## The Probabilistic Mind: Prospects for Bayesian Cognitive ScienceThe 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. |

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... mixture model 49 generalized linear model 412–14, 422 generative models 45, 331, 338, 352, 355–61, 432–3 generative power 460 German city environment 200–2

... mixture model 49 generalized linear model 412–14, 422 generative models 45, 331, 338, 352, 355–61, 432–3 generative power 460 German city environment 200–2

**Gibbs sampling**50, 315–18, 324, 338, 340, 365, 370 gist 518 INDEX E F G. Page 519

**Gibbs sampling**50, 315–18, 324, 338, 340, 365, 370 gist see topic models to extract verbatim and gist information goal framing 82n grammar-based induction see compositionality graphical models 33 directed 43–5 representation 52 ...### What people are saying - Write a review

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The Probabilistic Mind: Prospects for Bayesian Cognitive Science Nick Chater,Mike Oaksford No preview available - 2008 |

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algorithm Anderson approach approximate argument from ignorance associated assumed assumption baserates Bayes Bayesian inference behavior beliefs biases Cambridge cause Chater choice cluster cognitive science cognitive system computational concept conditional probability context correlation covariation cue validity decision diagnosticity Dirichlet process effect environment Equation estimate evidence example experience experimental explain Fiedler framing function gambles Gibbs sampling Gigerenzer Griffiths heuristic hidden Markov model human hypothesis inference intuitive Juslin Kahneman Kalman filter language learner likelihood logic Markov memory neural Oaksford objects observed optimal options outcomes overconfidence PageRank parameters participants people’s possible posterior posterior distribution posterior probability predictions prior probability probabilistic models probability distribution problem prospect Psychological Review rational analysis rational models reasoning reference relation representation retrieval sample semantic statistical Steyvers stimuli structure target task Tenenbaum theory tion trials Tversky University Press utility variables words