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

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.


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