Testing the efficiency of Markov chain Monte Carlo with people using facial affect categories

Jay B. Martin, Thomas L. Griffiths, Adam N. Sanborn

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure of natural categories called Markov chain Monte Carlo with People (MCMCP). Based on an algorithm used in computer science and statistics, MCMCP provides a way to sample from the set of stimuli associated with a natural category. We apply MCMCP and RC to the problem of recovering natural categories that correspond to two kinds of facial affect (happy and sad) from realistic images of faces. Our results show that MCMCP requires fewer trials to obtain a higher quality estimate of people's mental representations of these two categories.

Original languageEnglish (US)
Pages (from-to)150-162
Number of pages13
JournalCognitive science
Volume36
Issue number1
DOIs
StatePublished - Jan 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Classification images
  • Emotion classification
  • Markov chain Monte Carlo
  • Reverse correlation

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