A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Sbepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects ' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and Categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed.

A surprisingly large number of research disciplines have contributed towards the development of knowledge on lazy problem solving, which is characterized by its storage of ground cases and its demand driven response to queries. Case-based reasoning (CBR) is an alternative, increasingly popular approach for designing expert systems that implements this approach. This paper lists pointers to some contributions in some related disciplines that offer insights for CBR research. We then outline a small number of Navy applications based on this approach that demonstrate its breadth of applicability. Finally, we list a few successful and failed attempts to apply CBR, and list some predictions on the future roles of CBR in applications.

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A fundamental question in research on conceptual structure concerns how information is represented in memory and used in tasks such as recognizing words. The present research focused on the role of correlations among semantic properties in conceptual memory. Norms were collected for 190 entities from 10 categories. Property intercorrelations influenced people's performance in both a property verification task and a short interval semantic priming experiment. Furthermore, correlated properties were more important for biological kinds than for artifacts. A connectionist model of the computation of word meaning was implemented in which property intercorrelations developed in the course of learning. The model was used to simulate the results of the two experiments. We then tested a novel prediction derived from the model: that the intercorrelational density of a concept's properties should influence the speed with which a concept is computed. This prediction was confirmed in a final experi...

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