A Bayesian Hierarchical Mixture Approach to Individual Differences: Case Studies in Selective Attention and Representation in Category Learning

We demonstrate the potential of using a Bayesian hierarchical mixture approach to model individual differences in cognition. Mixture components can be used to identify latent groups of subjects who use different cognitive processes, while hierarchical distributions can be used to capture more minor variation within each group. We apply Bayesian hierarchical mixture methods in two illustrative applications involving category learning. One focuses on a problem that is typically conceived of as a problem of parameter estimation, while the other focuses on a problem that is traditionally tackled from a model selection perspective. Using both previously published and newly collected data, we demonstrate the flexibility and wide applicability of the hierarchical mixture approach to modeling individual differences. ∗Corresponding author. Address: Faculty of Psychology and Educational Sciences, Tiensestraat 102, bus 3713, 3000 Leuven, Belgium. Email: [email protected], Phone: +3216325832, Fax: +3216325993 Data and code are available on the following URL http://faculty.sites.uci.edu/mdlee/BartlemaEtAl2013.zip Preprint submitted to Journal of Mathematical Psychology December 5, 2013