The implications of null patterns and output unit activation functions on simulation studies of learning: A case study of patterning

Abstract

Animal learning researchers have argued that one example of a linearly nonseparable problem is negative patterning, and therefore they have used more complicated multilayer networks to study this kind of discriminant learning. However, it is shown in this paper that previous attempts to define negative patterning problems to artificial neural networks have specified the problem in such a way that it is much simpler than intended. The simulations described in this paper correct this problem by adding a “null” pattern to the training sets to make negative patterning problems truly nonseparable, and thus requiring a more complicated network than a perceptron. We show that with the elaborated training set, a hybrid multilayer network that treats reinforced patterns differently than nonreinforced patterns generates results more similar to those observed by Dalamater, Sosa, and Katz in animal experiments than do traditional multilayer networks.