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.
Highlights
With the explosion of Bbig data,^ digital repositories of texts and images are growing rapidly
Previous work showed by averaging the generated images in the chain that the samples generated by Markov chain Monte Carlo with people (MCMCP) more quickly converged to being representative of people’s mental representations of each category than did alternative methods
It seems likely that discrete Markov chain Monte Carlo with people (d-MCMCP) would show similar advantage over these other approaches
Summary
With the explosion of Bbig data,^ digital repositories of texts and images are growing rapidly. 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). To allow exploration of representations over large sets of discrete items we introduce a new method called discrete Markov chain Monte Carlo with people (d-MCMCP) This method allows estimation of subjective probability distributions over arbitrary datasets, such as images or text snippets. The resulting distributions can be used to identify the structure of people’s psychological representations in these domains
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