Generalization of a pattern categorization task was investigated in a simple, deterministic,inductive learning task. Each of eight patterns in a training set was specified in terms of four binary features. After subjects learned to categorize these patterns in a supervised learning paradigm they were asked generalize their knowledge by categorizing novel patterns. We analyzed both the details of the learning process as well as subjects' generalizations to novel patterns.Certain patterns in the training set were consistency found to be more difficult to learn than others.The subsequent generalizations made by subjects indicate that in spite of important individual differences, subjects showed systematic similarities in how they generalized to novel situations.The generalization performance of subjects was compared to those that could possibly be generated by a two-layer adaptive network. A comparison of network and human generalization syndicate that using a minimal network architecture is not a sufficient constraint to guarantee that a network will generalize the way humans do
