Uncovering Category Representations with Linked MCMC with People

AbstractCognitive science is often concerned with questions about our representations of concepts and the underlying psychological spaces in which these concepts are embedded. One method to reveal concepts and conceptual spaces experimentally is Markov chain Monte Carlo with people (MCMCP), where participants produce samples from their implicit categories. While MCMCP has allowed for the experimental study of psychological representations of complex categories, experiments are typically long and repetitive. Here, we contrasted the classical MCMCP design with a linked variant, in which each participant completed just a short run of MCMCP trials, which were then combined to produce a single sample set. We found that linking produced results that were nearly indistinguishable from classical MCMCP, and often converged to the desired distribution faster. Our results support linking as an approach for performing MCMCP experiments within broader populations, such as in developmental settings where large numbers of trials per participant are impractical

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