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Exploring the structure of mental representations by implementing computer algorithms with people

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Sanborn, Adam N. and Griffiths, Thomas L. (2015) Exploring the structure of mental representations by implementing computer algorithms with people. In: Raaijmakers, J. G. W. and Criss, A. H. and Goldstone, R. L. and Nosofsky, R. M. and Steyvers, M., (eds.) Cognitive Modeling in Perception and Memory: A Festschrift for Richard M. Shiffrin. New York: Psychology Press ; Taylor & Francis Group, pp. 212-228.

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Official URL: http://www.worldcat.org/oclc/886492191

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Abstract

Computer metaphors have been a mainstay of cognitive psychology, treating minds like information processing systems that operate on memory and mix serial and parallel processes. Recent work has taken this idea beyond a metaphor, examining the consequences of treating people as components in an algorithm that is usually executed by digital computers. By implementing a popular class of randomized algorithms – Markov chain Monte Carlo algorithms – with people, we are able to generate samples from subjective probability distributions that reveal the mental representations that inform people’s judgments. In this chapter, we summarize the key idea behind this approach and review a number of ways in which it has recently been extended.

Item Type: Book Item
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Divisions: Faculty of Science, Engineering and Medicine > Science > Psychology
Publisher: Psychology Press ; Taylor & Francis Group
Place of Publication: New York
Book Title: Cognitive Modeling in Perception and Memory: A Festschrift for Richard M. Shiffrin
Editor: Raaijmakers, J. G. W. and Criss, A. H. and Goldstone, R. L. and Nosofsky, R. M. and Steyvers, M.
Official Date: 2015
Dates:
DateEvent
2015Published
Number of Pages: 291
Page Range: pp. 212-228
Status: Peer Reviewed
Publication Status: Published
Date of first compliant deposit: 12 March 2017
Funder: Economic and Social Research Council (Great Britain) (ESRC), United States. Air Force. Office of Scientific Research (AFOSR)
Grant number: ES/K004948/1

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