Based on Estes' important Fitts Lectures, this volume details a set of psychological concepts and principles that offers a unified interpretation of a wide variety of memory, categorization, and decision-making phenomena. These phenomena are explained via two families of models established by the author: a storage-retrieval model and an adaptive network model. Estes considers whether the models are competing or complementary, offering cogent and instructive arguments for both perspectives. Estes' theory is then applied to two large-scale series of studies on category learning and recognition, providing an integrated understanding of seemingly disparate phenomena. This book is the culmination of the author's more than ten years of research in the field, and stands as a great achievement by one of this century's eminent psychologists. It will be indispensable to a wide variety of behavioral scientists, including mathematical and cognitive psychologists.

Understanding classification is a major challenge for formulating theories of both human cognition and artificial intelligence. Classification is a human mental activity which covers such diverse things as forming concepts, categorizing medical patients, recognising an acquaintance, or discriminating phonetic components of a language. This book considers both previous theories and new research and comes up with a new core model which it proposes as the basis of all forms of classification. Two versions of this model are developed, one based on symbol-processing, and one on connectionist architecture, and it is suggested that modules of each type of model coexist in the human cognitive system, competing for control of behavioural output.