Proceedings of the Annual Meeting of the Cognitive Science Society

Unsupervised learning is widely recognized as an important problem in cognitive science, but unsupervised learning inhumans has received relatively little empirical investigation to date. We investigate unsupervised categorization usinga new task in which people generate verbal labels to novel objects, with objects given the same label assumed to bein the same mental category. Our main finding is that categorization is determined by similarity, i.e., the probabilityof placing two objects into the same category is an exponentially declining function of their dissimilarity, consistentwith Shepard’s (1987) universal law of generalization. We present data demonstrating the overall exponential pattern,plus specific predictions regarding selective attention, sensitivity to correlated features, and the effects of category size(number of examples). Taken together, the results suggest that the similarity-based approach used successfully in modelsof supervised categorization (e.g., Nosofsky 1986, 1992) may also extend to the domain of unsupervised categorization.