Uncovering mental representations with Markov chain Monte Carlo
Sanborn, Adam N., Griffiths, Thomas L. and Shiffrin, Richard M.. (2010) Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, Vol.60 (No.2). pp. 63-106. ISSN 00100285Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.cogpsych.2009.07.001
A key challenge for cognitive psychology is the investigation of mental representations, such as object categories, subjective probabilities, choice utilities, and memory traces. In many cases, these representations can be expressed as a non-negative function defined over a set of objects. We present a behavioral method for estimating these functions. Our approach uses people as components of a Markov chain Monte Carlo (MCMC) algorithm, a sophisticated sampling method originally developed in statistical physics. Experiments 1 and 2 verified the MCMC method by training participants on various category structures and then recovering those structures. Experiment 3 demonstrated that the MCMC method can be used estimate the structures of the real-world animal shape categories of giraffes, horses, dogs, and cats. Experiment 4 combined the MCMC method with multidimensional scaling to demonstrate how different accounts of the structure of categories, such as prototype and exemplar models, can be tested, producing samples from the categories of apples, oranges, and grapes.
|Item Type:||Journal Article|
|Subjects:||B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Divisions:||Faculty of Science > Psychology|
|Library of Congress Subject Headings (LCSH):||Markov processes -- Testing, Monte Carlo method, Cognitive psychology, Categorization (Psychology) -- Testing , Experimental design|
|Journal or Publication Title:||Cognitive Psychology|
|Page Range:||pp. 63-106|
|Access rights to Published version:||Restricted or Subscription Access|
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