Modeling Individual Differences in Category Learning
Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility that there are important individual differences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these weaknesses, we develop a general approach to modeling individual differences using families of cognitive models, where different groups of subjects are identified as having different psychological behavior. Separate models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine the appropriate number of groups. We demonstrate the general approach in a concrete and detailed way using the ALCOVE model of category learning and data from four previously analysed category learning experiments. Meaningful individual differences are found for three of the four experiments, and ALCOVE is able to account for this variation through psychologically interpretable differences in parameterization. The results highlight the potential of extending cognitive models to consider individual differences.