Identifying representations of categories of discrete items using Markov chain Monte Carlo with People

Abstract

Identifying the structure of mental representations is a basic problem for cognitive science. We present a method for identifying people’s representations of categories that are defined over a set of discrete items, such as a collection of images. This method builds on previous work using Markov chain Monte Carlo algorithms as the basis for designing behavioral experiments, and we thus call it discrete Markov chain Monte Carlo with People (d-MCMCP). We illustrate how this approach can be used to identify the structure of visual categories using real images drawn from large databases.


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