Show Reference: "Bayesian learning theory applied to human cognition"

Bayesian learning theory applied to human cognition WIREs Cogn Sci, Vol. 2, No. 1. (2011), pp. 8-21, doi:10.1002/wcs.80 by Robert A. Jacobs, John K. Kruschke
    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 For further resources related to this article, please visit the {WIREs} website},
    author = {Jacobs, Robert A. and Kruschke, John K.},
    day = {16},
    doi = {10.1002/wcs.80},
    issn = {19395078},
    journal = {WIREs Cogn Sci},
    keywords = {bayes, biology},
    number = {1},
    pages = {8--21},
    posted-at = {2011-10-19 11:31:38},
    priority = {2},
    publisher = {John Wiley \& Sons, Inc.},
    title = {Bayesian learning theory applied to human cognition},
    url = {},
    volume = {2},
    year = {2011}

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When studying an information-processing system, and given a computational theory of it, algorithms and representations for implementing it can be designed, and their performance can be compared to that of natural processing.

If the performance is similar, that supports our computational theory.