TY - JOUR
T1 - Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people
AU - Hsu, Anne S.
AU - Martin, Jay B.
AU - Sanborn, Adam N.
AU - Griffiths, Thomas L.
N1 - Funding Information:
All authors contributed to developing the study concept. T.L.G. and J.B.M. contributed to the design of Experiment 1 , all authors contributed to the design of Experiment 2 , and A.S.H. and T.L.G. contributed to the design of Experiment 3. A.S.H. programmed and ran the experimental studies and analyzed the results. A.S.H. drafted the manuscript, T.L.G. and J.B.M. provided critical revisions, and all authors approved the final version for submission. Part of this work was published in a conference proceeding for the Cognitive Science Society (Hsu et al., 2012). This work was supported by Grant No. IIS-0845410 from the National Science Foundation and by Grant No. ERC-2013-AdG339182-BAYES_KNOWLEDGE from the European Research Council.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
KW - Category representation
KW - Image databases
KW - Markov chain Monte Carlo
KW - Words representations
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U2 - 10.3758/s13428-019-01201-9
DO - 10.3758/s13428-019-01201-9
M3 - Article
C2 - 30761464
AN - SCOPUS:85061582612
SN - 1554-351X
VL - 51
SP - 1706
EP - 1716
JO - Behavior Research Methods
JF - Behavior Research Methods
IS - 4
ER -