Empirical Evidence for Markov Chain Monte Carlo in Memory Search

David D. Bourgin, Joshua T. Abbott, Thomas L. Griffiths, Kevin A. Smith, Edward Vul

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014
PublisherThe Cognitive Science Society
Pages224-229
Number of pages6
ISBN (Electronic)9780991196708
StatePublished - 2014
Externally publishedYes
Event36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 26 2014

Publication series

NameProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014

Conference

Conference36th Annual Meeting of the Cognitive Science Society, CogSci 2014
Country/TerritoryCanada
CityQuebec City
Period7/23/147/26/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • Creativity
  • Information retrieval
  • Markov chain Monte Carlo
  • Remote Associates Test
  • Semantic networks

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