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Theorybased causal induction
 In
, 2003
"... Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various s ..."
Abstract

Cited by 33 (14 self)
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Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the cooccurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computationallevel analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domaingeneral statistical inference guided by domainspecific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.
Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting
 Psychological Review
, 2006
"... A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probab ..."
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Cited by 26 (7 self)
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A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trialbytrial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The backpropagation of target values to attention allows the model to show trialorder effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.
The psychophysics of contingency assessment
 Journal of Experimental Psychology: General
, 2008
"... The authors previously described a procedure that permits rapid, multiple withinparticipant evaluations ..."
Abstract

Cited by 5 (2 self)
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The authors previously described a procedure that permits rapid, multiple withinparticipant evaluations
Learning the form of causal relationships using hierarchical Bayesian models
 Cognitive Science
, 2010
"... People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well doc ..."
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Cited by 4 (1 self)
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People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and crossdomain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.
Cue interaction effects in contingency judgments using the streamedtrials procedure
 Canadian Journal of Experimental Psychology
, 2009
"... The authors previously described a procedure that permits rapid, multiple withinparticipant assessments of the contingency between a cue and an outcome (the “streamedtrial ” procedure, Crump, Hannah, Allan, & Hord, 2007). In the present experiments, the authors modified this procedure to investig ..."
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Cited by 1 (1 self)
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The authors previously described a procedure that permits rapid, multiple withinparticipant assessments of the contingency between a cue and an outcome (the “streamedtrial ” procedure, Crump, Hannah, Allan, & Hord, 2007). In the present experiments, the authors modified this procedure to investigate cueinteraction effects, replicating conventional findings in both the one and twophase blocking paradigms. The authors show that the streamedtrial procedure is not restricted to the geometric forms used as cues and outcomes by Crump et al., and that it can incorporate the conventional allergy stimuli, where food is the cue and an allergic reaction is the outcome. The authors discuss the value of the streamedtrial procedure as a method for advancing our theoretical understanding of cueinteraction effects.
1 Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting
"... Bayesian modeling generally 2 ..."