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
Results 1-3 of 14
Page 460
... causal chain , which has similar implications . The initial cause should covary with effect_1 and effect_2 , which is caused by effect_1 . Due to the Markov condition , the cause and effect_2 should become independent conditional on ...
... causal chain , which has similar implications . The initial cause should covary with effect_1 and effect_2 , which is caused by effect_1 . Due to the Markov condition , the cause and effect_2 should become independent conditional on ...
Page 472
... causal chain experiment ( Experiment 2 ) . In these conditions , interventions and observa- tions led to equal amounts of search for food . In sum , the results by Blaisdell et al . ( 2006 ) are inconsistent with current associative ...
... causal chain experiment ( Experiment 2 ) . In these conditions , interventions and observa- tions led to equal amounts of search for food . In sum , the results by Blaisdell et al . ( 2006 ) are inconsistent with current associative ...
Page 516
... causal chain model 460 , 469 , 472-3 , 479 causal graphical models 45 causal learning : minimal rational model 453–81 basic case and various special variations 465-8 causal directionality 463-4 development and testing of rational models ...
... causal chain model 460 , 469 , 472-3 , 479 causal graphical models 45 causal learning : minimal rational model 453–81 basic case and various special variations 465-8 causal directionality 463-4 development and testing of rational models ...
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 Review rule sample Science selection semantic shows similar simple statistical structure subjective suggest task theory tion trials University utility variables weight