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What the Bayesian framework has contributed to understanding cognition: Causal learning as a case study

Published online by Cambridge University Press:  25 August 2011

Keith J. Holyoak  and
Hongjing Lu
Keith J. Holyoak
Affiliation:
Department of Psychology, University of California, Los Angeles, CA 90095-1563. holyoak@lifesci.ucla.edu http://cvl.psych.ucla.edu/
Hongjing Lu
Affiliation:
Department of Psychology, University of California, Los Angeles, CA 90095-1563. holyoak@lifesci.ucla.edu http://cvl.psych.ucla.edu/ Department of Statistics, University of California, Los Angeles, CA 90095-1563. hongjing@ucla.edu http://www.reasoninglaboratory.dreamhosters.com
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Abstract

The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 34 , Issue 4 , August 2011 , pp. 203 - 204
Copyright
Copyright © Cambridge University Press 2011

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