Uncovering mental representations with Markov chain Monte Carlo

Adam N. Sanborn, Thomas L. Griffiths, Richard M. Shiffrin

Research output: Contribution to journalArticle

33 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)63-106
Number of pages44
JournalCognitive Psychology
Volume60
Issue number2
DOIs
StatePublished - Mar 1 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Artificial Intelligence

Keywords

  • Categorization
  • Experimental design
  • Markov chain Monte Carlo
  • Statistics

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