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. •Bayesian hierarchical mixture methods are used to model individual differences.•We demonstrate this method in two example applications in the domain of category learning.•These analyses lead to different conclusions than analyses on grouped data.•Different groups of people and variation within each group when learning a category.•Hierarchical mixture models suitable for parameter estimation and model selection.