From William James to Sigmund Freud to Jean Piaget to B.F. Skinner, scholars (and parents!) have wondered how children move from the blooming, buzzing confusion of infancy, through the tumult of childhood and adolescence, into adulthood. Does development occur continuously over time or in a series of dramatic stages? Is development driven by learning or by biological maturational processes? What is the nature of experience, and how does it generate change? The study of development has always been organized around these big questions. And answers to these questions have a profound influence on daily life, forming a framework for how parents think about their own children, and influencing both national policy and educational curricula. This book defines and refines two major theoretical approaches within developmental science that address the central issues of development--connectionism and dynamical systems theory. Spencer, Thomas, and McClelland have brought together chapters that provide an introduction, overview, and critical evaluation of each approach, including three sets of case studies that illustrate how both approaches have been used to study topics ranging from early motor development to the acquisition of grammar. They also present a collection of commentaries by leading scholars, which offer a critical view from both an"outsiders's" and an "insider's" perspective. The book is unique in the range of its treatment--it begins to delineate how developmental science can incorporate advances within neuroscience and computational modeling, and brings the new ideas of connectionism and dynamic systems theory into sharper focus, clarifying their usefulness and explanatory power.

Toward a Unified Theory of Development: Connectionism and Dynamic Systems Theory Re-Considered (Developmental Cognitive Neuroscience)

Health, Fitness & Dieting

Spencer, John

McClelland, James L.

Thomas, Michael S.C.

Psychology & Counseling

Oxford University Press

Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Psychological and Biological Models (Computational Models of Cogn)

Rumelhart, David E.

Mit Pr

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. 
The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. 
Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Foundations

PDP Research Group

Bradford Books

This two-volume work is now considered a classic in the field. It presents the results of the Parallel Distributed Processing (PDP) group's work in the early 1980s and provides a good overview of the earlier neural network research. The PDP approach (also known as connectionism among other things) is based on the conviction that various aspects of cognitive activity are thought of in terms of massively parallel processing. The first volume starts with the general framework and continues with an analysis of learning mechanisms and various mathematical and computational tools important in the analysis of neural networks. The chapter on backpropagation is written by Rumelhart, Hinton, and Williams, who codiscovered the algorithm in 1986. The second volume is written with a psychological and biological emphasis. It explores the relationship of PDP to various aspects of human cognition. The book is a comprehensive research survey of its time and most of the book's results and methods are still at the foundation of the neural network field.

Parallel Distributed Processing: Explorations in the Microstructure of Cognition (2 Volume Set)

This groundbreaking monograph offers a mechanistic theory of the representation and use of semantic knowledge, integrating the strengths and overcoming many of the weaknesses of hierarchical, categorization-based approaches, similarity-based approaches, and the approach often called "theory theory." Building on earlier models by Geoffrey Hinton in the 1980s and David Rumelhart in the early 1990s, the authors propose that performance in semantic tasks arises through the propagation of graded signals in a system of interconnected processing units. The representations used in performing these tasks are patterns of activation across units, governed by weighted connections among them. Semantic knowledge is acquired through the gradual adjustment of the strengths of these connections in the course of day-to-day experience.The authors show how a simple computational model proposed by Rumelhart exhibits a progressive differentiation of conceptual knowledge, paralleling aspects of cognitive development seen in the work of Frank Keil and Jean Mandler. The authors extend the model to address aspects of conceptual knowledge acquisition in infancy, disintegration of conceptual knowledge in dementia, "basic-level" effects and their interaction with expertise, and many findings introduced to support the idea that semantic cognition is guided by naive, domain-specific theories.

Semantic Cognition: A Prallel Distributed Processing Approach

Rogers, Timothy T.

What makes people smarter than computers? The work described in these two volumes suggests that the answer lies in the massively parallel architecture of the human mind. It is some of the most exciting work in cognitive science, unifying neural and cognitive processes in a highly computational framework, with links to artificial intelligence. Although thought and problem solving have a sequential character when viewed over a time frame of minutes or hours, the authors argue that each step in the sequence is the result of the simultaneous activity of a large number of simple computational elements, each influencing others and being influenced by them. Parallel Distributed Processing describes their work in developing a theoretical framework for describing this parallel distributed processing activity and in applying the framework to the development of models of aspects of perception, memory, language, and thought. Volume 2 applies to a number of specific issues in cognitive science and neuroscience. Some chapters describe models of aspects of perception, memory, language, and thought. Others discuss the relation between parallel distributed processing models and neurophysiology or describe models that are specifically addressed to neurophysiological data. The book concludes with an epilogue noting the strengths and weaknesses of the approach and directions for the future.

Parallel Distributed Processing, Vol. 2: Psychological and Biological Models

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind.  The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network.  Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.  David E. Rumelhart is Professor of Psychology at the University of California, San Diego. James L. McClelland is Professor of Psychology at Carnegie-Mellon University. A Bradford Book.

Parallel Distributed Processing - 2 Vol. Set: Explorations in the Microstructure of Cognition

Includes two 5 1/4" DS/DD diskettes for IBM PCs and compatibles

Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises/Software for IBM PC

Computers & Technology

Includes 2 diskettes (for the Macintosh)

Explorations in Parallel Distributed Processing - Macintosh version: A Handbook of Models, Programs, and Exercises

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought. A Bradford Book.

Parallel Distributed Processing, Vol. 1: Foundations

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