## The Probabilistic Mind: Prospects for Bayesian Cognitive ScienceDepartment of Psychology Nick Chater, Nick Chater, Mike Oaksford 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. |

### From inside the book

Results 1-3 of 93

Page 34

Prospects for

, Nick Chater, Mike Oaksford. algorithms that are used to evaluate the predictions

of probabilistic models — the Expectation - Maximization ( EM ) algorithm , and ...

Prospects for

**Bayesian**Cognitive Science Department of Psychology Nick Chater, Nick Chater, Mike Oaksford. algorithms that are used to evaluate the predictions

of probabilistic models — the Expectation - Maximization ( EM ) algorithm , and ...

Page 421

Prospects for

, Nick Chater, Mike Oaksford ... Paired - sample t - tests show that the

model performs significantly better than the associative model with decreasing or

...

Prospects for

**Bayesian**Cognitive Science Department of Psychology Nick Chater, Nick Chater, Mike Oaksford ... Paired - sample t - tests show that the

**Bayesian**model performs significantly better than the associative model with decreasing or

...

Page 515

Prospects for

, Nick Chater, Mike Oaksford. Index 2D trade - off options 289 – 92 , 296 absolute

difference 218 absolute expected difference 216 – 17 , 220 _ 1 accumulators ...

Prospects for

**Bayesian**Cognitive Science Department of Psychology Nick Chater, Nick Chater, Mike Oaksford. Index 2D trade - off options 289 – 92 , 296 absolute

difference 218 absolute expected difference 216 – 17 , 220 _ 1 accumulators ...

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

prospects for a Bayesian cognitive science | 3 |

A primer on probabilistic inference | 33 |

Rational analyses instrumentalism and implementations | 59 |

Copyright | |

20 other sections not shown

### Other editions - View all

The Probabilistic Mind: Prospects for Bayesian Cognitive Science Nick Chater,Mike Oaksford Limited preview - 2008 |

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

### Common terms and phrases

algorithm alternative analysis approach approximate argument associated assumed assumption attribute Bayesian behavior beliefs Cambridge causal cause Chater choice cluster cognitive complexity computational concept conditional consider correlation decision depends described developed distribution effect environment estimate et al evidence example expected experience Experimental explain framing function given heuristic human hypothesis important individual inference involved Journal judgment language learning logic mean memory methods natural normative Oaksford objects observed optimal options outcomes parameters participants particular performance possible posterior predictions present Press principle prior probabilistic probability problem produce prospect Psychological question rational rational analysis reasoning reference relation relative represent representation require response rule sample Science selection semantic shows similar simple statistical structure subjective suggest task theory tion topic trials University utility variables weight