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A Bayesian approach to the evolution of perceptual and cognitive systems
Author(s) -
Geisler Wilson S.,
Diehl Randy L.
Publication year - 2003
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2703_3
Subject(s) - bayesian probability , natural selection , computer science , perception , artificial intelligence , selection (genetic algorithm) , cognition , camouflage , natural (archaeology) , bayesian inference , machine learning , psychology , geography , archaeology , neuroscience
We describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin's theory of natural selection. Simulations of Bayesian natural selection were found to yield new insights, for example, into the co‐evolution of camouflage, color vision, and decision criteria. The Bayesian framework captures and generalizes, in a formal way, many of the important ideas of other approaches to perception and cognition.