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Uncovering Mental Representations with Markov Chain Monte Carlo

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Title: Uncovering Mental Representations with Markov Chain Monte Carlo
Language: English
Authors: Sanborn, Adam N., Griffiths, Thomas L., Shiffrin, Richard M.
Source: Cognitive Psychology. Mar 2010 60(2):63-106.
Availability: Elsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
Peer Reviewed: Y
Physical Description: PDF
Page Count: 44
Publication Date: 2010
Document Type: Journal Articles
Reports - Research
Descriptors: Markov Processes, Multidimensional Scaling, Cognitive Psychology, Probability, Animals, Classification, Food, Research Methodology, Computation, Monte Carlo Methods, Psychological Studies
DOI: 10.1016/j.cogpsych.2009.07.001
ISSN: 0010-0285
Abstract: 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. (Contains 21 figures and 4 tables.)
Abstractor: As Provided
Entry Date: 2009
Accession Number: EJ863558
Database: ERIC
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  Data: Elsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
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  Data: 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. (Contains 21 figures and 4 tables.)
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