Proceedings of the Annual Meeting of the Cognitive Science Society

When humans learn to categorize multidimensional stimuli, they learn which stimulus dimensions are relevant or irrelevant for distinguishing the categories. Results of a category learning experiment are presented, which show that categories defined by a single dimension are much easier to learn than categories defined by the combination of two dimensions. Three models are fit to the data, ALCOVE (Kruschke 1990a,b, in press), standard back propagation (Rumelhart, Hinton & Wilhams 1986), and the configural-cue model (Gluck & Bower 1988). It is found that alcove, with its dimensional attention learning mechanism, can capture the trends in the data, whereas back propagation and the configural-cue model cannot. Implications for other models of human category learning are discussed.