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Bayesian modeling of the mind: From norms to neurons
Author(s) -
Rescorla Michael
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.1540
Subject(s) - bayesian probability , bayesian inference , bayesian statistics , bayesian econometrics , bayesian experimental design , variable order bayesian network , bayesian linear regression , normative , artificial intelligence , inference , cognition , computer science , bayes estimator , bayesian hierarchical modeling , machine learning , psychology , cognitive science , epistemology , philosophy , neuroscience
Bayesian decision theory is a mathematical framework that models reasoning and decision‐making under uncertain conditions. The past few decades have witnessed an explosion of Bayesian modeling within cognitive science. Bayesian models are explanatorily successful for an array of psychological domains. This article gives an opinionated survey of foundational issues raised by Bayesian cognitive science, focusing primarily on Bayesian modeling of perception and motor control. Issues discussed include the normative basis of Bayesian decision theory; explanatory achievements of Bayesian cognitive science; intractability of Bayesian computation; realist versus instrumentalist interpretation of Bayesian models; and neural implementation of Bayesian inference. This article is categorized under: Philosophy > Foundations of Cognitive Science