A central issue in cognitive science is whether learning and processing constraints are particular to domains or whether they generalize across domains. In this paper the domain-generality of a particular type of constraint, linear separability, was examined. Prior research has found that decisions in the social domain are often consistent with linear separability but this is rarely true of decisions in the object domain. Two experiments were conducted to examine the generality of this result by using fiindamentally different types of social and object materials than have been used in previous research. In both experiments different integration strategies were observed in social and object domains, and as in prior research many more Summation sorts occurred with social materials. These results indicate that previous differences that have been observed between object and social domains generalize to very different types of object and social materials. At a general level the results indicate that the structure of knowledge varies with domain, and consequently it will be difficult to formulate domaingeneral constraints in terms of abstract structural properties such as linear separability.

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.

Classification models have implicitly assumed that the nature of the representation that emerges from encoding will determine the type of classification strategy that will be used. These experiments, however, demonstrate that differences in classification performance can occur even when different transfer strategies operate on identical representations. Specifically, a series of examples was presented under incidental concept learning conditions. When the encoding task was completed, subjects were induced to make transfer decisions by analogy to stored information or to search for and apply rules. Across four experiments, an analogical transfer mode was found to be more effective than a rule-based transfer mode for preserving co-occurring features in classification decisions. This result held across a variety of category structures and stimulus materials. It was difficult for subjects who adopted an analytic transfer strategy to test hypotheses and identify regularities that were embedded in stored instances. Alternatively, subjects who adopted an analogical strategy preserved feature covariations as an indirect result of similarity-based retrieval and comparison processes.