COMPONENT AND PATTERN INFORMATION IN ADAPTIVE NETWORKS

We (Gluck & Bower, 1988) presented an adaptive network model of human classification in which associative weights are modified according to Rescorla and Wagner's (1972) conditioning theory, a special case of the Widrow-Hoff/LMS learning rule. Presenting empirical data from a series of artificial medical classification tasks, we argued that the network model predicts results that were unanticipated, given prevailing alternative theories of category learning. We consider here some alternative interpretations of this data suggested by Shanks (1990) and argue that they are not sufficiently compelling when compared to the network model's treatment of the data from all the experiments presented in our earlier study.