A Bayesian Latent Mixture Approach to Modeling Individual Differences in Categorization Using General Recognition Theory

Irina Danileiko, Michael D. Lee, Michael L. Kalish

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

5 Scopus citations

Abstract

Decision-bound models of categorization like General Recognition Theory (GRT: Ashby & Townsend, 1986) assume that people divide a stimulus space into different response regions, associated with different categorization decisions. These models have traditionally been applied to empirical data using standard model-fitting methods like maximum likelihood estimation. We implement the GRT as a Bayesian latent mixture model to infer both qualitative individual differences in the types of decision bounds people use, and quantitative differences in where they place the bounds. We apply this approach to a previous data set with two category structures tested under different cognitive loads. Our results show that different participants categorize by applying diagonal, vertical, or horizontal decision bounds. Various types of contaminant behavior are also found, depending on the category structures and presence or absence of load. We argue that our Bayesian latent mixture framework offers a powerful approach to studying individual differences in categorization.

Original languageEnglish (US)
Title of host publicationProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015
EditorsDavid C. Noelle, Rick Dale, Anne Warlaumont, Jeff Yoshimi, Teenie Matlock, Carolyn D. Jennings, Paul P. Maglio
PublisherThe Cognitive Science Society
Pages501-506
Number of pages6
ISBN (Electronic)9780991196722
StatePublished - 2015
Event37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015 - Pasadena, United States
Duration: Jul 23 2015Jul 25 2015

Publication series

NameProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015

Conference

Conference37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015
Country/TerritoryUnited States
CityPasadena
Period7/23/157/25/15

Keywords

  • Bayesian inference
  • category learning
  • decision bound models
  • General Recognition Theory
  • latent mixture model

ASJC Scopus subject areas

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

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