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AbstractCategorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of divergent, non-complementary models with little consensus on the relative adequacy of these accounts.Progress on assessing relative adequacy of formal categorization models against these criteria has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered, and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).Keywords: categorization; cluster; exemplar; model selection; modeling; prototype.
EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 2The study of categorization is a fascinating endeavor. The process of constructing and using categories underpins our capacity to encode and apply information in the world in an efficient and competent manner but also, ultimately, our ability to think in terms of abstract kind of categorization models should we aim to develop? The lack of consensus regarding such key issues has resulted in categorization research being carried out in increasingly independent strands and this has been inhibiting overall progress in the field. Nosofsky, Gluck, Palmeri, McKinley and Glauthier (1994) wrote, "Recent years have seen an avalanche of newly proposed models of category learning and representation. As such models grow increasingly more sophisticated, there is a need to develop increasingly more rigorous testing grounds so that one may choose among them" (p. 352). Almost 20 years later, progress towards this goal remains limited.In the current article, we first provide a definition of the term formal model, consider the principal advantages of formal modeling over other forms of theorizing, briefly summarize some of the leading formal models of categorization, and assess progress to date on the empirical evaluation and comparison of these models. We then set out the approaches EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 3 we believe are most likely to lead to progress in the future. We organize our conclusions in terms of a set of criteria for assessing the relative adequacy of models, and a list of dependent and independent variables that any adequate formal model of categorization should be expected to...