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

Authors
  • Hsu, Anne S.1
  • Martin, Jay B.2
  • Sanborn, Adam N.3
  • Griffiths, Thomas L.4
  • 1 Queen Mary University of London,
  • 2 New York University,
  • 3 University of Warwick,
  • 4 University of California, Berkeley,
Type
Published Article
Journal
Behavior Research Methods
Publisher
Springer - Psychonomic Society
Publication Date
Feb 13, 2019
Volume
51
Issue
4
Pages
1706–1716
Identifiers
DOI: 10.3758/s13428-019-01201-9
PMID: 30761464
PMCID: PMC6691032
Source
PubMed Central
Keywords
License
Unknown

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. Electronic supplementary material The online version of this article (10.3758/s13428-019-01201-9) contains supplementary material, which is available to authorized users.

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