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Applying action theory to psychiatry
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The Probabilistic Mind
Prospects for Bayesian cognitive science
Edited by Nick Chater and Mike Oaksford
536 pages
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78 line figures
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234x156mm
978-0-19-921609-3
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Paperback
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27 March 2008
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- A timely appraisal of the rational analysis approach to understanding cognition, and a successor to an influential volume in the field (Rational Models of Cognition)
- Presents cutting edge research from leading figures in cognitive science, applying Bayesian methods to a broad range of topics, including reasoning, decision making, categorization, and memory - resulting in a book with wide appeal within cognitive psychology
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 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 variety of theoretical and experimental lines of research. The scope of the book is broad, covering important recent 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. Readership: Cognitive scientists interested in thinking, reasoning, and learning.
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Edited by Nick Chater, Professor of Cognitive and Decision Sciences, Department of Psychology, University College London, UK, and Mike Oaksford, Professor of Psychology and Head of School of Psychology, Birkbeck College London, UK Contributors:
Aaron P Blaisdell, Dept of Psychology, University of California, Los Angeles, USA
Henry Brighton, Centre for Adaptive Behaviour and Cognition, Max Planck Institute, Berlin, Germany
Kevin R Canini, Dept of Computer Science, University of California, Berkeley, USA
Nick Chater, Dept of Psychology, University College London, UK
Patricia W
Cheng, Dept of Psychology, University of California, Los Angeles, USA
Aaron C Courville,
David Danks, Dept of Philosophy, Carnegie Mellon University, Pitttsburgh, USA
Nathaniel D Daw, Center for Neural Science, New York University, New York, USA
Peter Dayan, Gatsby Computational Neuroscience Unit, University College London, UK
Anat Elhalal, School of Psychology, Birkbeck College London, UK
Jacob Feldman, Dept of Psychology, Center for Cognitive Science, Rutgers University, New Brunswick, USA
Philip M Fernbach, Cognitive and Linguistic Sciences, Brown University, Providence, USA
Klaus Fiedler, Psychology Institute, Heidelberg University, Heidelberg, Germany
Gerd Gigerenzer, Centre
for Adaptive Behaviour and Cognition, Max Planck Institute, Berlin, Germany
Noah D Goodman, Massachusetts Institute of Technology, Cambridge MA, USA
Thomas L Griffiths, Dept of Psychology, University of California, Berkeley, USA
York Hagmeyer, Dept of Psychology, University of Goettingen, Goettingen, Germany
Ulrike Hahn, School of Psychology, Cardiff University, Cardiff, UK
Patrik Hansson, Dept of Psychology, Umea University, Umea, Sweden
Ralph Hertwig, Dept of Psychology, Basel University, Basel, Switzerland
David E Huber, Dept of Psychology, University of California San Diego, La Jolla, USA
Peter Juslin, Dept of Psychology, Uppsala University, Uppsala, Sweden
James L McClelland,
Dept of Psychology, University of California, Palo Alto, USA
Craig R M McKenzie, Dept of Psychology, University of California San Diego, La Jolla, USA
Daniel J Navarro, School of Psychology, University of Adelaide, Adelaide SA, Australis
Jonathan Nelson, Salk Institute, San Diego, USA
Mike Oaksford, School of Psychology, Birkbeck College London, UK
Timothy J Pleskac,
Adam N Sanborn, Dept of Psychological and Brain Sciences, Indiana University, Bloomington, USA
David R Shanks, Dept of Psychology, University College London, UK
Shlomi Sher, Dept of Psychology, Princeton University, Princeton, USA
Keith Simpson, Dept of Psychology, University of Warwick, Coventry, UK
Steven
Sloman, Cognitive and Linguistic Sciences, Brown University, Providence, USA
Maarten Speekenbrink, Dept of Psychology, University College London, UK
Neil Stewart, Dept of Psychology, University of Warwick, Coventry, UK
Mark Steyvers, Dept of Cognitive Sciences, University of California, Irvine, USA
Joshua B Tenenbaum, Massachusetts Institute of Technology, Cambridge MA, USA
Marius Usher, School of Psychology, Birkbeck College London, UK
Michael R Waldmann, Dept of Psychology, University of Goettingen, Goettingen, Germany
Anders Winman, Dept of Psychology, Uppsala University, Uppsala, Sweden
Alan Yuille, Dept of Statistics, University of California, Los Angeles, USA
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Part I - Foundations
1: Nick Chater & Mike Oaksford: The probabilistic mind: prospects for a Bayesian cognitive science
2: Thomas L Griffiths & Alan Yuille: Technical introduction: a primer on probabilistic inference
3: David Danks: Rational analyses, instrumentalism, and implementations
Part II - Inference and Argument
4: Shlomi Sher & Craig R M McKenzie: Framing effects and rationality
5: Mike Oaksford & Nick Chater: Probability logic and the 'Modus Ponens - Modus Tollens' asymmetry
6: Ulrike Hahn & Mike Oaksford: Inference from absence in language and thought
7: Jonathan Nelson: Towards a rational theory of human information acquisition
8: Klaus Fiedler: Pseudocontingencies: a key paradigm for understanding adaptive cognition
Part III - Judgement and Decision-making
9: Henry Brighton & Gerd Gigerenzer: Probabilistic minds, Bayesian brains, and cognitive mechanisms: harmony or dissonance
10: Ralph Hertwig & Timothy J Pleskac: The game of life: how small samples render choice simple
11: Patrik Hansson, Peter Juslin & Anders Winman: The naive intuitive statistician: organism-environment relations from yet another angle
12: Neil Stewart & Keith Simpson: A decision-by-sampling account of decision under risk
13: Marius Usher, Anat Elhalal & James L McClelland: The neurodynamics of choice, value-based decisions and preference reversal
Part IV - Categorization and Memory
14: Thomas L Griffiths, Adam N Sanborn, Kevin R Canini & Daniel J Navarro: Categorization as nonparametric Bayesian density estimation
15: Mark Steyvers & Thomas L Griffiths: Rational analysis as a link between human memory and information retrieval
16: David E Huber: Causality in time: explaining away the future and the past
17: Noah D Goodman, Joshua B Tenenbaum, Thomas L Griffiths & Jacob Feldman: Compositionality in rational analysis: grammar-based induction for concept learning
Part V - Learning about Contingency and Causality
18: Maarten Speekenbrink & David R Shanks: Through the looking-glass: a dynamic lens model approach to learning in MCPL tasks
19: Nathaniel D Daw, Aaron C Courville & Peter Dayan: Semi-rational models of conditioning: the case of trial order
20: Michael R Waldmann, Patricia W Cheng, York Hagmeyer & Aaron P Blaisdell: Causal learning in rats and humans: a minimal rational model
21: Steven Sloman & Philip M Fernbach: The value of rational analysis: an assessment of causal reasoning and learning
22: Nick Chater & Mike Oaksford: Conclusion: where next?
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The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
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