Empirical Evidence for Markov Chain Monte Carlo in Memory Search

David Bourgin	University of California, Berkeley, Berkeley, California
Joshua Abbott	University of California, Berkeley
Tom Griffiths	University of California, Berkeley
Kevin Smith	University of California, San Diego,
Ed Vul	University of California, San Diego

Abstract

Previous theoretical work has proposed the use of Markov chain Monte Carlo as a model of exploratory search in memory. In the current study we introduce such a model and evaluate it on a semantic network against human performance on the Remote Associates Test (RAT), a commonly used creativity metric. We find that a family of search models closely resembling the Metropolis-Hastings algorithm is capable of reproducing many of the response patterns evident when human participants are asked to report their intermediate guesses on a RAT problem. In particular we find that when run our model produces the same response clustering patterns, local dependencies, undirected search trajectories, and low associative hierarchies witnessed in human responses.

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Empirical Evidence for Markov Chain Monte Carlo in Memory Search (115 KB)