The Probabilistic Mind: Prospects for Bayesian Cognitive ScienceNick 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
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Page 34
... rule ( Bayes , 1763/1958 ) . When stated in terms of abstract random variables , Bayes ' rule is a simple tautology of probability theory . Assume we have two random variables , A and B.1 One of the principles of probability theory ...
... rule ( Bayes , 1763/1958 ) . When stated in terms of abstract random variables , Bayes ' rule is a simple tautology of probability theory . Assume we have two random variables , A and B.1 One of the principles of probability theory ...
Page 35
... rule seems relatively innocuous ( and perhaps rather uninteresting ! ) . Bayes ' rule gets its strength , and its notoriety , by making some assumptions about the variables we are considering and the meaning of probability . Assume that ...
... rule seems relatively innocuous ( and perhaps rather uninteresting ! ) . Bayes ' rule gets its strength , and its notoriety , by making some assumptions about the variables we are considering and the meaning of probability . Assume that ...
Page 38
... rule is the MAP estimator . Alternatively , if the loss function is the square of the error L ( 0 , α ( d ) ) = ( 0– a ( d ) ) 2 then the best decision rule is the posterior mean . This approach can be extended to dynamical systems ...
... rule is the MAP estimator . Alternatively , if the loss function is the square of the error L ( 0 , α ( d ) ) = ( 0– a ( d ) ) 2 then the best decision rule is the posterior mean . This approach can be extended to dynamical systems ...
Contents
prospects for a Bayesian cognitive science | 3 |
A primer on probabilistic inference | 33 |
Rational analyses instrumentalism and implementations | 59 |
Copyright | |
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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 Review rule sample Science selection semantic shows similar simple statistical structure subjective suggest task theory tion trials University utility variables weight