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The Probabilistic Mind:: Prospects for Bayesian cognitive science

Nick Chater and Mike Oaksford

Abstract

The rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes. This book is a follow-up to ‘Rational Models of Cognition’ (OUP, 1998). It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods. It synthesizes and evaluates the progress in the past decade, taking into account developments in Bayesian statistics, statistical analysis of the cognitive ‘environment’ and a variet ... More

Keywords: rational analysis, John R. Anderson, high-level cognitive processes, Bayesian statistics, statistical analysis, cognitive environment, reasoning, decision making, categorization, memory

Bibliographic Information

Print publication date: 2008 Print ISBN-13: 9780199216093
Published to Oxford Scholarship Online: March 2012 DOI:10.1093/acprof:oso/9780199216093.001.0001

Authors

Affiliations are at time of print publication.

Nick Chater, editor
Professor of Cognitive and Decision Sciences, Department of Psychology, University College London, UK

Mike Oaksford, editor
Professor of Psychology and Head of School of Psychology, Birkbeck College London, UK

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Contents

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Front Matter

Part 1 Foundations

Chapter 1 The probabilistic mind

Nick Chater, and Mike Oaksford

Chapter 2 A primer on probabilistic inference

Thomas L. Griffiths, and Alan Yuille

Part 2 Inference and Argument

Chapter 4 Framing effects and rationality

Shlomi Sher, and Craig R. M. McKenzie

Part 3 Judgement and Decision-Making

Chapter 9 Bayesian brains and cognitive mechanisms

Henry Brighton, and Gerd Gigerenzer

Chapter 10 The game of life

Ralph Hertwig, and Timothy J. Pleskac

Chapter 11 The naïve intuitive statistician

Patrik Hansson, Peter Juslin, and Anders Winman

Part 4 Categorization and Memory

Chapter 14 Categorization as nonparametric Bayesian density estimation

Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, and Daniel J. Navarro

Chapter 17 Compositionality in rational analysis

Noah D. Goodman, Joshua B. Tenenbaum, Thomas L. Griffiths, and Jacob Feldman

Part 5 Learning about Contingency and Causality

Chapter 19 Semi-rational models of conditioning

Nathaniel D. Daw, Aaron C. Courville, and Peter Dayan

Chapter 20 Causal learning in rats and humans

Michael R. Waldmann, Patricia W. Cheng, York Hagmayer, and Aaron P. Blaisdell

Chapter 21 The value of rational analysis

Steven Sloman, and Philip M. Fernbach

Chapter 22 The probabilistic mind: where next?

Nick Chater, and Mike Oaksford

End Matter