The long-term objective of this research is the development of a general computational model of perceptual categorization and memory, which interrelates performance across a variety of tasks, including classification, identification, and old-new recognition. The goal also is to provide an account of the development of expertise and highly skilled performance in categorization. The present project is organized around the continued development and testing of two models proposed by the principal investigator. According to Nosofsky's (1986) generalized context model (GCM), people represent categories by storing individual exemplars in memory, and make classification and recognition decisions on the basis of similarity comparisons with the stored exemplars. According to the RULEX model of Nosofsky, Palmeri, and McKinley (1994), people learn categories by forming simple rules along single dimensions, and then supplement these rules with occasional exceptions. The present project pursues the idea that both rules and exemplars are fundamental components of the category representation, and seeks to develop a hybrid model that provides a complete account of categorization performance in different stimulus domains and at different stages of learning. Experiments are proposed to test the hybrid model's predictions of classification accuracy and response time, and of how the category representation is expected to evolve as a function of extended training. The project involves a continuing interplay among theory development, experimental testing, and modification of theory in line with newly obtained empirical results. Understanding the fundamental processes of categorization and recognition is one of the central goals of research in memory and cognition. A direct health-related application of the present work would be to provide information about how radiologists make disease classifications on the basis of imperfect information contained in X-ray displays, with the ultimate goal of developing training techniques as well as computer technology to assist in radiological decision making.
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