to redress this by examining candidate skills and considering what is known about the way in which they emerge, how they relate to each other and to other abilities, how they change with age, and how their growth may vary between topic areas. There is growing evidence that early-emerging tacit awareness of causal associations is initially separated from language-based causal knowledge, which is acquired in part from everyday conversation and shows inaccuracies not evident in tacit knowledge. Mapping of descriptive and explanatory language onto causal awareness appears therefore to be a key development, which promotes unified conceptual and procedural understanding. This account suggests that the core components of initial science learning are (1) accurate observation, (2) the ability to extract and reason explicitly about causal connections, and (3) knowledge of mechanisms that explain these connections. Observational ability is educationally inaccessible until integrated with verbal description and explanation, for instance, via collaborative group work tasks that require explicit reasoning with respect to joint observations. Descriptive ability and explanatory ability are further promoted by managed exposure to scientific vocabulary and use of scientific language. Scientific reasoning and hypothesis testing are later acquisitions that depend on this integration of systems and improved executive control. © 2016 The British Psychological Society.

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences.

PubMed

Bhat, Ajaz Ahmad; Mohan, Vishwanathan; Sandini, Giulio; Morasso, Pietro

2016-07-01

Emerging studies indicate that several species such as corvids, apes and children solve 'The Crow and the Pitcher' task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause-effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended 'learning-prediction-abstraction' loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. © 2016 The Author(s).

Ways of learning: Observational studies versus experiments

USGS Publications Warehouse

Shaffer, T.L.; Johnson, D.H.

2008-01-01

Manipulative experimentation that features random assignment of treatments, replication, and controls is an effective way to determine causal relationships. Wildlife ecologists, however, often must take a more passive approach to investigating causality. Their observational studies lack one or more of the 3 cornerstones of experimentation: controls, randomization, and replication. Although an observational study can be analyzed similarly to an experiment, one is less certain that the presumed treatment actually caused the observed response. Because the investigator does not actively manipulate the system, the chance that something other than the treatment caused the observed results is increased. We reviewed observational studies and contrasted them with experiments and, to a lesser extent, sample surveys. We identified features that distinguish each method of learning and illustrate or discuss some complications that may arise when analyzing results of observational studies. Findings from observational studies are prone to bias. Investigators can reduce the chance of reaching erroneous conclusions by formulating a priori hypotheses that can be pursued multiple ways and by evaluating the sensitivity of study conclusions to biases of various magnitudes. In the end, however, professional judgment that considers all available evidence is necessary to render a decision regarding causality based on observational studies.

The cradle of causal reasoning: newborns' preference for physical causality.

PubMed

Mascalzoni, Elena; Regolin, Lucia; Vallortigara, Giorgio; Simion, Francesca

2013-05-01

Perception of mechanical (i.e. physical) causality, in terms of a cause-effect relationship between two motion events, appears to be a powerful mechanism in our daily experience. In spite of a growing interest in the earliest causal representations, the role of experience in the origin of this sensitivity is still a matter of dispute. Here, we asked the question about the innate origin of causal perception, never tested before at birth. Three experiments were carried out to investigate sensitivity at birth to some visual spatiotemporal cues present in a launching event. Newborn babies, only a few hours old, showed that they significantly preferred a physical causality event (i.e. Michotte's Launching effect) when matched to a delay event (i.e. a delayed launching; Experiment 1) or to a non-causal event completely identical to the causal one except for the order of the displacements of the two objects involved which was swapped temporally (Experiment 3). This preference for the launching event, moreover, also depended on the continuity of the trajectory between the objects involved in the event (Experiment 2). These results support the hypothesis that the human system possesses an early available, possibly innate basic mechanism to compute causality, such a mechanism being sensitive to the additive effect of certain well-defined spatiotemporal cues present in the causal event independently of any prior visual experience. © 2013 Blackwell Publishing Ltd.

People or systems? To blame is human. The fix is to engineer.

PubMed

Holden, Richard J

2009-12-01

Person-centered safety theories that place the burden of causality on human traits and actions have been largely dismissed in favor of systems-centered theories. Students and practitioners are now taught that accidents are caused by multiple factors and occur due to the complex interactions of numerous work system elements, human and non-human. Nevertheless, person-centered approaches to safety management still prevail. This paper explores the notion that attributing causality and blame to people persists because it is both a fundamental psychological tendency as well as an industry norm that remains strong in aviation, health care, and other industries. Consequences of that possibility are discussed and a case is made for continuing to invest in whole-system design and engineering solutions.

Investigating the Structural Relationship among Perceived Innovation Attributes, Intention to Use and Actual Use of Mobile Learning in an Online University in South Korea

ERIC Educational Resources Information Center

Joo, Young Ju; Lim, Kyu Yon; Lim, Eugene

2014-01-01

The purpose of this study was to investigate the effect of perceived attributes of innovation, that is, relative advantage, compatibility, complexity, trialability and observability on learners' use of mobile learning. Specifically, this study employed structural equation modeling in order to examine the causal relationships among perceived…

Structure and Agency in Learning: A Critical Realist Theory of the Development of Capacity to Reflect on Academic Practice

ERIC Educational Resources Information Center

Kahn, Peter; Qualter, Anne; Young, Richard

2012-01-01

Theories of learning typically downplay the interplay between social structure and student agency. In this article, we adapt a causal hypothesis from realist social theory and draw on wider perspectives from critical realism to account for the development of capacity to engage in reflection on professional practice in academic roles. We thereby…

A Proposed Framework to Understand the Intrinsic Motivation Factors on University Students' Behavioral Intention to Use a Mobile Application for Learning

ERIC Educational Resources Information Center

Shroff, Ronnie H.; Keyes, Christopher J.

2017-01-01

Aim/Purpose: By integrating a motivational perspective into the Technology Acceptance Model, the goal of this study is to empirically test the causal relationship of intrinsic motivational factors on students' behavioral intention to use (BIU) a mobile application for learning. Background: Although the Technology Acceptance Model is a significant…

Subgroups of Attributional Profiles in Students with Learning Difficulties and Their Relation to Self-Concept and Academic Goals

ERIC Educational Resources Information Center

Nunez, Jose Carlos; Gonzalez-Pienda, Julio A.; Gonzalez-Pumariega, Soledad; Roces, Cristina; Alvarez, Luis; Gonzalez, Paloma; Cabanach, Ramon G.; Valle, Antonio; Rodriguez, Susana

2005-01-01

The aim of this article was fourfold: first, to determine whether there are significant differences between students with (N=173) and without learning disabilities (LD; N=172) in the dimensions of self-concept, causal attributions, and academic goals. Second, to determine whether students with LD present a uniform attributional profile or whether…

An Examination of Reflective Thinking, Learning Approaches, and Self-Efficacy Beliefs at the University of the South Pacific: A Path Analysis Approach

ERIC Educational Resources Information Center

Phan, Huy P.

2007-01-01

This study examined the causal and mediating relations between students' learning approaches, self-efficacy beliefs, stages of reflective thinking, and academic performance. Second-year undergraduate students (n = 241; 118 females, 123 males) in the South Pacific were administered the revised version of Biggs' Study Process Questionnaire, the…

The Effect of Face-to-Face versus Online Pedagogy-Based Professional Development on Student Learning Outcomes

ERIC Educational Resources Information Center

Rock, Heidi Marie

2017-01-01

The purpose of this quantitative retrospective causal-comparative study was to determine to what extent the form of professional development (face-to-face or online) or the level of instruction (elementary or high school) has on classroom teaching practices as measured by student learning outcomes. The first research question sought to determine…

Mathematics Achievement with Digital Game-Based Learning in High School Algebra 1 Classes

ERIC Educational Resources Information Center

Ferguson, Terri Lynn Kurley

2014-01-01

This study examined the impact of digital game-based learning (DGBL) on mathematics achievement in a rural high school setting in North Carolina. A causal comparative research design was used in this study to collect data to determine the effectiveness of DGBL in high school Algebra 1 classes. Data were collected from the North Carolina…

Theory of mind training causes honest young children to lie

PubMed Central

Ding, Xiao Pan; Wellman, Henry; Wang, Yu; Fu, Genyue; Lee, Kang

2015-01-01

Theory of mind (ToM) has long been recognized to play a major role in children’s social functioning. However, no direct evidence confirms the causal linkage between the two. Here we addressed this significant gap by examining whether ToM causes the emergence of lying, an important social skill. We showed that after participating in ToM training to learn about mental state concepts, 3-year-olds who originally had been unable to lie began to deceive consistently. This training effect lasted for more than a month. In contrast, 3-year-olds who participated in control training to learn about physical concepts were significantly less inclined to lie than the ToM trained children. These findings provide the first experimental evidence supporting the causal role of ToM in the development of social competence in early childhood. PMID:26431737

Causal Inferences with Group Based Trajectory Models

ERIC Educational Resources Information Center

Haviland, Amelia M.; Nagin, Daniel S.

2005-01-01

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This paper lays out and applies a method for using observational longitudinal data to make more confident causal inferences about the…

Episodic memory and the witness trump card.

PubMed

Henry, Jeremy; Craver, Carl

2018-01-01

We accept Mahr & Csibra's (M&C's) causal claim that episodic memory provides humans with the means for evaluating the veracity of reports about non-occurrent events. We reject their evolutionary argument that this is the proper function of episodic memory. We explore three intriguing implications of the causal claim, for cognitive neuropsychology, comparative psychology, and philosophy.

Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.

PubMed

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

2017-01-01

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

Academic Procrastination: The Relationship Between Causal Attribution Styles and Behavioral Postponement

PubMed Central

Badri Gargari, Rahim; Sabouri, Hossein; Norzad, Fatemeh

2011-01-01

Objective: This research was conducted to study the relationship between attribution and academic procrastination in University Students. Methods: The subjects were 203 undergraduate students, 55 males and 148 females, selected from English and French language and literature students of Tabriz University. Data were gathered through Procrastination Assessment Scale-student (PASS) and Causal Dimension Scale (CDA) and were analyzed by multiple regression analysis (stepwise). Results: The results showed that there was a meaningful and negative relation between the locus of control and controllability in success context and academic procrastination. Besides, a meaningful and positive relation was observed between the locus of control and stability in failure context and procrastination. It was also found that 17% of the variance of procrastination was accounted by linear combination of attributions. Conclusion: We believe that causal attribution is a key in understanding procrastination in academic settings and is used by those who have the knowledge of Causal Attribution styles to organize their learning. PMID:24644450

Abnormal Visual Motion Processing is not a Cause of Dyslexia

PubMed Central

Olulade, Olumide A.; Napoliello, Eileen M.; Eden, Guinevere F.

2013-01-01

SUMMARY Developmental dyslexia is a reading disorder, yet deficits also manifest in the magnocellular-dominated dorsal visual system. Uncertainty about whether visual deficits are causal or consequential to reading disability encumbers accurate identification and appropriate treatment of this common learning disability. Using fMRI, we demonstrate in typical readers a relationship between reading ability and activity in area V5/MT during visual motion processing and, as expected, also found lower V5/MT activity for dyslexic children compared to age-matched controls. However, when dyslexics were matched to younger controls on reading ability, no differences emerged, suggesting that weakness in V5/MT may not be causal to dyslexia. To further test for causality, dyslexics underwent a phonological-based reading intervention. Surprisingly, V5/MT activity increased along with intervention-driven reading gains, demonstrating that activity here is mobilized through reading. Our results provide strong evidence that visual magnocellular dysfunction is not causal to dyslexia, but may instead be consequential to impoverished reading. PMID:23746630

Deep learning with convolutional neural networks for EEG decoding and visualization.

PubMed

Schirrmeister, Robin Tibor; Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

2017-11-01

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Causal judgment from contingency information: relation between subjective reports and individual tendencies in judgment.

PubMed

White, P A

2000-04-01

In two experiments, participants made causal judgments from contingency information for problems with different objective contingencies. After the judgment task, the participants reported how their judgments had changed following each type of contingency information. Some reported idiosyncratic tendencies--in other words, tendencies contrary to those expected under associative-learning and normative rule induction models of contingency judgment. These idiosyncratic reports tended to be better predictors of the judgments of those who made them than did the models. The results are consistent with the view that causal judgment from contingency information is made, at least in part, by deliberative use of acquired and sometimes idiosyncratic notions of evidential value, the outcomes of which tend, in aggregate, to be highly correlated with the outcomes of normative procedures.

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

PubMed Central

Murray-Watters, Alexander; Glymour, Clark

2016-01-01

Using Gebharter's (2014) representation, we consider aspects of the problem of discovering the structure of unmeasured sub-mechanisms when the variables in those sub-mechanisms have not been measured. Exploiting an early insight of Sober's (1998), we provide a correct algorithm for identifying latent, endogenous structure—sub-mechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned. PMID:27313331

Entrainment of prefrontal beta oscillations induces an endogenous echo and impairs memory formation.

PubMed

Hanslmayr, Simon; Matuschek, Jonas; Fellner, Marie-Christin

2014-04-14

Brain oscillations across all frequency bands play a key role for memory formation. Specifically, desynchronization of local neuronal assemblies in the left inferior prefrontal cortex (PFC) in the beta frequency (∼18 Hz) has been shown to be central for encoding of verbal memories. However, it remains elusive whether prefrontal beta desynchronization is causally relevant for memory formation and whether these endogenous beta oscillations can be entrained by external stimulation. By using combined EEG-TMS (transcranial magnetic stimulation), we here address these fundamental questions in human participants performing a word-list learning task. Confirming our predictions, memory encoding was selectively impaired when the left inferior frontal gyrus (IFG) was driven at beta (18.7 Hz) compared to stimulation at other frequencies (6.8 Hz and 10.7 Hz) and to ineffective sham stimulation (18.7 Hz). Furthermore, a sustained oscillatory "echo" in the left IFG, which outlasted the stimulation period by approximately 1.5 s, was observed solely after beta stimulation. The strength of this beta echo was related to memory impairment on a between-subjects level. These results show endogenous oscillatory entrainment effects and behavioral impairment selectively in beta frequency for stimulation of the left IFG, demonstrating an intimate causal relationship between prefrontal beta desynchronization and memory formation. Copyright © 2014 Elsevier Ltd. All rights reserved.

The center for causal discovery of biomedical knowledge from big data

PubMed Central

Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-01-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. PMID:26138794

The Effect of Career and Technical Education on Human Capital Accumulation: Causal Evidence from Massachusetts

ERIC Educational Resources Information Center

Dougherty, Shaun M.

2018-01-01

Earlier work demonstrates that career and technical education (CTE) can provide long-term financial benefits to participants, yet few have explored potential academic impacts, with none in the era of high-stakes accountability. This paper investigates the causal impact of participating in a specialized high school-based CTE delivery system on high…

The good, the bad, and the timely: how temporal order and moral judgment influence causal selection

PubMed Central

Reuter, Kevin; Kirfel, Lara; van Riel, Raphael; Barlassina, Luca

2014-01-01

Causal selection is the cognitive process through which one or more elements in a complex causal structure are singled out as actual causes of a certain effect. In this paper, we report on an experiment in which we investigated the role of moral and temporal factors in causal selection. Our results are as follows. First, when presented with a temporal chain in which two human agents perform the same action one after the other, subjects tend to judge the later agent to be the actual cause. Second, the impact of temporal location on causal selection is almost canceled out if the later agent did not violate a norm while the former did. We argue that this is due to the impact that judgments of norm violation have on causal selection—even if the violated norm has nothing to do with the obtaining effect. Third, moral judgments about the effect influence causal selection even in the case in which agents could not have foreseen the effect and did not intend to bring it about. We discuss our findings in connection to recent theories of the role of moral judgment in causal reasoning, on the one hand, and to probabilistic models of temporal location, on the other. PMID:25477851

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

PubMed Central

Schlottmann, Anne; Cole, Katy; Watts, Rhianna; White, Marina

2013-01-01

Humans, even babies, perceive causality when one shape moves briefly and linearly after another. Motion timing is crucial in this and causal impressions disappear with short delays between motions. However, the role of temporal information is more complex: it is both a cue to causality and a factor that constrains processing. It affects ability to distinguish causality from non-causality, and social from mechanical causality. Here we study both issues with 3- to 7-year-olds and adults who saw two computer-animated squares and chose if a picture of mechanical, social or non-causality fit each event best. Prior work fit with the standard view that early in development, the distinction between the social and physical domains depends mainly on whether or not the agents make contact, and that this reflects concern with domain-specific motion onset, in particular, whether the motion is self-initiated or not. The present experiments challenge both parts of this position. In Experiments 1 and 2, we showed that not just spatial, but also animacy and temporal information affect how children distinguish between physical and social causality. In Experiments 3 and 4 we showed that children do not seem to use spatio-temporal information in perceptual causality to make inferences about self- or other-initiated motion onset. Overall, spatial contact may be developmentally primary in domain-specific perceptual causality in that it is processed easily and is dominant over competing cues, but it is not the only cue used early on and it is not used to infer motion onset. Instead, domain-specific causal impressions may be automatic reactions to specific perceptual configurations, with a complex role for temporal information. PMID:23874308

Perceptual learning modifies the functional specializations of visual cortical areas.

PubMed

Chen, Nihong; Cai, Peng; Zhou, Tiangang; Thompson, Benjamin; Fang, Fang

2016-05-17

Training can improve performance of perceptual tasks. This phenomenon, known as perceptual learning, is strongest for the trained task and stimulus, leading to a widely accepted assumption that the associated neuronal plasticity is restricted to brain circuits that mediate performance of the trained task. Nevertheless, learning does transfer to other tasks and stimuli, implying the presence of more widespread plasticity. Here, we trained human subjects to discriminate the direction of coherent motion stimuli. The behavioral learning effect substantially transferred to noisy motion stimuli. We used transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms underlying the transfer of learning. The TMS experiment revealed dissociable, causal contributions of V3A (one of the visual areas in the extrastriate visual cortex) and MT+ (middle temporal/medial superior temporal cortex) to coherent and noisy motion processing. Surprisingly, the contribution of MT+ to noisy motion processing was replaced by V3A after perceptual training. The fMRI experiment complemented and corroborated the TMS finding. Multivariate pattern analysis showed that, before training, among visual cortical areas, coherent and noisy motion was decoded most accurately in V3A and MT+, respectively. After training, both kinds of motion were decoded most accurately in V3A. Our findings demonstrate that the effects of perceptual learning extend far beyond the retuning of specific neural populations for the trained stimuli. Learning could dramatically modify the inherent functional specializations of visual cortical areas and dynamically reweight their contributions to perceptual decisions based on their representational qualities. These neural changes might serve as the neural substrate for the transfer of perceptual learning.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants.

PubMed

Uricchio, Lawrence H; Zaitlen, Noah A; Ye, Chun Jimmie; Witte, John S; Hernandez, Ryan D

2016-07-01

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature. © 2016 Uricchio et al.; Published by Cold Spring Harbor Laboratory Press.

Learning Styles in Continuing Medical Education.

ERIC Educational Resources Information Center

Bennet, Nancy L.; Fox, Robert D.

1984-01-01

Synthesizes literature on cognitive style and considers issues about its role in continuing medical education research, including whether it should be used as a dependent or independent variable and how it may be used in causal models. (SK)

Depression, Activity, and Evaluation of Reinforcement

ERIC Educational Resources Information Center

Hammen, Constance L.; Glass, David R., Jr.

1975-01-01

This research attempted to find the causal relation between mood and level of reinforcement. An effort was made to learn what mood change might occur if depressed subjects increased their levels of participation in reinforcing activities. (Author/RK)

Learning from others' mistakes? limits on understanding a trap-tube task by young chimpanzees (Pan troglodytes) and children (Homo sapiens).

PubMed

Horner, Victoria; Whiten, Andrew

2007-02-01

A trap-tube task was used to determine whether chimpanzees (Pan troglodytes) and children (Homo sapiens) who observed a model's errors and successes could master the task in fewer trials than those who saw only successes. Two- to 7-year-old chimpanzees and 3- to 4-year-old children did not benefit from observing errors and found the task difficult. Two of the 6 chimpanzees developed a successful anticipatory strategy but showed no evidence of representing the core causal relations involved in trapping. Three- to 4-year-old children showed a similar limitation and tended to copy the actions of the demonstrator, irrespective of their causal relevance. Five- to 6-year-old children were able to master the task but did not appear to be influenced by social learning or benefit from observing errors.

Theory-of-Mind Training Causes Honest Young Children to Lie.

PubMed

Ding, Xiao Pan; Wellman, Henry M; Wang, Yu; Fu, Genyue; Lee, Kang

2015-11-01

Theory of mind (ToM) has long been recognized to play a major role in children's social functioning. However, no direct evidence confirms the causal linkage between the two. In the current study, we addressed this significant gap by examining whether ToM causes the emergence of lying, an important social skill. We showed that after participating in ToM training to learn about mental-state concepts, 3-year-olds who originally had been unable to lie began to deceive consistently. This training effect lasted for more than a month. In contrast, 3-year-olds who participated in control training to learn about physical concepts were significantly less inclined to lie than the ToM-trained children. These findings provide the first experimental evidence supporting the causal role of ToM in the development of social competence in early childhood. © The Author(s) 2015.

Understanding bicycling in cities using system dynamics modelling.

PubMed

Macmillan, Alexandra; Woodcock, James

2017-12-01

Increasing urban bicycling has established net benefits for human and environmental health. Questions remain about which policies are needed and in what order, to achieve an increase in cycling while avoiding negative consequences. Novel ways of considering cycling policy are needed, bringing together expertise across policy, community and research to develop a shared understanding of the dynamically complex cycling system. In this paper we use a collaborative learning process to develop a dynamic causal model of urban cycling to develop consensus about the nature and order of policies needed in different cycling contexts to optimise outcomes. We used participatory system dynamics modelling to develop causal loop diagrams (CLDs) of cycling in three contrasting contexts: Auckland, London and Nijmegen. We combined qualitative interviews and workshops to develop the CLDs. We used the three CLDs to compare and contrast influences on cycling at different points on a "cycling trajectory" and drew out policy insights. The three CLDs consisted of feedback loops dynamically influencing cycling, with significant overlap between the three diagrams. Common reinforcing patterns emerged: growing numbers of people cycling lifts political will to improve the environment; cycling safety in numbers drives further growth; and more cycling can lead to normalisation across the population. By contrast, limits to growth varied as cycling increases. In Auckland and London, real and perceived danger was considered the main limit, with added barriers to normalisation in London. Cycling congestion and "market saturation" were important in the Netherlands. A generalisable, dynamic causal theory for urban cycling enables a more ordered set of policy recommendations for different cities on a cycling trajectory. Participation meant the collective knowledge of cycling stakeholders was represented and triangulated with research evidence. Extending this research to further cities, especially in low-middle income countries, would enhance generalizability of the CLDs.

Scientific thinking in young children: theoretical advances, empirical research, and policy implications.

PubMed

Gopnik, Alison

2012-09-28

New theoretical ideas and empirical research show that very young children's learning and thinking are strikingly similar to much learning and thinking in science. Preschoolers test hypotheses against data and make causal inferences; they learn from statistics and informal experimentation, and from watching and listening to others. The mathematical framework of probabilistic models and Bayesian inference can describe this learning in precise ways. These discoveries have implications for early childhood education and policy. In particular, they suggest both that early childhood experience is extremely important and that the trend toward more structured and academic early childhood programs is misguided.

Autism-associated neuroligin-3 mutations commonly impair striatal circuits to boost repetitive behaviors.

PubMed

Rothwell, Patrick E; Fuccillo, Marc V; Maxeiner, Stephan; Hayton, Scott J; Gokce, Ozgun; Lim, Byung Kook; Fowler, Stephen C; Malenka, Robert C; Südhof, Thomas C

2014-07-03

In humans, neuroligin-3 mutations are associated with autism, whereas in mice, the corresponding mutations produce robust synaptic and behavioral changes. However, different neuroligin-3 mutations cause largely distinct phenotypes in mice, and no causal relationship links a specific synaptic dysfunction to a behavioral change. Using rotarod motor learning as a proxy for acquired repetitive behaviors in mice, we found that different neuroligin-3 mutations uniformly enhanced formation of repetitive motor routines. Surprisingly, neuroligin-3 mutations caused this phenotype not via changes in the cerebellum or dorsal striatum but via a selective synaptic impairment in the nucleus accumbens/ventral striatum. Here, neuroligin-3 mutations increased rotarod learning by specifically impeding synaptic inhibition onto D1-dopamine receptor-expressing but not D2-dopamine receptor-expressing medium spiny neurons. Our data thus suggest that different autism-associated neuroligin-3 mutations cause a common increase in acquired repetitive behaviors by impairing a specific striatal synapse and thereby provide a plausible circuit substrate for autism pathophysiology. Copyright © 2014 Elsevier Inc. All rights reserved.

Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction

PubMed Central

Liljeholm, Mimi; Tricomi, Elizabeth; O’Doherty, John P.; Balleine, Bernard W.

2011-01-01

Contingency theories of goal-directed action propose that experienced disjunctions between an action and its specific consequences, as well as conjunctions between these events, contribute to encoding the action-outcome association. Although considerable behavioral research in rats and humans has provided evidence for this proposal, relatively little is known about the neural processes that contribute to the two components of the contingency calculation. Specifically, while recent findings suggest that the influence of action-outcome conjunctions on goal-directed learning is mediated by a circuit involving ventromedial prefrontal, medial orbitofrontal cortex and dorsomedial striatum, the neural processes that mediate the influence of experienced disjunctions between these events are unknown. Here we show differential responses to probabilities of conjunctive and disjunctive reward deliveries in the ventromedial prefrontal cortex, the dorsomedial striatum, and the inferior frontal gyrus. Importantly, activity in the inferior parietal lobule and the left middle frontal gyrus varied with a formal integration of the two reward probabilities, ΔP, as did response rates and explicit judgments of the causal efficacy of the action. PMID:21325514

Penalized regression procedures for variable selection in the potential outcomes framework

PubMed Central

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L.

2015-01-01

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple ‘impute, then select’ class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems, and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data and imputation are drawn. A difference LASSO algorithm is defined, along with its multiple imputation analogues. The procedures are illustrated using a well-known right heart catheterization dataset. PMID:25628185

Empirical evaluation of the conceptual model underpinning a regional aquatic long-term monitoring program using causal modelling

USGS Publications Warehouse

Irvine, Kathryn M.; Miller, Scott; Al-Chokhachy, Robert K.; Archer, Erik; Roper, Brett B.; Kershner, Jeffrey L.

2015-01-01

Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of adaptive monitoring. We suspect our situation is not unique and many programs may suffer from the same inferential disconnect. Commonly, the survey design is optimized for robust estimates of regional status and trend detection and not necessarily to provide statistical inferences on the causal mechanisms outlined in the conceptual model, even though these relationships are typically used to justify and promote the long-term monitoring of a chosen ecological indicator. Our application demonstrates a process for empirical evaluation of conceptual models and exemplifies the need for such interim assessments in order for programs to evolve and persist.

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis.

PubMed

Wang, Yuan; Wang, Yao; Lui, Yvonne W

2018-05-18

Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks. Copyright © 2018 Elsevier Inc. All rights reserved.

Loop Analysis of Causal Feedback in Epidemiology: An Illustration Relating To Urban Neighborhoods and Resident Depressive Experiences

PubMed Central

2008-01-01

The causal feedback implied by urban neighborhood conditions that shape human health experiences, that in turn shape neighborhood conditions through a complex causal web, raises a challenge for traditional epidemiological causal analyses. This article introduces the loop analysis method, and builds off of a core loop model linking neighborhood property vacancy rate, resident depressive symptoms, rate of neighborhood death, and rate of neighborhood exit in a feedback network. I justify and apply loop analysis to the specific example of depressive symptoms and abandoned urban residential property to show how inquiries into the behavior of causal systems can answer different kinds of hypotheses, and thereby compliment those of causal modeling using statistical models. Neighborhood physical conditions that are only indirectly influenced by depressive symptoms may nevertheless manifest in the mental health experiences of their residents; conversely, neighborhood physical conditions may be a significant mental health risk for the population of neighborhood residents. I find that participatory greenspace programs are likely to produce adaptive responses in depressive symptoms and different neighborhood conditions, which are different in character to non-participatory greenspace interventions. PMID:17706851

Causality discovery technology

NASA Astrophysics Data System (ADS)

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

2012-11-01

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

Effect of tDCS on task relevant and irrelevant perceptual learning of complex objects.

PubMed

Van Meel, Chayenne; Daniels, Nicky; de Beeck, Hans Op; Baeck, Annelies

2016-01-01

During perceptual learning the visual representations in the brain are altered, but these changes' causal role has not yet been fully characterized. We used transcranial direct current stimulation (tDCS) to investigate the role of higher visual regions in lateral occipital cortex (LO) in perceptual learning with complex objects. We also investigated whether object learning is dependent on the relevance of the objects for the learning task. Participants were trained in two tasks: object recognition using a backward masking paradigm and an orientation judgment task. During both tasks, an object with a red line on top of it were presented in each trial. The crucial difference between both tasks was the relevance of the object: the object was relevant for the object recognition task, but not for the orientation judgment task. During training, half of the participants received anodal tDCS stimulation targeted at the lateral occipital cortex (LO). Afterwards, participants were tested on how well they recognized the trained objects, the irrelevant objects presented during the orientation judgment task and a set of completely new objects. Participants stimulated with tDCS during training showed larger improvements of performance compared to participants in the sham condition. No learning effect was found for the objects presented during the orientation judgment task. To conclude, this study suggests a causal role of LO in relevant object learning, but given the rather low spatial resolution of tDCS, more research on the specificity of this effect is needed. Further, mere exposure is not sufficient to train object recognition in our paradigm.

Combining Group-Based Trajectory Modeling and Propensity Score Matching for Causal Inferences in Nonexperimental Longitudinal Data

ERIC Educational Resources Information Center

Haviland, Amelia; Nagin, Daniel S.; Rosenbaum, Paul R.; Tremblay, Richard E.

2008-01-01

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This article describes and applies a method for using observational longitudinal data to make more transparent causal inferences about the…

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

ERIC Educational Resources Information Center

Ghosh, Rajashi; Jacobson, Seth

2016-01-01

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

The Effects of the Collaborative Leadership Style Provided by Professional Learning Communities on the Tennessee Comprehensive Assessment Program Mean Value-Added Scores in Selected Middle Schools in East Tennessee

ERIC Educational Resources Information Center

Jones, Jone S.

2011-01-01

This causal-comparative study examined the effects of the collaborative leadership style provided by professional learning communities (PLCs) on the students' Tennessee Comprehensive Assessment Program (TCAP) mean Value-Added achievement scores in language arts and mathematics. The study used twenty-nine selected middle schools in eight different…

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences

PubMed Central

2016-01-01

Emerging studies indicate that several species such as corvids, apes and children solve ‘The Crow and the Pitcher’ task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause–effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended ‘learning–prediction–abstraction’ loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. PMID:27466440

A deep learning framework for causal shape transformation.

PubMed

Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

2018-02-01

Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

Zika in the Americas, year 2: What have we learned? What gaps remain? A report from the Global Virus Network.

PubMed

Aliota, Matthew T; Bassit, Leda; Bradrick, Shelton S; Cox, Bryan; Garcia-Blanco, Mariano A; Gavegnano, Christina; Friedrich, Thomas C; Golos, Thaddeus G; Griffin, Diane E; Haddow, Andrew D; Kallas, Esper G; Kitron, Uriel; Lecuit, Marc; Magnani, Diogo M; Marrs, Caroline; Mercer, Natalia; McSweegan, Edward; Ng, Lisa F P; O'Connor, David H; Osorio, Jorge E; Ribeiro, Guilherme S; Ricciardi, Michael; Rossi, Shannan L; Saade, George; Schinazi, Raymond F; Schott-Lerner, Geraldine O; Shan, Chao; Shi, Pei-Yong; Watkins, David I; Vasilakis, Nikos; Weaver, Scott C

2017-08-01

In response to the outbreak of Zika virus (ZIKV) infection in the Western Hemisphere and the recognition of a causal association with fetal malformations, the Global Virus Network (GVN) assembled an international taskforce of virologists to promote basic research, recommend public health measures and encourage the rapid development of vaccines, antiviral therapies and new diagnostic tests. In this article, taskforce members and other experts review what has been learned about ZIKV-induced disease in humans, its modes of transmission and the cause and nature of associated congenital manifestations. After describing the make-up of the taskforce, we summarize the emergence of ZIKV in the Americas, Africa and Asia, its spread by mosquitoes, and current control measures. We then review the spectrum of primary ZIKV-induced disease in adults and children, sites of persistent infection and sexual transmission, then examine what has been learned about maternal-fetal transmission and the congenital Zika syndrome, including knowledge obtained from studies in laboratory animals. Subsequent sections focus on vaccine development, antiviral therapeutics and new diagnostic tests. After reviewing current understanding of the mechanisms of emergence of Zika virus, we consider the likely future of the pandemic. Copyright © 2017. Published by Elsevier B.V.

How people learn about causal influence when there are many possible causes: A model based on informative transitions.

PubMed

Derringer, Cory; Rottman, Benjamin Margolin

2018-05-01

Four experiments tested how people learn cause-effect relations when there are many possible causes of an effect. When there are many cues, even if all the cues together strongly predict the effect, the bivariate relation between each individual cue and the effect can be weak, which can make it difficult to detect the influence of each cue. We hypothesized that when detecting the influence of a cue, in addition to learning from the states of the cues and effect (e.g., a cue is present and the effect is present), which is hypothesized by multiple existing theories of learning, participants would also learn from transitions - how the cues and effect change over time (e.g., a cue turns on and the effect turns on). We found that participants were better able to identify positive and negative cues in an environment in which only one cue changed from one trial to the next, compared to multiple cues changing (Experiments 1A, 1B). Within a single learning sequence, participants were also more likely to update their beliefs about causal strength when one cue changed at a time ('one-change transitions') than when multiple cues changed simultaneously (Experiment 2). Furthermore, learning was impaired when the trials were grouped by the state of the effect (Experiment 3) or when the trials were grouped by the state of a cue (Experiment 4), both of which reduce the number of one-change transitions. We developed a modification of the Rescorla-Wagner algorithm to model this 'Informative Transitions' learning processes. Copyright © 2018 Elsevier Inc. All rights reserved.

Design of fuzzy cognitive maps using neural networks for predicting chaotic time series.

PubMed

Song, H J; Miao, C Y; Shen, Z Q; Roel, W; Maja, D H; Francky, C

2010-12-01

As a powerful paradigm for knowledge representation and a simulation mechanism applicable to numerous research and application fields, Fuzzy Cognitive Maps (FCMs) have attracted a great deal of attention from various research communities. However, the traditional FCMs do not provide efficient methods to determine the states of the investigated system and to quantify causalities which are the very foundation of the FCM theory. Therefore in many cases, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to model subjectivity and difficulties with accessing its reliability. In this paper, we propose a fuzzy neural network to enhance the learning ability of FCMs so that the automatic determination of membership functions and quantification of causalities can be incorporated with the inference mechanism of conventional FCMs. In this manner, FCM models of the investigated systems can be automatically constructed from data, and therefore are independent of the experts. Furthermore, we employ mutual subsethood to define and describe the causalities in FCMs. It provides more explicit interpretation for causalities in FCMs and makes the inference process easier to understand. To validate the performance, the proposed approach is tested in predicting chaotic time series. The simulation studies show the effectiveness of the proposed approach. Copyright © 2010 Elsevier Ltd. All rights reserved.

The explanatory structure of unexplainable events: Causal constraints on magical reasoning.

PubMed

Shtulman, Andrew; Morgan, Caitlin

2017-10-01

A common intuition, often captured in fiction, is that some impossible events (e.g., levitating a stone) are "more impossible" than others (e.g., levitating a feather). We investigated the source of this intuition, hypothesizing that graded notions of impossibility arise from explanatory considerations logically precluded by the violation at hand but still taken into account. Studies 1-4 involved college undergraduates (n = 357), and Study 5 involved preschool-aged children (n = 32). In Studies 1 and 2, participants saw pairs of magical spells that violated one of 18 causal principles-six physical, six biological, and six psychological-and were asked to indicate which spell would be more difficult to learn. Both spells violated the same causal principle but differed in their relation to a subsidiary principle. Participants' judgments of spell difficulty honored the subsidiary principle, even when participants were given the option of judging the two spells equally difficult. Study 3 replicated those effects with Likert-type ratings; Study 4 replicated them in an open-ended version of the task in which participants generated their own causal violations; and Study 5 replicated them with children. Taken together, these findings suggest that events that defy causal explanation are interpreted in terms of explanatory considerations that hold in the absence of such violations.

The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

PubMed

Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

2017-06-15

Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Understanding variation in human fertility: what can we learn from evolutionary demography?

PubMed

Sear, Rebecca; Lawson, David W; Kaplan, Hillard; Shenk, Mary K

2016-04-19

Decades of research on human fertility has presented a clear picture of how fertility varies, including its dramatic decline over the last two centuries in most parts of the world. Why fertility varies, both between and within populations, is not nearly so well understood. Fertility is a complex phenomenon, partly physiologically and partly behaviourally determined, thus an interdisciplinary approach is required to understand it. Evolutionary demographers have focused on human fertility since the 1980s. The first wave of evolutionary demographic research made major theoretical and empirical advances, investigating variation in fertility primarily in terms of fitness maximization. Research focused particularly on variation within high-fertility populations and small-scale subsistence societies and also yielded a number of hypotheses for why fitness maximization seems to break down as fertility declines during the demographic transition. A second wave of evolutionary demography research on fertility is now underway, paying much more attention to the cultural and psychological mechanisms underpinning fertility. It is also engaging with the complex, multi-causal nature of fertility variation, and with understanding fertility in complex modern and transitioning societies. Here, we summarize the history of evolutionary demographic work on human fertility, describe the current state of the field, and suggest future directions. © 2016 The Author(s).

Understanding variation in human fertility: what can we learn from evolutionary demography?

PubMed Central

Sear, Rebecca; Lawson, David W.; Kaplan, Hillard

2016-01-01

Decades of research on human fertility has presented a clear picture of how fertility varies, including its dramatic decline over the last two centuries in most parts of the world. Why fertility varies, both between and within populations, is not nearly so well understood. Fertility is a complex phenomenon, partly physiologically and partly behaviourally determined, thus an interdisciplinary approach is required to understand it. Evolutionary demographers have focused on human fertility since the 1980s. The first wave of evolutionary demographic research made major theoretical and empirical advances, investigating variation in fertility primarily in terms of fitness maximization. Research focused particularly on variation within high-fertility populations and small-scale subsistence societies and also yielded a number of hypotheses for why fitness maximization seems to break down as fertility declines during the demographic transition. A second wave of evolutionary demography research on fertility is now underway, paying much more attention to the cultural and psychological mechanisms underpinning fertility. It is also engaging with the complex, multi-causal nature of fertility variation, and with understanding fertility in complex modern and transitioning societies. Here, we summarize the history of evolutionary demographic work on human fertility, describe the current state of the field, and suggest future directions. PMID:27022071

Differences in the early cognitive development of children and great apes.

PubMed

Wobber, Victoria; Herrmann, Esther; Hare, Brian; Wrangham, Richard; Tomasello, Michael

2014-04-01

There is very little research comparing great ape and human cognition developmentally. In the current studies we compared a cross-sectional sample of 2- to 4-year-old human children (n=48) with a large sample of chimpanzees and bonobos in the same age range (n=42, hereafter: apes) on a broad array of cognitive tasks. We then followed a group of juvenile apes (n=44) longitudinally over 3 years to track their cognitive development in greater detail. In skills of physical cognition (space, causality, quantities), children and apes performed comparably at 2 years of age, but by 4 years of age children were more advanced (whereas apes stayed at their 2-year-old performance levels). In skills of social cognition (communication, social learning, theory of mind), children out-performed apes already at 2 years, and increased this difference even more by 4 years. Patterns of development differed more between children and apes in the social domain than the physical domain, with support for these patterns present in both the cross-sectional and longitudinal ape data sets. These results indicate key differences in the pattern and pace of cognitive development between humans and other apes, particularly in the early emergence of specific social cognitive capacities in humans. Copyright © 2013 Wiley Periodicals, Inc.

Agents and Patients in Physical Settings: Linguistic Cues Affect the Assignment of Causality in German and Tongan

PubMed Central

Bender, Andrea; Beller, Sieghard

2017-01-01

Linguistic cues may be considered a potent tool for focusing attention on causes or effects. In this paper, we explore how different cues affect causal assignments in German and Tongan. From a larger screening study, two parts are reported here: Part 1 dealt with syntactic variations, including word order (agent vs. patient in first/subject position) and case marking (e.g., as ergative vs. non-ergative in Tongan) depending on verb type (transitive vs. intransitive). For two physical settings (wood floating on water and a man breaking a glass), participants assigned causality to the two entities involved. In the floating setting, speakers of the two languages were sensitive to syntactic variations, but differed in the entity regarded as causative. In the breaking setting, the human agent was uniformly regarded as causative. Part 2 dealt with implicit verb causality. Participants assigned causality to subject or object of 16 verbs presented in minimal social scenarios. In German, all verbs showed a subject (agent) focus; in Tongan, the focus depended on the verb; and for nine verbs, the focus differed across languages. In conclusion, we discuss the question of domain-specificity of causal cognition, the role of the ergative as causal marker, and more general differences between languages. PMID:28736538

Planning for the Human Dimensions of Oil Spills and Spill Response

NASA Astrophysics Data System (ADS)

Webler, Thomas; Lord, Fabienne

2010-04-01

Oil spill contingency planners need an improved approach to understanding and planning for the human dimensions of oil spills. Drawing on existing literature in social impact assessment, natural hazards, human ecology, adaptive management, global change and sustainability, we develop an integrative approach to understanding and portraying the human dimensions impacts of stressors associated with oil spill events. Our approach is based on three fundamental conclusions that are drawn from this literature review. First, it is productive to acknowledge that, while stressors can produce human impacts directly, they mainly affect intermediary processes and changes to these processes produce human impacts. Second, causal chain modeling taken from hazard management literature provides a means to document how oil spill stressors change processes and produce human impacts. Third, concepts from the global change literature on vulnerability enrich causal models in ways that make more obvious how management interventions lessen hazards and mitigate associated harm. Using examples from recent spill events, we illustrate how these conclusions can be used to diagrammatically portray the human dimensions of oil spills.

Wrongdoing and Retribution: Children's Conceptions of Illness Causality in a Central Indian Village.

PubMed

Froerer, Peggy

2007-12-01

This paper is a study of children's conceptions of illness causality. Based on ethnographic research in a central Indian tribal community, it is a response to the lack of systematic attention within mainstream anthropology on children, and within medical anthropology on children's understanding of illness causation. A combination of participant observation and structured interviews was used to examine local distinctions between 'natural' and 'supernatural' illness, which are underpinned by ideas about supernatural retribution. The focus in this paper is on how children learn and reason about such ideas, and on the processes by which they assume culpability for 'supernatural' illnesses. By arguing that children do not simply replicate adult conceptions about illness causality but instead apply their own experience to their understanding and representation of such ideas, this paper challenges taken-for-granted assumptions about the acquisition and reproduction of cultural knowledge.

Causal Structure of Brain Physiology after Brain Injury from Subarachnoid Hemorrhage.

PubMed

Claassen, Jan; Rahman, Shah Atiqur; Huang, Yuxiao; Frey, Hans-Peter; Schmidt, J Michael; Albers, David; Falo, Cristina Maria; Park, Soojin; Agarwal, Sachin; Connolly, E Sander; Kleinberg, Samantha

2016-01-01

High frequency physiologic data are routinely generated for intensive care patients. While massive amounts of data make it difficult for clinicians to extract meaningful signals, these data could provide insight into the state of critically ill patients and guide interventions. We develop uniquely customized computational methods to uncover the causal structure within systemic and brain physiologic measures recorded in a neurological intensive care unit after subarachnoid hemorrhage. While the data have many missing values, poor signal-to-noise ratio, and are composed from a heterogeneous patient population, our advanced imputation and causal inference techniques enable physiologic models to be learned for individuals. Our analyses confirm that complex physiologic relationships including demand and supply of oxygen underlie brain oxygen measurements and that mechanisms for brain swelling early after injury may differ from those that develop in a delayed fashion. These inference methods will enable wider use of ICU data to understand patient physiology.

Human Papillomavirus Drives Tumor Development Throughout the Head and Neck: Improved Prognosis Is Associated With an Immune Response Largely Restricted to the Oropharynx

PubMed Central

Chakravarthy, Ankur; Henderson, Stephen; Thirdborough, Stephen M.; Ottensmeier, Christian H.; Su, Xiaoping; Lechner, Matt; Feber, Andrew; Thomas, Gareth J.

2016-01-01

Purpose In squamous cell carcinomas of the head and neck (HNSCC), the increasing incidence of oropharyngeal squamous cell carcinomas (OPSCCs) is attributable to human papillomavirus (HPV) infection. Despite commonly presenting at late stage, HPV-driven OPSCCs are associated with improved prognosis compared with HPV-negative disease. HPV DNA is also detectable in nonoropharyngeal (non-OPSCC), but its pathogenic role and clinical significance are unclear. The objectives of this study were to determine whether HPV plays a causal role in non-OPSCC and to investigate whether HPV confers a survival benefit in these tumors. Methods Meta-analysis was used to build a cross-tissue gene-expression signature for HPV-driven cancer. Classifiers trained by machine-learning approaches were used to predict the HPV status of 520 HNSCCs profiled by The Cancer Genome Atlas project. DNA methylation data were similarly used to classify 464 HNSCCs and these analyses were integrated with genomic, histopathology, and survival data to permit a comprehensive comparison of HPV transcript-positive OPSCC and non-OPSCC. Results HPV-driven tumors accounted for 4.1% of non-OPSCCs. Regardless of anatomic site, HPV+ HNSCCs shared highly similar gene expression and DNA methylation profiles; nonkeratinizing, basaloid histopathological features; and lack of TP53 or CDKN2A alterations. Improved overall survival, however, was largely restricted to HPV-driven OPSCCs, which were associated with increased levels of tumor-infiltrating lymphocytes compared with HPV-driven non-OPSCCs. Conclusion Our analysis identified a causal role for HPV in transcript-positive non-OPSCCs throughout the head and neck. Notably, however, HPV-driven non-OPSCCs display a distinct immune microenvironment and clinical behavior compared with HPV-driven OPSCCs. PMID:27863190

Exploring the influence of students' attributions for success on their self-regulation in pathophysiology.

PubMed

Dunn, Karee E; Osborne, Cara; Link, Hope J

2012-06-01

Pathophysiology is a difficult course both for students to take and for instructors to teach. However, little research has explored learner characteristics that teachers may address through targeted instruction to make both the teaching and learning experience better. This study examined the influence of students' causal attributions for success on their self-regulated learning, which is strongly associated with positive learning outcomes. Results indicated that ability, effort, and luck attributions for success collectively influenced Pathophysiology students' self-regulated learning and that ability was the most potent influence. The findings and the implication for teaching are discussed. Copyright 2012, SLACK Incorporated.

The latent causal chain of industrial water pollution in China.

PubMed

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

2015-01-01

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

Koilocytes indicate a role for human papilloma virus in breast cancer

PubMed Central

Lawson, J S; Glenn, W K; Heng, B; Ye, Y; Tran, B; Lutze-Mann, L; Whitaker, N J

2009-01-01

Background: High-risk human papilloma viruses (HPVs) are candidates as causal viruses in breast cancer. The scientific challenge is to determine whether HPVs are causal and not merely passengers or parasites. Studies of HPV-related koilocytes in breast cancer offer an opportunity to address this crucial issue. Koilocytes are epithelial cells characterised by perinuclear haloes surrounding condensed nuclei and are commonly present in cervical intraepithelial neoplasia. Koilocytosis is accepted as pathognomonic (characteristic of a particular disease) of HPV infection. The aim of this investigation is to determine whether putative koilocytes in normal and malignant breast tissues are because of HPV infection. Methods: Archival formalin-fixed normal and malignant breast specimens were investigated by histology, in situ PCR with confirmation of the findings by standard PCR and sequencing of the products, plus immunohistochemistry to identify HPV E6 oncoproteins. Results: human papilloma virus-associated koilocytes were present in normal breast skin and lobules and in the breast skin and cancer tissue of patients with ductal carcinoma in situ (DCIS) and invasive ductal carcinomas (IDCs). Interpretation: As koilocytes are known to be the precursors of some HPV-associated cervical cancer, it follows that HPVs may be causally associated with breast cancer. PMID:19773762

Marginal Structural Models with Counterfactual Effect Modifiers.

PubMed

Zheng, Wenjing; Luo, Zhehui; van der Laan, Mark J

2018-06-08

In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers are also of interest. In this work, we investigate the robust and efficient estimation of the Counterfactual-History-Adjusted Marginal Structural Model (van der Laan MJ, Petersen M. Statistical learning of origin-specific statically optimal individualized treatment rules. Int J Biostat. 2007;3), which models the conditional intervention-specific mean outcome given a counterfactual modifier history in an ideal experiment. We establish the semiparametric efficiency theory for these models, and present a substitution-based, semiparametric efficient and doubly robust estimator using the targeted maximum likelihood estimation methodology (TMLE, e.g. van der Laan MJ, Rubin DB. Targeted maximum likelihood learning. Int J Biostat. 2006;2, van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data, 1st ed. Springer Series in Statistics. Springer, 2011). To facilitate implementation in applications where the effect modifier is high dimensional, our third contribution is a projected influence function (and the corresponding projected TMLE estimator), which retains most of the robustness of its efficient peer and can be easily implemented in applications where the use of the efficient influence function becomes taxing. We compare the projected TMLE estimator with an Inverse Probability of Treatment Weighted estimator (e.g. Robins JM. Marginal structural models. In: Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1-10. 1997a, Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. 2000;11:561-570), and a non-targeted G-computation estimator (Robins JM. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Math Modell. 1986;7:1393-1512.). The comparative performance of these estimators is assessed in a simulation study. The use of the projected TMLE estimator is illustrated in a secondary data analysis for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial where effect modifiers are subject to missing at random.

Inability to activate Rac1-dependent forgetting contributes to behavioral inflexibility in mutants of multiple autism-risk genes

PubMed Central

Dong, Tao; He, Jing; Wang, Shiqing; Wang, Lianzhang; Cheng, Yuqi; Zhong, Yi

2016-01-01

The etiology of autism is so complicated because it involves the effects of variants of several hundred risk genes along with the contribution of environmental factors. Therefore, it has been challenging to identify the causal paths that lead to the core autistic symptoms such as social deficit, repetitive behaviors, and behavioral inflexibility. As an alternative approach, extensive efforts have been devoted to identifying the convergence of the targets and functions of the autism-risk genes to facilitate mapping out causal paths. In this study, we used a reversal-learning task to measure behavioral flexibility in Drosophila and determined the effects of loss-of-function mutations in multiple autism-risk gene homologs in flies. Mutations of five autism-risk genes with diversified molecular functions all led to a similar phenotype of behavioral inflexibility indicated by impaired reversal-learning. These reversal-learning defects resulted from the inability to forget or rather, specifically, to activate Rac1 (Ras-related C3 botulinum toxin substrate 1)-dependent forgetting. Thus, behavior-evoked activation of Rac1-dependent forgetting has a converging function for autism-risk genes. PMID:27335463

Current Status of Research in Teaching and Learning Evolution: I. Philosophical/Epistemological Issues

NASA Astrophysics Data System (ADS)

Smith, Mike U.

2010-06-01

Scholarship that addresses teaching and learning about evolution has rapidly increased in recent years. This review of that scholarship first addresses the philosophical/epistemological issues that impinge on teaching and learning about evolution, including the proper philosophical goals of evolution instruction; the correlational and possibly causal relationships among knowing, understanding, accepting, and believing; and the factors that affect student understanding, acceptance, and/or belief. Second, I summarize the specific epistemological issues involved, including empiricism, naturalism, philosophical vs methodological materialism, science vs religion as non-overlapping magisteria, and science as a way of knowing. Third, the paper critically reviews the strengths and weaknesses of the research tools available to measure the nature of science, epistemological beliefs, and especially the acceptance of evolution. Based on these findings, further research in these areas, especially study of the factors that cause lack of explanatory coherence as well as replications of studies that promise to explain current confusing findings about the interrelationships among student understanding, acceptance, and belief in evolution, are called for. In addition, this review calls for more longitudinal studies to delineate causal connections as well as improved measurement tools.

Bodily systems and the spatial-functional structure of the human body.

PubMed

Smith, Barry; Munn, Katherine; Papakin, Igor

2004-01-01

The human body is a system made of systems. The body is divided into bodily systems proper, such as the endocrine and circulatory systems, which are subdivided into many sub-systems at a variety of levels, whereby all systems and subsystems engage in massive causal interaction with each other and with their surrounding environments. Here we offer an explicit definition of bodily system and provide a framework for understanding their causal interactions. Medical sciences provide at best informal accounts of basic notions such as system, process, and function, and while such informality is acceptable in documentation created for human beings, it falls short of what is needed for computer representations. In our analysis we will accordingly provide the framework for a formal definition of bodily system and of associated notions.

The causality between smoking and lung cancer among groups and individuals: addressing issues in tobacco litigation in South Korea

PubMed Central

Khang, Young-Ho

2015-01-01

This article discusses issues on the causality between smoking and lung cancer, which have been raised during the tobacco litigation in South Korea. It should be recognized that the explanatory ability of risk factor(s) for inter-individual variations in disease occurrence is different from the causal contribution of the risk factor(s) to disease occurrence. The affected subjects of the tobacco litigation in South Korea are lung cancer patients with a history of cigarette smoking. Thus, the attributable fraction of the exposed rather than the population attributable fraction should be used in the tobacco litigation regarding the causal contribution of smoking to lung cancer. Scientific evidence for the causal relationship between smoking and lung cancer is based on studies of individuals and groups, studies in animals and humans, studies that are observational or experimental, studies in laboratories and communities, and studies in both underdeveloped and developed countries. The scientific evidence collected is applicable to both groups and individuals. The probability of causation, which is calculated based on the attributable fraction for the association between smoking and lung cancer, could be utilized as evidence to prove causality in individuals. PMID:26137845

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

PubMed

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

2011-07-01

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

The causality between smoking and lung cancer among groups and individuals: addressing issues in tobacco litigation in South Korea.

PubMed

Khang, Young-Ho

2015-01-01

This article discusses issues on the causality between smoking and lung cancer, which have been raised during the tobacco litigation in South Korea. It should be recognized that the explanatory ability of risk factor(s) for inter-individual variations in disease occurrence is different from the causal contribution of the risk factor(s) to disease occurrence. The affected subjects of the tobacco litigation in South Korea are lung cancer patients with a history of cigarette smoking. Thus, the attributable fraction of the exposed rather than the population attributable fraction should be used in the tobacco litigation regarding the causal contribution of smoking to lung cancer. Scientific evidence for the causal relationship between smoking and lung cancer is based on studies of individuals and groups, studies in animals and humans, studies that are observational or experimental, studies in laboratories and communities, and studies in both underdeveloped and developed countries. The scientific evidence collected is applicable to both groups and individuals. The probability of causation, which is calculated based on the attributable fraction for the association between smoking and lung cancer, could be utilized as evidence to prove causality in individuals.

Common sense: folk wisdom that ethnobiological and ethnomedical research cannot afford to ignore

PubMed Central

2013-01-01

Common sense [CS], especially that of the non-scientist, can have predictive power to identify promising research avenues, as humans anywhere on Earth have always looked for causal links to understand, shape and control the world around them. CS is based on the experience of many individuals and is thus believed to hold some truths. Outcomes predicted by CS are compatible with observations made by whole populations and have survived tests conducted by a plethora of non-scientists. To explore our claim, we provide 4 examples of empirical insights (relevant to probably all ethnic groups on Earth) into causal phenomena predicted by CS: (i) “humans must have a sense of time”, (ii) “at extreme latitudes, more people have the winter blues”, (iii) “sleep is a cure for many ills” and (iv) “social networks affect health and disease”. While CS is fallible, it should not be ignored by science – however improbable or self-evident the causal relationships predicted by CS may appear to be. PMID:24295068

Healthier students are better learners: a missing link in school reforms to close the achievement gap.

PubMed

Basch, Charles E

2011-10-01

This article provides an introduction to the October 2011 special issue of the Journal of School Health on "Healthier Students Are Better Learners." Literature was reviewed and synthesized to identify health problems affecting school-aged youth that are highly prevalent, disproportionately affect urban minority youth, directly and indirectly causally affect academic achievement, and can be feasibly and effectively addressed through school health programs and services. Based on these criteria, 7 educationally relevant health disparities were selected as strategic priorities to help close the achievement gap: (1) vision, (2) asthma, (3) teen pregnancy, (4) aggression and violence, (5) physical activity, (6) breakfast, and (7) inattention and hyperactivity. Research clearly shows that these health problems influence students' motivation and ability to learn. Disparities among urban minority youth are outlined, along with the causal pathways through which each adversely affects academic achievement, including sensory perceptions, cognition, school connectedness, absenteeism, and dropping out. Evidence-based approaches that schools can implement to address these problems are presented. These health problems and the causal pathways they influence have interactive and a synergistic effect, which is why they must be addressed collectively using a coordinated approach. No matter how well teachers are prepared to teach, no matter what accountability measures are put in place, no matter what governing structures are established for schools, educational progress will be profoundly limited if students are not motivated and able to learn. Particular health problems play a major role in limiting the motivation and ability to learn of urban minority youth. This is why reducing these disparities through a coordinated approach warrants validation as a cohesive school improvement initiative to close the achievement gap. Local, state, and national policies for implementing this recommendation are suggested. © 2011, American School Health Association.

Illusory Reversal of Causality between Touch and Vision has No Effect on Prism Adaptation Rate.

PubMed

Tanaka, Hirokazu; Homma, Kazuhiro; Imamizu, Hiroshi

2012-01-01

Learning, according to Oxford Dictionary, is "to gain knowledge or skill by studying, from experience, from being taught, etc." In order to learn from experience, the central nervous system has to decide what action leads to what consequence, and temporal perception plays a critical role in determining the causality between actions and consequences. In motor adaptation, causality between action and consequence is implicitly assumed so that a subject adapts to a new environment based on the consequence caused by her action. Adaptation to visual displacement induced by prisms is a prime example; the visual error signal associated with the motor output contributes to the recovery of accurate reaching, and a delayed feedback of visual error can decrease the adaptation rate. Subjective feeling of temporal order of action and consequence, however, can be modified or even reversed when her sense of simultaneity is manipulated with an artificially delayed feedback. Our previous study (Tanaka et al., 2011; Exp. Brain Res.) demonstrated that the rate of prism adaptation was unaffected when the subjective delay of visual feedback was shortened. This study asked whether subjects could adapt to prism adaptation and whether the rate of prism adaptation was affected when the subjective temporal order was illusory reversed. Adapting to additional 100 ms delay and its sudden removal caused a positive shift of point of simultaneity in a temporal order judgment experiment, indicating an illusory reversal of action and consequence. We found that, even in this case, the subjects were able to adapt to prism displacement with the learning rate that was statistically indistinguishable to that without temporal adaptation. This result provides further evidence to the dissociation between conscious temporal perception and motor adaptation.

Human Capital or Human Connections? The Cultural Meanings of Education in Brazil

ERIC Educational Resources Information Center

Bartlett, Lesley

2007-01-01

Background/Context: In the field of educational research, conventional wisdom holds that primary-level schooling, specifically literacy acquisition, promotes economic mobility for individuals and economic development for the nation. This belief is rooted in human capital theory, the causal argument claiming that state investment in schooling or…

Role of visceral adipose tissue in aging.

PubMed

Huffman, Derek M; Barzilai, Nir

2009-10-01

Visceral fat (VF) accretion is a hallmark of aging in humans. Epidemiologic studies have implicated abdominal obesity as a major risk factor for insulin resistance, type 2 diabetes, cardiovascular disease, metabolic syndrome and death. Studies utilizing novel rodent models of visceral obesity and surgical strategies in humans have been undertaken to determine if subcutaneous (SC) abdominal or VF are causally linked to age-related diseases. Specific depletion or expansion of the VF depot using genetic or surgical tools in rodents has been shown to have direct effects on disease risk. In contrast, surgically removing large quantities of SC fat does not consistently improve metabolic parameters in humans or rodents, while benefits were observed with SC fat expansion in mice, suggesting that SC fat accrual is not an important contributor to metabolic decline. There is also compelling evidence in humans that abdominal obesity is a stronger risk factor for mortality risk than general obesity. Likewise, we have shown that surgical removal of VF improves mean and maximum lifespan in rats, providing the first causal evidence that VF depletion may be an important underlying cause of improved lifespan with caloric restriction. This review provides both corollary and causal evidence for the importance of accounting for body fat distribution, and specifically VF, when assessing disease and mortality risk. Given the hazards of VF accumulation on health, treatment strategies aimed at selectively depleting VF should be considered as a viable tool to effectively reduce disease risk in humans.

Control of cognition and adaptive behavior by the GLP/G9a epigenetic suppressor complex

PubMed Central

Schaefer, Anne; Sampath, Srihari C.; Intrator, Adam; Min, Alice; Gertler, Tracy S.; Surmeier, D. James; Tarakhovsky, Alexander; Greengard, Paul

2009-01-01

SUMMARY The genetic basis of cognition and behavioral adaptation to the environment remains poorly understood. Here we demonstrate that the histone methyltransferase complex GLP/G9a controls cognition and adaptive responses in a region-specific fashion in the adult brain. Using conditional mutagenesis in mice, we show that postnatal, neuron-specific deficiency of GLP/G9a leads to de-repression of numerous non-neuronal and neuron progenitor genes in adult neurons. This transcriptional alteration is associated with complex behavioral abnormalities, including defects in learning, motivation and environmental adaptation. The behavioral changes triggered by GLP/G9a deficiency are similar to key symptoms of the human 9q34 mental retardation syndrome that is associated with structural alterations of the GLP gene. The likely causal role of GLP/G9a in mental retardation in mice and humans suggests a key role for the GLP/G9a controlled histone H3K9 di-methylation in regulation of brain function through maintenance of the transcriptional homeostasis in adult neurons. PMID:20005824

Mediodorsal thalamus hypofunction impairs flexible goal-directed behavior.

PubMed

Parnaudeau, Sébastien; Taylor, Kathleen; Bolkan, Scott S; Ward, Ryan D; Balsam, Peter D; Kellendonk, Christoph

2015-03-01

Cognitive inflexibility is a core symptom of several mental disorders including schizophrenia. Brain imaging studies in schizophrenia patients performing cognitive tasks have reported decreased activation of the mediodorsal thalamus (MD). Using a pharmacogenetic approach to model MD hypofunction, we recently showed that decreasing MD activity impairs reversal learning in mice. While this demonstrates causality between MD hypofunction and cognitive inflexibility, questions remain about the elementary cognitive processes that account for the deficit. Using the Designer Receptors Exclusively Activated by Designer Drugs system, we reversibly decreased MD activity during behavioral tasks assessing elementary cognitive processes inherent to flexible goal-directed behaviors, including extinction, contingency degradation, outcome devaluation, and Pavlovian-to-instrumental transfer (n = 134 mice). While MD hypofunction impaired reversal learning, it did not affect the ability to learn about nonrewarded cues or the ability to modulate action selection based on the outcome value. In contrast, decreasing MD activity delayed the ability to adapt to changes in the contingency between actions and their outcomes. In addition, while Pavlovian learning was not affected by MD hypofunction, decreasing MD activity during Pavlovian learning impaired the ability of conditioned stimuli to modulate instrumental behavior. Mediodorsal thalamus hypofunction causes cognitive inflexibility reflected by an impaired ability to adapt actions when their consequences change. Furthermore, it alters the encoding of environmental stimuli so that they cannot be properly utilized to guide behavior. Modulating MD activity could be a potential therapeutic strategy for promoting adaptive behavior in human subjects with cognitive inflexibility. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Is exposure to formaldehyde in air causally associated with leukemia?—A hypothesis-based weight-of-evidence analysis

PubMed Central

Rhomberg, Lorenz R; Bailey, Lisa A; Goodman, Julie E; Hamade, Ali K; Mayfield, David

2011-01-01

Recent scientific debate has focused on the potential for inhaled formaldehyde to cause lymphohematopoietic cancers, particularly leukemias, in humans. The concern stems from certain epidemiology studies reporting an association, although particulars of endpoints and dosimetry are inconsistent across studies and several other studies show no such effects. Animal studies generally report neither hematotoxicity nor leukemia associated with formaldehyde inhalation, and hematotoxicity studies in humans are inconsistent. Formaldehyde's reactivity has been thought to preclude systemic exposure following inhalation, and its apparent inability to reach and affect the target tissues attacked by known leukemogens has, heretofore, led to skepticism regarding its potential to cause human lymphohematopoietic cancers. Recently, however, potential modes of action for formaldehyde leukemogenesis have been hypothesized, and it has been suggested that formaldehyde be identified as a known human leukemogen. In this article, we apply our hypothesis-based weight-of-evidence (HBWoE) approach to evaluate the large body of evidence regarding formaldehyde and leukemogenesis, attending to how human, animal, and mode-of-action results inform one another. We trace the logic of inference within and across all studies, and articulate how one could account for the suite of available observations under the various proposed hypotheses. Upon comparison of alternative proposals regarding what causal processes may have led to the array of observations as we see them, we conclude that the case fora causal association is weak and strains biological plausibility. Instead, apparent association between formaldehyde inhalation and leukemia in some human studies is better interpreted as due to chance or confounding. PMID:21635189

Psychosocial Environment and Affective Outcomes in Technology-Rich Classrooms: Testing a Causal Model

ERIC Educational Resources Information Center

Dorman, Jeffrey P.; Fraser, Barry J.

2009-01-01

Research investigated classroom environment antecedent variables and student affective outcomes in Australian high schools. The Technology-Rich Outcomes-Focused Learning Environment Inventory (TROFLEI) was used to assess 10 classroom environment dimensions: student cohesiveness, teacher support, involvement, investigation, task orientation,…

Image segmentation using hidden Markov Gauss mixture models.

PubMed

Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M

2007-07-01

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

Own and Others' Prior Experiences Influence Children's Imitation of Causal Acts.

PubMed

Williamson, Rebecca A; Meltzoff, Andrew N

2011-07-01

Young children learn from others' examples, and they do so selectively. We examine whether the efficacy of prior experiences influences children's imitation. Thirty-six-month-olds had initial experience on a causal learning task either by performing the task themselves or by watching an adult perform it. The nature of the experience was manipulated such that the actor had either an easy or a difficult experience completing the task. Next, a second adult demonstrated an innovative technique for completing it. Children who had a difficult first-person experience, and those who had witnessed another person having difficulty, were significantly more likely to adopt and imitate the adult's innovation than those who had or witnessed an easy experience. Children who observed another were also more likely to imitate than were those who had the initial experience themselves. Imitation is influenced by prior experience, both when it is obtained through one's own hands-on motor manipulation and when it derives from observing the acts of others.

Win-Stay, Lose-Sample: a simple sequential algorithm for approximating Bayesian inference.

PubMed

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

2014-11-01

People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm "Win-Stay, Lose-Sample", inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a "mini-microgenetic method", investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments. Copyright © 2014 Elsevier Inc. All rights reserved.

Statistical learning using real-world scenes: extracting categorical regularities without conscious intent.

PubMed

Brady, Timothy F; Oliva, Aude

2008-07-01

Recent work has shown that observers can parse streams of syllables, tones, or visual shapes and learn statistical regularities in them without conscious intent (e.g., learn that A is always followed by B). Here, we demonstrate that these statistical-learning mechanisms can operate at an abstract, conceptual level. In Experiments 1 and 2, observers incidentally learned which semantic categories of natural scenes covaried (e.g., kitchen scenes were always followed by forest scenes). In Experiments 3 and 4, category learning with images of scenes transferred to words that represented the categories. In each experiment, the category of the scenes was irrelevant to the task. Together, these results suggest that statistical-learning mechanisms can operate at a categorical level, enabling generalization of learned regularities using existing conceptual knowledge. Such mechanisms may guide learning in domains as disparate as the acquisition of causal knowledge and the development of cognitive maps from environmental exploration.

Structural equation modeling: building and evaluating causal models: Chapter 8

USGS Publications Warehouse

Grace, James B.; Scheiner, Samuel M.; Schoolmaster, Donald R.

2015-01-01

Scientists frequently wish to study hypotheses about causal relationships, rather than just statistical associations. This chapter addresses the question of how scientists might approach this ambitious task. Here we describe structural equation modeling (SEM), a general modeling framework for the study of causal hypotheses. Our goals are to (a) concisely describe the methodology, (b) illustrate its utility for investigating ecological systems, and (c) provide guidance for its application. Throughout our presentation, we rely on a study of the effects of human activities on wetland ecosystems to make our description of methodology more tangible. We begin by presenting the fundamental principles of SEM, including both its distinguishing characteristics and the requirements for modeling hypotheses about causal networks. We then illustrate SEM procedures and offer guidelines for conducting SEM analyses. Our focus in this presentation is on basic modeling objectives and core techniques. Pointers to additional modeling options are also given.

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

PubMed

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

2012-06-01

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

Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities

PubMed Central

Cox, Tony; Popken, Douglas; Ricci, Paolo F

2013-01-01

Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures. PMID:23983662

Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex.

PubMed

Schomers, Malte R; Garagnani, Max; Pulvermüller, Friedemann

2017-03-15

The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory-a specifically human trait providing the foundation for language abilities-but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order "jumping links" between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage. SIGNIFICANCE STATEMENT Why do humans have superior language abilities compared to primates? Recently, a uniquely human neuroanatomical feature has been demonstrated in the strength of the arcuate fasciculus (AF), a fiber pathway interlinking the left-hemispheric language areas. Although AF anatomy has been related to linguistic skills, an explanation of how this fiber bundle may support language abilities is still missing. We use neuroanatomically structured computational models to investigate the consequences of evolutionary changes in language area connectivity and demonstrate that the human-specific higher connectivity degree and comparatively shorter sensorimotor path length implicated by the AF entail emergence of verbal working memory, a prerequisite for language learning. These results offer a better understanding of specifically human anatomical features for language and their evolutionary selection advantage. Copyright © 2017 Schomers et al.

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

PubMed Central

2017-01-01

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr. PMID:28821014

Analogical and category-based inference: a theoretical integration with Bayesian causal models.

PubMed

Holyoak, Keith J; Lee, Hee Seung; Lu, Hongjing

2010-11-01

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.

The center for causal discovery of biomedical knowledge from big data.

PubMed

Cooper, Gregory F; Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-11-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

PubMed Central

Williamson, Rebecca A.; Meltzoff, Andrew N.

2018-01-01

Causal reasoning is an important aspect of scientific thinking. Even young human children can use causal reasoning to explain observations, make predictions, and design actions to bring about specific outcomes in the physical world. Weight is an interesting type of cause because it is an invisible property. Here, we tested preschool children with causal problem-solving tasks that assessed their understanding of weight. In an experimental setting, 2- to 5-year-old children completed three different tasks in which they had to use weight to produce physical effects—an object displacement task, a balance-scale task, and a tower-building task. The results showed that the children’s understanding of how to use object weight to produce specific object-to-object causal outcomes improved as a function of age, with 4- and 5-year-olds showing above-chance performance on all three tasks. The younger children’s performance was more variable. The pattern of results provides theoretical insights into which aspects of weight processing are particularly difficult for preschool children and why they find it difficult. PMID:29561840

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

PubMed

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

2018-01-01

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

Encopresis in Children on the Autistic Spectrum.

ERIC Educational Resources Information Center

Radford, Jo; Anderson, Maggie

2003-01-01

Outlines problems faced by parents and caregivers of a child with encopresis. Differentiates between knowledge, skill, and volition issues when dealing with the encoprectic child; and suggests possible causal frameworks for the behavior. Concludes with suggestions for managing the teaching/learning situation arising from this behavior. (Author/KB)

The Role of Memory for Compounds in Cue Competition

ERIC Educational Resources Information Center

Vandorpe, Stefaan; de Houwer, Jan; Beckers, Tom

2007-01-01

Revisions of common associative learning models incorporate a within-compound association mechanism in order to explain retrospective cue competition effects (e.g., [Dickinson, A., & Burke, J. (1996). Within-compound associations mediate the retrospective revaluation of causality judgements. "Quarterly Journal of Experimental Psychology, 49B", pp.…

Cognitions of Ordinary Events: Their Relationship to Depression.

ERIC Educational Resources Information Center

Hindin, David A.; And Others

Beck's cognitive theory and the reformulated learned helplessness model suggest that there is a negative idiosyncratic cognitive style that characterizes the way depressed individuals appraise events which is causally associated with depression. Although a few researchers have recognized the importance of examining appraisals of everyday events,…

The perceptual shaping of anticipatory actions.

PubMed

Maffei, Giovanni; Herreros, Ivan; Sanchez-Fibla, Marti; Friston, Karl J; Verschure, Paul F M J

2017-12-20

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behaviour relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts. © 2017 The Author(s).

Brain systems underlying susceptibility to helplessness and depression.

PubMed

Shumake, J; Gonzalez-Lima, F

2003-09-01

There has been a relative lack of research into the neurobiological predispositions that confer vulnerability to depression. This article reviews functional brain mappings from a genetic animal model, the congenitally helpless rat, which is predisposed to develop learned helplessness. Neurometabolic findings from this model are integrated with the neuroscientific literature from other animal models of depression as well as depressed humans. Changes in four major brain systems are suggested to underlie susceptibility to helplessness and possibly depression: (a) an unbalanced prefrontal-cingulate cortical system, (b) a dissociated hypothalamic-pituitary-adrenal axis, (c) a dissociated septal-hippocampal system, and (d) a hypoactive brain reward system, as exemplified by a hypermetabolic habenula-interpeduncular nucleus pathway and a hypometabolic ventral tegmental area-striatum pathway. Functional interconnections and causal relationships among these systems are considered and further experiments are suggested, with theoretical attention to how an abnormality in any one system could affect the others.

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

PubMed

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

2015-01-01

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

The Economics and Psychology of Inequality and Human Development. NBER Working Paper No. 14695

ERIC Educational Resources Information Center

Cunha, Flavio; Heckman, James J.

2009-01-01

Recent research on the economics of human development deepens understanding of the origins of inequality and excellence. It draws on and contributes to personality psychology and the psychology of human development. Inequalities in family environments and investments in children are substantial. They causally affect the development of…

George Glasson and George Bogg's prospects on the environmental friendly relationship and ecojustice

NASA Astrophysics Data System (ADS)

Dopico, Eduardo

2011-06-01

This rejoinder to George Glasson and George Bogg's papers provides additional conversation for considering the idea that we try to develop: leaving the classroom to continue teaching. Converting the teaching-learning process into research experiences brings our students not only scientific knowledge, but also an understanding of the research procedures. To be involved in field work, students can connect more personally the local action with global issues. On the ground in which we operate, environmental science, teaching of knowledge is insufficient if not accompanied by ecological experiences where students can see and share the needs of environmental protection and the idea of sustainability. Both response authors tell us about their own experiences in research in this regard. In their essays we can appreciate the desire to investigate human activities on ecosystems. Reading it makes us look with passion and awareness at the different consequences for our ecological environments: if we develop environmentally consequential behavior or harmful lifestyles for the planet. Furthermore, they warn us of the need to follow the development of students learning and reflect on the ways in which it produces time-causal relationship between persons and the environment.

Neurogenesis and Developmental Anesthetic Neurotoxicity

PubMed Central

Kang, Eunchai; Berg, Daniel A.; Furmanski, Orion; Jackson, William M.; Ryu, Yun Kyoung; Gray, Christy D.; Mintz, C. David

2017-01-01

The mechanism by which anesthetics might act on the developing brain in order to cause long term deficits remains incompletely understood. The hippocampus has been identified as a structure that is likely to be involved, as rodent models show numerous deficits in behavioral tasks of learning that are hippocampal-dependent. The hippocampus is an unusual structure in that it is the site of large amounts of neurogenesis postnatally, particularly in the first year of life in humans, and these newly generated neurons are critical to the function of this structure. Intriguingly, neurogenesis is a major developmental event that occurs during postulated windows of vulnerability to developmental anesthetic neurotoxicity across the different species in which it has been studied. In this review, we examine the evidence for anesthetic effects on neurogenesis in the early postnatal period and ask whether neurogenesis should be studied further as a putative mechanism of injury. Multiple anesthetics are considered, and both in vivo and in vitro work is presented. While there is abundant evidence that anesthetics act to suppress neurogenesis at several different phases, evidence of a causal link between these effects and any change in learning behavior remains elusive. PMID:27751818

Neurogenesis and developmental anesthetic neurotoxicity.

PubMed

Kang, Eunchai; Berg, Daniel A; Furmanski, Orion; Jackson, William M; Ryu, Yun Kyoung; Gray, Christy D; Mintz, C David

The mechanism by which anesthetics might act on the developing brain in order to cause long term deficits remains incompletely understood. The hippocampus has been identified as a structure that is likely to be involved, as rodent models show numerous deficits in behavioral tasks of learning that are hippocampal-dependent. The hippocampus is an unusual structure in that it is the site of large amounts of neurogenesis postnatally, particularly in the first year of life in humans, and these newly generated neurons are critical to the function of this structure. Intriguingly, neurogenesis is a major developmental event that occurs during postulated windows of vulnerability to developmental anesthetic neurotoxicity across the different species in which it has been studied. In this review, we examine the evidence for anesthetic effects on neurogenesis in the early postnatal period and ask whether neurogenesis should be studied further as a putative mechanism of injury. Multiple anesthetics are considered, and both in vivo and in vitro work is presented. While there is abundant evidence that anesthetics act to suppress neurogenesis at several different phases, evidence of a causal link between these effects and any change in learning behavior remains elusive. Copyright © 2016. Published by Elsevier Inc.

Altered mGluR5-Homer scaffolds and corticostriatal connectivity in a Shank3 complete knockout model of autism.

PubMed

Wang, Xiaoming; Bey, Alexandra L; Katz, Brittany M; Badea, Alexandra; Kim, Namsoo; David, Lisa K; Duffney, Lara J; Kumar, Sunil; Mague, Stephen D; Hulbert, Samuel W; Dutta, Nisha; Hayrapetyan, Volodya; Yu, Chunxiu; Gaidis, Erin; Zhao, Shengli; Ding, Jin-Dong; Xu, Qiong; Chung, Leeyup; Rodriguiz, Ramona M; Wang, Fan; Weinberg, Richard J; Wetsel, William C; Dzirasa, Kafui; Yin, Henry; Jiang, Yong-Hui

2016-05-10

Human neuroimaging studies suggest that aberrant neural connectivity underlies behavioural deficits in autism spectrum disorders (ASDs), but the molecular and neural circuit mechanisms underlying ASDs remain elusive. Here, we describe a complete knockout mouse model of the autism-associated Shank3 gene, with a deletion of exons 4-22 (Δe4-22). Both mGluR5-Homer scaffolds and mGluR5-mediated signalling are selectively altered in striatal neurons. These changes are associated with perturbed function at striatal synapses, abnormal brain morphology, aberrant structural connectivity and ASD-like behaviour. In vivo recording reveals that the cortico-striatal-thalamic circuit is tonically hyperactive in mutants, but becomes hypoactive during social behaviour. Manipulation of mGluR5 activity attenuates excessive grooming and instrumental learning differentially, and rescues impaired striatal synaptic plasticity in Δe4-22(-/-) mice. These findings show that deficiency of Shank3 can impair mGluR5-Homer scaffolding, resulting in cortico-striatal circuit abnormalities that underlie deficits in learning and ASD-like behaviours. These data suggest causal links between genetic, molecular, and circuit mechanisms underlying the pathophysiology of ASDs.

Altered mGluR5-Homer scaffolds and corticostriatal connectivity in a Shank3 complete knockout model of autism

PubMed Central

Wang, Xiaoming; Bey, Alexandra L.; Katz, Brittany M.; Badea, Alexandra; Kim, Namsoo; David, Lisa K.; Duffney, Lara J.; Kumar, Sunil; Mague, Stephen D.; Hulbert, Samuel W.; Dutta, Nisha; Hayrapetyan, Volodya; Yu, Chunxiu; Gaidis, Erin; Zhao, Shengli; Ding, Jin-Dong; Xu, Qiong; Chung, Leeyup; Rodriguiz, Ramona M.; Wang, Fan; Weinberg, Richard J.; Wetsel, William C.; Dzirasa, Kafui; Yin, Henry; Jiang, Yong-hui

2016-01-01

Human neuroimaging studies suggest that aberrant neural connectivity underlies behavioural deficits in autism spectrum disorders (ASDs), but the molecular and neural circuit mechanisms underlying ASDs remain elusive. Here, we describe a complete knockout mouse model of the autism-associated Shank3 gene, with a deletion of exons 4–22 (Δe4–22). Both mGluR5-Homer scaffolds and mGluR5-mediated signalling are selectively altered in striatal neurons. These changes are associated with perturbed function at striatal synapses, abnormal brain morphology, aberrant structural connectivity and ASD-like behaviour. In vivo recording reveals that the cortico-striatal-thalamic circuit is tonically hyperactive in mutants, but becomes hypoactive during social behaviour. Manipulation of mGluR5 activity attenuates excessive grooming and instrumental learning differentially, and rescues impaired striatal synaptic plasticity in Δe4–22−/− mice. These findings show that deficiency of Shank3 can impair mGluR5-Homer scaffolding, resulting in cortico-striatal circuit abnormalities that underlie deficits in learning and ASD-like behaviours. These data suggest causal links between genetic, molecular, and circuit mechanisms underlying the pathophysiology of ASDs. PMID:27161151

Translational studies of goal-directed action as a framework for classifying deficits across psychiatric disorders

PubMed Central

Griffiths, Kristi R.; Morris, Richard W.; Balleine, Bernard W.

2014-01-01

The ability to learn contingencies between actions and outcomes in a dynamic environment is critical for flexible, adaptive behavior. Goal-directed actions adapt to changes in action-outcome contingencies as well as to changes in the reward-value of the outcome. When networks involved in reward processing and contingency learning are maladaptive, this fundamental ability can be lost, with detrimental consequences for decision-making. Impaired decision-making is a core feature in a number of psychiatric disorders, ranging from depression to schizophrenia. The argument can be developed, therefore, that seemingly disparate symptoms across psychiatric disorders can be explained by dysfunction within common decision-making circuitry. From this perspective, gaining a better understanding of the neural processes involved in goal-directed action, will allow a comparison of deficits observed across traditional diagnostic boundaries within a unified theoretical framework. This review describes the key processes and neural circuits involved in goal-directed decision-making using evidence from animal studies and human neuroimaging. Select studies are discussed to outline what we currently know about causal judgments regarding actions and their consequences, action-related reward evaluation, and, most importantly, how these processes are integrated in goal-directed learning and performance. Finally, we look at how adaptive decision-making is impaired across a range of psychiatric disorders and how deepening our understanding of this circuitry may offer insights into phenotypes and more targeted interventions. PMID:24904322

Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

PubMed Central

Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W.; Stufflebeam, Steven M.; Hamalainen, Matti S.

2010-01-01

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using Structural Equation Modeling and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. PMID:19378280

When Does Model-Based Control Pay Off?

PubMed Central

2016-01-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand. PMID:27564094

When Does Model-Based Control Pay Off?

PubMed

Kool, Wouter; Cushman, Fiery A; Gershman, Samuel J

2016-08-01

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Instructional Design and Online Learning: A Quality Assurance Study

ERIC Educational Resources Information Center

Monroe, Rose M.

2011-01-01

The purpose of this study was to investigate the difference in the evaluations of online course quality using the Quality Matters model among four groups of reviewers: instructional designers, faculty with subject-matter expertise, peer faculty with no subject-matter expertise, and administrators. A causal-comparative design was utilized to…

Cross-Lagged Relationships between Career Aspirations and Goal Orientation in Early Adolescents

ERIC Educational Resources Information Center

Creed, Peter; Tilbury, Clare; Buys, Nick; Crawford, Meegan

2011-01-01

We surveyed 217 students (145 girls; average age = 14.6 years) on two occasions, twelve months apart, on measures of career aspirations (job aspirations, job expectations, educational aspirations) and goal orientation (learning, performance-prove, performance-avoid), and tested the causal relationship between goal orientation and aspirations. We…

A Psychological Theory of Educational Productivity.

ERIC Educational Resources Information Center

Walberg, Herbert J.

To solve problems of causality and measurement in educational research, this paper combines seven variables into a proposed model of educational productivity on achievement tests. The authors first review psychological models of educational production that relate learning to aptitude and environment, and note that these models do not allow for…

Sharka epidemiology and worldwide management strategies: learning lessons to optimize disease control in perennial plants

USDA-ARS?s Scientific Manuscript database

Many plant epidemics that cause major economic losses cannot be controlled with pesticides. Among them, sharka epidemics severely affect prunus trees worldwide. Its causal agent, Plum pox virus (PPV;, genus Potyvirus), has been classified as a quarantine pathogen in numerous countries. As a result, ...

Cognitive Processes in University Learning: A Developmental Framework Using Structural Equation Modelling

ERIC Educational Resources Information Center

Phan, Huy P.

2011-01-01

Background: Both achievement goals and study processing strategies theories have been shown to contribute to the prediction of students' academic performance. Existing research studies (Fenollar, Roman, & Cuestas, 2007; Liem, Lau, & Nie, 2008; Simons, Dewitte, & Lens, 2004) amalgamating these two theoretical orientations in different causal models…

Math Anxiety and Math Ability in Early Primary School Years

ERIC Educational Resources Information Center

Krinzinger, Helga; Kaufmann, Liane; Willmes, Klaus

2009-01-01

Mathematical learning disabilities (MLDs) are often associated with math anxiety, yet until now, very little is known about the causal relations between calculation ability and math anxiety during early primary school years. The main aim of this study was to longitudinally investigate the relationship between calculation ability, self-reported…

Misconceived Causal Explanations for Emergent Processes

ERIC Educational Resources Information Center

Chi, Michelene T. H.; Roscoe, Rod D.; Slotta, James D.; Roy, Marguerite; Chase, Catherine C.

2012-01-01

Studies exploring how students learn and understand science processes such as "diffusion" and "natural selection" typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes' necks get longer over generations, or how ink dropped into water appears to "flow"). Instead of…

Promoting Critical Thinking in Online Intercultural Communication

ERIC Educational Resources Information Center

Batardière, Marie-Thérèse

2015-01-01

This paper investigates the educational experience arising from the use of an online discussion forum in an undergraduate blended learning language programme; to do this, it focuses on the type of cognitive processes that learners experience during a computer-mediated collaborative task and explores the potential causal relationship between the…