The Probabilistic Mind

Prospects for Bayesian Cognitive Science

Edited by Nick Chater and Mike Oaksford

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 Probabilistic Mind

Prospects for Bayesian Cognitive Science

Edited by Nick Chater and Mike Oaksford

Description

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.

The Probabilistic Mind

Prospects for Bayesian Cognitive Science

Edited by Nick Chater and Mike Oaksford

Part I - Foundations
1. The probabilistic mind: prospects for a Bayesian cognitive science, Nick Chater & Mike Oaksford
2. Technical introduction: a primer on probabilistic inference, Thomas L Griffiths & Alan Yuille
3. Rational analyses, instrumentalism, and implementations, David Danks
Part II - Inference and Argument
4. Framing effects and rationality, Shlomi Sher & Craig R M McKenzie
5. Probability logic and the 'Modus Ponens - Modus Tollens' asymmetry, Mike Oaksford & Nick Chater
6. Inference from absence in language and thought, Ulrike Hahn & Mike Oaksford
7. Towards a rational theory of human information acquisition, Jonathan Nelson
8. Pseudocontingencies: a key paradigm for understanding adaptive cognition, Klaus Fiedler
Part III - Judgement and Decision-making
9. Probabilistic minds, Bayesian brains, and cognitive mechanisms: harmony or dissonance, Henry Brighton & Gerd Gigerenzer
10. The game of life: how small samples render choice simple, Ralph Hertwig & Timothy J Pleskac
11. The naive intuitive statistician: organism-environment relations from yet another angle, Patrik Hansson, Peter Juslin & Anders Winman
12. A decision-by-sampling account of decision under risk, Neil Stewart & Keith Simpson
13. The neurodynamics of choice, value-based decisions and preference reversal, Marius Usher, Anat Elhalal & James L McClelland
Part IV - Categorization and Memory
14. Categorization as nonparametric Bayesian density estimation, Thomas L Griffiths, Adam N Sanborn, Kevin R Canini & Daniel J Navarro
15. Rational analysis as a link between human memory and information retrieval, Mark Steyvers & Thomas L Griffiths
16. Causality in time: explaining away the future and the past, David E Huber
17. Compositionality in rational analysis: grammar-based induction for concept learning, Noah D Goodman, Joshua B Tenenbaum, Thomas L Griffiths & Jacob Feldman
Part V - Learning about Contingency and Causality
18. Through the looking-glass: a dynamic lens model approach to learning in MCPL tasks, Maaarten Speekenbrink & David R Shanks
19. Semi-rational models of conditioning: the case of trial order, Nathaniel D Daw, Aaron C Courville & Peter Dayan
20. Causal learning in rats and humans: a minimal rational model, Michael R Waldmann, Patricia W Cheng, York Hagmeyer & Aaron P Blaisdell
21. The value of rational analysis: an assessment of causal reasoning and learning, Steven Sloman & Philip M Fernbach
22. Conclusion: where next?, Nick Chater & Mike Oaksford

The Probabilistic Mind

Prospects for Bayesian Cognitive Science

Edited by Nick Chater and Mike Oaksford

Author Information

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 principles that may be applicable across many cognitive domains, ranging from reasoning and decision making, to language acquisition and processing, to perception and categorization. Since the late 1980s, in collaboration with Mike Oaksford, he has been interested in the application of probabilistic and information-theoretic methods for understanding human reasoning. Mike Oaksford is Professor of Psychology and Head of School at Birkbeck College London. He was a research fellow at the Centre for Cognitive Science, University of Edinburgh, he was then lecturer at the University of Wales, Bangor, and senior lecturer at the University of Warwick, before moving to Cardiff University in 1996 as Professor of Experimental Psychology, a post he held until 2005. His research interests are in the area of human reasoning and decision making. In particular, with his colleague Nick Chater, he has been developing a Bayesian probabilistic approach to deductive reasoning tasks. According to this approach reasoning "biases" are the result of applying the wrong normative model and failing to take account of people's normal environment. He also studies the way the emotions affect and interact with reasoning and decision making processes.

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