RETRIEVAL OF EXEMPLAR AND FEATURE INFORMATION IN CATEGORY LEARNING

Two assumptions were tested regarding what information Ss use during c ategory learning with independent features. One assumption is that Ss use only exemplar information and the other is that they use only feat ure information. Previous work has revealed no clear superiority of on e over the other after training with independent features (W. K. Estes , 1986a, 1986b). The experiments presented here manipulate the opportu nity for using whole exemplars when categorizing test patterns by prov iding either whole or fragmented training patterns. The results show t hat a feature-node network model was superior to feature-frequency and exemplar models at predicting asymptotic test performance after fragm ented-pattern training. However, to achieve this result, all models we re modified to account for possible attentional differences among the training conditions.