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 48
... algorithm ( Dempster et al . , 1977 ) . Imagine we have a model for data x that has parameters 0 , and latent variables y . A mixture model is one example of such a model , in which the distribution of X , is assumed to be a mixture of ...
... algorithm ( Dempster et al . , 1977 ) . Imagine we have a model for data x that has parameters 0 , and latent variables y . A mixture model is one example of such a model , in which the distribution of X , is assumed to be a mixture of ...
Page 197
... algorithm is to process sequences of observations in order to induce a hypothesis . The hypothesis space of the algorithm can also be viewed as a model class . A model is simply a parameterized family of hypotheses , were each ...
... algorithm is to process sequences of observations in order to induce a hypothesis . The hypothesis space of the algorithm can also be viewed as a model class . A model is simply a parameterized family of hypotheses , were each ...
Page 314
... algorithm that satisfies these desiderata . We will refer to this algorithm as the local MAP algorithm , as it involves assigning each stimulus to the cluster that has the highest posterior probability given the previous assignments ...
... algorithm that satisfies these desiderata . We will refer to this algorithm as the local MAP algorithm , as it involves assigning each stimulus to the cluster that has the highest posterior probability given the previous assignments ...
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