Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people

Anne S. Hsu, Jay B. Martin, Adam N. Sanborn, Thomas L. Griffiths

Research output: Contribution to journalArticle

Abstract

With the explosion of “big data,” digital repositories of texts and images are growing rapidly. These datasets present new opportunities for psychological research, but they require new methodologies before researchers can use these datasets to yield insights into human cognition. We present a new method that allows psychological researchers to take advantage of text and image databases: a procedure for measuring human categorical representations over large datasets of items, such as arbitrary words or pictures. We call this method discrete Markov chain Monte Carlo with people (d-MCMCP). We illustrate our method by evaluating the following categories over datasets: emotions as represented by facial images, moral concepts as represented by relevant words, and seasons as represented by images drawn from large online databases. Three experiments demonstrate that d-MCMCP is powerful and flexible enough to work with complex, naturalistic stimuli drawn from large online databases.

Original languageEnglish (US)
Pages (from-to)1706-1716
Number of pages11
JournalBehavior Research Methods
Volume51
Issue number4
DOIs
StatePublished - Aug 15 2019

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)

Keywords

  • Category representation
  • Image databases
  • Markov chain Monte Carlo
  • Words representations

Fingerprint Dive into the research topics of 'Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people'. Together they form a unique fingerprint.

  • Cite this