A neural network based on adaptive resonance theory, known as Categ ART has previously been developed to model the actual process of human decision making and to discern the basis for the actual categorizations made, and applied to data on choices made among bank savings schemes. This network is further extended herein to include representations of the criteria for categorization decisions. The strength of a particular criterion representation can be increased if that criterion successfully predicts the appropriate category, and decreased if it leads to ambiguity in the choice of category. Moreover the connections between criterion and category nodes can be modulated by selective attentional biases that may in turn be influenced by external reinforcement. Some possible analogies with frontal lobe function and with animal inductive learning results are discussed.