The Probabilistic Mind: Prospects for Bayesian cognitive science
The Probabilistic Mind: Prospects for Bayesian cognitive science
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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 variety of theoretical and experimental lines of research. The scope of the book is broad, covering recent work in reasoning, decision making, categorization, and memory.
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Front Matter
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Part 1 Foundations
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Part 2 Inference and Argument
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4
Framing effects and rationality
Shlomi Sher andCraig R. M. McKenzie
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5
Probability logic and the Modus Ponens—Modus Tollens asymmetry in conditional inference
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6
Inference from absence in language and thought
Ulrike Hahn andMike Oaksford
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7
Towards a rational theory of human information acquisition
Jonathan D. Nelson
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Pseudocontingencies—A key paradigm for understanding adaptive cognition
Klaus Fiedler
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4
Framing effects and rationality
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Part 3 Judgement and Decision-Making
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9
Bayesian brains and cognitive mechanisms: Harmony or dissonance?
Henry Brighton andGerd Gigerenzer
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10
The game of life: How small samples render choice simpler
Ralph Hertwig andTimothy J. Pleskac
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11
The naïve intuitive statistician: Organism—environment relations from yet another angle
Patrik Hansson and others
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12
A decision-by-sampling account of decision under risk
Neil Stewart andKeith Simpson
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13
The neurodynamics of choice, value-based decisions, and preference reversal
Marius Usher and others
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9
Bayesian brains and cognitive mechanisms: Harmony or dissonance?
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Part 4 Categorization and Memory
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14
Categorization as nonparametric Bayesian density estimation
Thomas L. Griffiths and others
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15
Rational analysis as a link between human memory and information retrieval
Mark Steyvers andThomas L. Griffiths
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Causality in time: Explaining away the future and the past
David E. Huber
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Compositionality in rational analysis: Grammar-based induction for concept learning
Noah D. Goodman and others
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14
Categorization as nonparametric Bayesian density estimation
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Part 5 Learning about Contingency and Causality
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18
Through the looking glass: a dynamic lens model approach to multiple cue probability learning
Maarten Speekenbrink andDavid R. Shanks
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19
Semi-rational models of conditioning: The case of trial order
Nathaniel D. Daw and others
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20
Causal learning in rats and humans: A minimal rational model
Michael R. Waldmann and others
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21
The value of rational analysis: An assessment of causal reasoning and learning
Steven Sloman andPhilip M. Fernbach
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22
The probabilistic mind: where next?
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18
Through the looking glass: a dynamic lens model approach to multiple cue probability learning
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End Matter
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