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

Abstract

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 "information-integration" 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 model-selection 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.