Identifying strategy use in category learning tasks: A case for more diagnostic data and models

The strength of conclusions about the adoption of different categorization strategies-and their implications for theories about the cognitive and neural bases of category learning-depend heavily on the techniques for identifying strategy use. We examine performance in an often-used "informationintegration" category structure and demonstrate that strategy identification is affected markedly by the range of models under consideration, the type of data collected, and model-selection techniques. We use a set of 27 potential models that represent alternative rule-based and information-integration categorization strategies. Our experimental paradigm includes the presentation of nonreinforced transfer stimuli that improve one's ability to discriminate among the predictions of alternative models. Our modelselection techniques incorporate uncertainty in the identification of individuals as either rule-based or information-integration strategy users. Based on this analysis we identify 48% of participants as unequivocally using an information-integration strategy. However, adopting the standard practice of using a restricted set of models, restricted data, and ignoring the degree of support for a particular strategy, we would typically conclude that 89% of participants used an information-integration strategy. We discuss the implications of potentially erroneous strategy identification for the security of conclusions about the categorization capabilities of various participant and patient groups.