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Uncovering Category Representations with Linked MCMC with people
Abstract
Cognitive science is often concerned with questions about ourrepresentations of concepts and the underlying psychologicalspaces in which these concepts are embedded. One methodto reveal concepts and conceptual spaces experimentally isMarkov chain Monte Carlo with people (MCMCP), whereparticipants produce samples from their implicit categories.While MCMCP has allowed for the experimental study of psy-chological representations of complex categories, experimentsare typically long and repetitive. Here, we contrasted the clas-sical MCMCP design with a linked variant, in which each par-ticipant completed just a short run of MCMCP trials, whichwere then combined to produce a single sample set. We foundthat linking produced results that were nearly indistinguishablefrom classical MCMCP, and often converged to the desired dis-tribution faster. Our results support linking as an approach forperforming MCMCP experiments within broader populations,such as in developmental settings where large numbers of trialsper participant are impractical.
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