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Bayesian learning theory applied to human cognition
Author(s) -
Jacobs Robert A.,
Kruschke John K.
Publication year - 2010
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.80
Subject(s) - artificial intelligence , bayesian inference , bayesian probability , bayesian network , machine learning , computer science , cognition , inference , bayesian statistics , bayes' theorem , normative , dynamic bayesian network , cognitive science , psychology , philosophy , epistemology , neuroscience
Probabilistic models based on Bayes' rule are an increasingly popular approach to understanding human cognition. Bayesian models allow immense representational latitude and complexity. Because they use normative Bayesian mathematics to process those representations, they define optimal performance on a given task. This article focuses on key mechanisms of Bayesian information processing, and provides numerous examples illustrating Bayesian approaches to the study of human cognition. We start by providing an overview of Bayesian modeling and Bayesian networks. We then describe three types of information processing operations—inference, parameter learning, and structure learning—in both Bayesian networks and human cognition. This is followed by a discussion of the important roles of prior knowledge and of active learning. We conclude by outlining some challenges for Bayesian models of human cognition that will need to be addressed by future research. WIREs Cogn Sci 2011 2 8–21 DOI: 10.1002/wcs.80 This article is categorized under: Computer Science > Artificial Intelligence Psychology > Learning