The Probabilistic Mind: Prospects for Bayesian cognitive science

Paperback | April 4, 2008

EditorNick Chater, Mike Oaksford

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The rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes.'The Probabilistic Mind' is a follow-up to the influential and highly cited '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 Bayesianmethods. 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 variety of theoretical and experimental lines of research. The scope of the book is broad, covering importantrecent work in reasoning, decision making, categorization, and memory. Including chapters from many of the leading figures in this field, 'The Probabilistic Mind' will be valuable for psychologists and philosophers interested in cognition.

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The rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes.'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition' (OUP, 1998). It brings together developments in understanding how, and how f...

Nick Chater is Professor of Cognitive and Decision Sciences at University College London. He has an M.A. in Psychology from Cambridge University, and a PhD in Cognitive Science from Edinburgh. He has held academic appointments at Edinburgh, Oxford, and Warwick Universities. His research focussed on attempting to find general principle...

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Format:PaperbackDimensions:384 pages, 9.21 × 6.14 × 1.1 inPublished:April 4, 2008Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199216096

ISBN - 13:9780199216093

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Table of Contents

Part I - Foundations1. Nick Chater and Mike Oaksford: The probabilistic mind: prospects for a Bayesian cognitive science2. Thomas L Griffiths and Alan Yuille: Technical introduction: a primer on probabilistic inference3. David Danks: Rational analyses, instrumentalism, and implementationsPart II - Inference and Argument4. Shlomi Sher and Craig R M McKenzie: Framing effects and rationality5. Mike Oaksford and Nick Chater: Probability logic and the 'Modus Ponens - Modus Tollens' asymmetry6. Ulrike Hahn and Mike Oaksford: Inference from absence in language and thought7. Jonathan Nelson: Towards a rational theory of human information acquisition8. Klaus Fiedler: Pseudocontingencies: a key paradigm for understanding adaptive cognitionPart III - Judgement and Decision-making9. Henry Brighton and Gerd Gigerenzer: Probabilistic minds, Bayesian brains, and cognitive mechanisms: harmony or dissonance10. Ralph Hertwig and Timothy J Pleskac: The game of life: how small samples render choice simple11. Patrik Hansson, Peter Juslin and Anders Winman: The naive intuitive statistician: organism-environment relations from yet another angle12. Neil Stewart and Keith Simpson: A decision-by-sampling account of decision under risk13. Marius Usher, Anat Elhalal and James L McClelland: The neurodynamics of choice, value-based decisions and preference reversalPart IV - Categorization and Memory14. Thomas L Griffiths, Adam N Sanborn, Kevin R Canini and Daniel J Navarro: Categorization as nonparametric Bayesian density estimation15. Mark Steyvers and Thomas L Griffiths: Rational analysis as a link between human memory and information retrieval16. David E Huber: Causality in time: explaining away the future and the past17. Noah D Goodman, Joshua B Tenenbaum, Thomas L Griffiths and Jacob Feldman: Compositionality in rational analysis: grammar-based induction for concept learningPart V - Learning about Contingency and Causality18. Maaarten Speekenbrink and David R Shanks: Through the looking-glass: a dynamic lens model approach to learning in MCPL tasks19. Nathaniel D Daw, Aaron C Courville and Peter Dayan: Semi-rational models of conditioning: the case of trial order20. Michael R Waldmann, Patricia W Cheng, York Hagmeyer and Aaron P Blaisdell: Causal learning in rats and humans: a minimal rational model21. Steven Sloman and Philip M Fernbach: The value of rational analysis: an assessment of causal reasoning and learning22. Nick Chater and Mike Oaksford: Conclusion: where next?