INSTANCE-BASED MODELS OF LEARNING MEMORY AND JUDGMENT

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This project aims to improve our understanding of the basic processes of human learning and memory by developing instance-based models of memory. These models have been used to explain categorization tasks by assuming that our mental representations of categories consist of sets of category members or exemplars. Instance models are extended here to explain more complex phenomena from the areas of learning, memory, and judgment. These phenomena include reasoning from chains of examples, conceptual combination, learning relations between multiple variables, and judgment of event frequency. In experimental tests of these extended models, subjects will study examples, such as personality trait descriptions for a group of people, then make probability or classification judgments about the traits. The long-term objective of this research is to develop instance-based models into a framework that helps psychologists understand a wide range of cognitive abilities.