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. |
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Page 196
... problems for which the optimal solution is either intractable or uncomputable ( Simon , 1956 ; Reddy , 1988 ; Russell & Norvig , 1995 ) . For the problem of inductive inference , ideal forms of rational calculation are uncomputable ...
... problems for which the optimal solution is either intractable or uncomputable ( Simon , 1956 ; Reddy , 1988 ; Russell & Norvig , 1995 ) . For the problem of inductive inference , ideal forms of rational calculation are uncomputable ...
Page 264
... Problem 2 is generated from Problem 1 by removing a .66 chance of 2,400 from both prospects . The reversal of preference between Problems 1 and 2 violates expected utility theory . Problems 3 and 4 are a variant of the Allais common ...
... Problem 2 is generated from Problem 1 by removing a .66 chance of 2,400 from both prospects . The reversal of preference between Problems 1 and 2 violates expected utility theory . Problems 3 and 4 are a variant of the Allais common ...
Page 265
... Problems 11 and 12 also follow this pattern and show the same effect . Here , however , an initial bonus of 1,000 was given before Problem 11 and 2,000 before Problem 12. Participants failed to integrate the bonus with the gains and ...
... Problems 11 and 12 also follow this pattern and show the same effect . Here , however , an initial bonus of 1,000 was given before Problem 11 and 2,000 before Problem 12. Participants failed to integrate the bonus with the gains and ...
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