Proceedings of the Annual Meeting of the Cognitive Science Society

We investigate the situation in which some target values in the training set for a neural network are left unspecified. After training, unspecified outputs tend to assimilate to certain values as a function of features of the training environment. The roles of the following features in assimilation are analyzed: similarity between input vectors in the training set, similarity between target vectors, linearity versus non-linearity of the mapping, training set size, and error criterion. All are found to have significant effects on the assimilation value of an unspecified output node.