These two volumes consist of chapters written by students and colleagues of W.K. Estes. The books' contributors -- themselves eminent figures in the field -- reflect on Estes' sweeping contributions to mathematical as well as cognitive and experimental psychology. As indicated by their titles, Volume I features mathematical and theoretical essays, and Volume II presents cognitive and experimental essays. Both volumes contain insightful literature reviews as well as descriptions of exciting new theoretical and empirical advances. Many of the essays also incorporate personal reminiscences reflecting the authors' fond affection for their illustrious mentor.

These two volumes consist of chapters written by students and colleagues of W.K. Estes. The books' contributors -- themselves eminent figures in the field -- reflect on Estes' sweeping contributions to mathematical as well as cognitive and experimental psychology. As indicated by their titles, Volume I features mathematical and theoretical essays, and Volume II presents cognitive and experimental essays. Both volumes contain insightful literature reviews as well as descriptions of exciting new theoretical and empirical advances. Many of the essays also incorporate personal reminiscences reflecting the authors' fond affection for their illustrious mentor.

A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.

Presenting research on the computational abilities of connectionist, neural, and neurally inspired systems, this series emphasizes the question of how connectionist or neural network models can be made to perform rapid, short-term types of computation that are useful in higher level cognitive processes. The most recent volumes are directed mainly at researchers in connectionism, analogy, metaphor, and case-based reasoning, but are also suitable for graduate courses in those areas.

This resource defines and refines two major theoretical approaches within developmental science that address the central issues of development-connectionism and dynamical systems theory.

Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.

First Published in 1992. Routledge is an imprint of Taylor & Francis, an informa company.

Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.

This unique two-volume set provides detailed coverage of contemporary learning theory. Uniting leading experts in modern behavioral theory, these texts give students a complete view of the field. Volume I details the complexities of Pavlovian conditioning and describes the current status of traditional learning theories. Volume II discusses several important facets of instrumental conditioning and presents comprehensive coverage of the role of inheritance on learning. A strong and complete base of knowledge concerning learning theories, these volumes are ideal reference sources for a.