Date of Original Version

4-2008

Type

Book Chapter

Rights Management

All Rights Reserved

Abstract or Description

For over 200 years, philosophers and mathematicians have be en using probability theory to describe human cognition. While the theory of prob abilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty (Gigerenzer et al., 1989; Hacking, 1975). Our goal in this ch apter is to illustrate the kinds of computational models of cognition that we can build if we assume that human learning and inference approximately follow the principles of Bayesian probabilistic inference, and to explain some of the mathematical ideas and techniques underlying those models

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Published In

The Cambridge Handbook of Computational Psychology, Ron Sun (ed.), Cambridge University Press.