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WIREs Cogn Sci
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Bayesian learning theory applied to human cognition

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Abstract 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

Bayesian network representing the joint probability distribution of variables A, B, C, D, E, F, and G. A node represents the value of the variable it contains. Arrows impinging on a node indicate what other variables the value of the node depends on. The conditional probability besides each node expresses mathematically what the arrows express graphically.

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Bayesian network characterizing a domain in which a large number of visible variables are dependent on a small number of unobserved latent variables.

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Bayesian model of sensory integration. (Top) A situation in which visual and haptic percepts are equally good indicators of depth. (Bottom) A situation in which the visual percept is a more reliable indicator of depth.

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Bayesian network characterizing a domain in which an observer both sees and touches the objects in an environment. At the top of the hierarchy, the values of scene variables determine the probabilities of distal haptic and visual features. The distal haptic and visual features in turn determine the probabilities of values of proximal haptic and visual input (sensory) variables.

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Bayesian network characterizing a domain with four binary variables indicating whether it is cloudy, the sprinkler was recently on, it recently rained, and the grass is wet.

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