Proceedings of the Annual Meeting of the Cognitive Science Society

In this paper we examined the extent to which linear separability constrained learning and categorization in different content domains. Linear separability has been a focus of research in many different areas such as categorization, connectionist modeling, machine learning, and social cognition. In relation to categorization, linearly separable (LS) categories are categories that can be perfectly partitioned on the basis of a weighted, additive combination of component information. W e examined the importance of linear separability in object and social domains. Across seven exp)eriments that used a wide variety of stimulus materials and classification tasks, LS structures were found to be more compatible with social than object materials. Nonlincarly separable structures, however, were more compatible with object than social materials. This interaction between linear separability and content domain was attributed to differences in the types of knowledge and integration strategies that were activated. It was concluded that the structure of knowledge varies with domain, and consequently it will be difficult to formulate domain general constraints in terms of abstract structural properties such as linear separability.