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What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models. (Contains 7 tables and 5 figures.)

We report four experiments examining effects of instance similarity on the application of simple explicit rules. We found effects of similarity to illustrative exemplars in error patterns and reaction times. These effects arose even though participants were given perfectly predictive rules, the similarity manipulation depended entirely on rule-irrelevant features, and attention to exemplar similarity was detrimental to task performance. Comparison of results across studies suggests that the effects are mandatory, non-strategic and not subject to conscious control, and as a result, should be pervasive throughout categorization. (Contains 2 figures and 5 tables.)

When participants are asked to spontaneously categorize a set of items, they typically produce unidimensional classifications, i.e., categorize the items on the basis of only one of their dimensions of variation. We examine whether it is possible to predict unidimensional vs. two-dimensional classification on the basis of the abstract stimulus structure, by employing Pothos and Chater's simplicity model of spontaneous categorization [Pothos, E. M., & Chater, N. (2002). "A simplicity principle in unsupervised human categorization." "Cognitive Science," 26, 303-343]. The simplicity model provides a quantitative measure of how intuitive a particular classification is. With objects represented in two dimensions, we propose that a unidimensional classification will be preferred if it is more intuitive than all possible two-dimensional ones, and vice versa. Empirical results supporting this proposal are reported. Implications for Goodman's paradox are discussed.