Connectionist Models in Cognitive Psychology is a state-of-the-art review of neural network modelling in core areas of cognitive psychology including: memory and learning, language (written and spoken), cognitive development, cognitive control, attention and action. The chapters discuss neural network models in a clear and accessible style, with an emphasis on the relationship between the models and relevant experimental data drawn from experimental psychology, neuropsychology and cognitive neuroscience. These lucid high-level contributions will serve as introductory articles for postgraduates and researchers whilst being of great use to undergraduates with an interest in the area of connectionist modelling.
Author: Julien Mayor
Publisher: World Scientific
Release Date: 2009
The neural computational approach to cognitive and psychological processes is relatively new. However, Neural Computation and Psychology Workshops (NCPW), first held 16 years ago, lie at the heart of this fast-moving discipline, thanks to its interdisciplinary nature ? bringing together researchers from different disciplines such as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their work on models of cognitive processes.Once again, the Eleventh Neural Computation and Psychology Workshop (NCPW11), held in 2008 at the University of Oxford (England), reflects the interdisciplinary nature and wide range of backgrounds of this field. This volume is a collection of peer-reviewed contributions of most of the papers presented at NCPW11 by researchers from four continents and 15 countries.
Author: Martin A. Conway
Publisher: MIT Press
Release Date: 1997
The chapters of this volume evaluate models of the short-term retention of knowledge, conceptual knowledge, autobiographical knowledge, transitory mental representations, the neurobiological basis of memory, and age-related changes in human memory.
Exploring Cognition: Damaged Brains and Neural Networks analyses the contribution made by cognitive neuropsychology and connectionist modelling to theoretical explanations of cognitive processes. Bringing together evidence from both damaged brains and neural networks, this exciting and innovative approach leads to re-evaluation of traditional theories: connectionist models lesioned to mimic the residual function of the damaged brain and rehabilitated to simulate the process of recovery suggest underlying mechanisms and challenge previous interpretations. In this reader key articles by leading international researchers are combined with linking commentaries that provide a context, highlight the conceptual themes and evaluate the evidence. Carefully selected to include hotly debated topics, the papers cover, among others, the controversies surrounding explanations for category specificity in object recognition and for covert recognition of faces and words; the mechanisms underlying the use of regular and irregular past tenses; and the reading of regularly and irregularly spelled words. The challenges posed by connectionist models to assumptions about the nature of dissociations, the need for symbolic rule-based operations in language processing and the modularity and localisation of processes are assessed. Exploring Cognition: Damaged Brains and Neural Networks will be of interest to advanced undergraduates, postgraduates and researchers in cognitive neuropsychology and cognitive neuroscience.
This volume provides an overview of a relatively neglected branch of connectionism known as localist connectionism. The singling out of localist connectionism is motivated by the fact that some critical modeling strategies have been more readily applied in the development and testing of localist as opposed to distributed connectionist models (models using distributed hidden-unit representations and trained with a particular learning algorithm, typically back-propagation). One major theme emerging from this book is that localist connectionism currently provides an interesting means of evolving from verbal-boxological models of human cognition to computer-implemented algorithmic models. The other central messages conveyed are that the highly delicate issue of model testing, evaluation, and selection must be taken seriously, and that model-builders of the localist connectionist family have already shown exemplary steps in this direction.
Author: Philip T. Quinlan
Publisher: University of Chicago Press
Release Date: 1991
The rapid growth of neural network research has led to a major reappraisal of many fundamental assumptions in cognitive and perceptual psychology. This text—aimed at the advanced undergraduate and beginning postgraduate student—is an in-depth guide to those aspects of neural network research that are of direct relevance to human information processing. Examples of new connectionist models of learning, vision, language and thought are described in detail. Both neurological and psychological considerations are used in assessing its theoretical contributions. The status of the basic predicates like exclusive-OR is examined, the limitations of perceptrons are explained and properties of multi-layer networks are described in terms of many examples of psychological processes. The history of neural networks is discussed from a psychological perspective which examines why certain issues have become important. The book ends with a general critique of the new connectionist approach. It is clear that new connectionism work provides a distinctive framework for thinking about central questions in cognition and perception. This new textbook provides a clear and useful introduction to its theories and applications.
The philosophy of cognitive science has recently become one of the most exciting and fastest growing domains of philosophical inquiry and analysis. Until the early 1980s, nearly all of the models developed treated cognitive processes -- like problem solving, language comprehension, memory, and higher visual processing -- as rule-governed symbol manipulation. However, this situation has changed dramatically over the last half dozen years. In that period there has been an enormous shift of attention toward connectionist models of cognition that are inspired by the network-like architecture of the brain. Because of their unique architecture and style of processing, connectionist systems are generally regarded as radically different from the more traditional symbol manipulation models. This collection was designed to provide philosophers who have been working in the area of cognitive science with a forum for expressing their views on these recent developments. Because the symbol-manipulating paradigm has been so important to the work of contemporary philosophers, many have watched the emergence of connectionism with considerable interest. The contributors take very different stands toward connectionism, but all agree that the potential exists for a radical shift in the way many philosophers think of various aspects of cognition. Exploring this potential and other philosophical dimensions of connectionist research is the aim of this volume.
Author: Stephen José Hanson
Publisher: Bradford Books
Release Date: 1990
Bringing together contributions in biology, neuroscience, computer science, physics, and psychology, this book offers a solid tutorial on current research activity in connectionist-inspired biology-based modeling. It describes specific experimental approaches and also confronts general issues related to learning associative memory, and sensorimotor development. Introductory chapters by editors Hanson and Olson, along with Terrence Sejnowski, Christof Koch, and Patricia S. Churchland, provide an overview of computational neuroscience, establish the distinction between "realistic" brain models and "simplified" brain models, provide specific examples of each, and explain why each approach might be appropriate in a given context. The remaining chapters are organized so that material on the anatomy and physiology of a specific part of the brain precedes the presentation of modeling studies. The modeling itself ranges from simplified models to more realistic models and provides examples of constraints arising from known brain detail as well as choices modelers face when including or excluding such constraints. There are three sections, each focused on a key area where biology and models have converged. Stephen Jose Hanson is Member of Technical Staff, Bellcore, and Visiting Faculty, Cognitive Science Laboratory, Princeton University. Carl R. Olson is Assistant Professor, Department of Psychology at Princeton Connectionist Modeling and Brain Function is included in the Network Modeling and Connectionism series, edited by Jeffrey Elman.
This text provides a state-of-the-art overview of research that attempts to model aspects of human behaviour in terms of 'connectionist' architectures, using models with neural-like structures. The text covers the history of connectionism, the different connectionist architectures and learning algorithms that have been used. It then relates the models to psychological data on: memory, perception, sequential behaviour and attention, single word and higher-order linguistic processing, and neuropsychological disorders. Linked to the text are critical papers that serve to illustrate connectionist approaches to understanding the mind. The connectionist approach is reviewed in relation to other approaches to modelling in cognitive science, and pointers to future directions for research are outlined.
Author: Philip T. Quinlan
Publisher: Psychology Press
Release Date: 2004-03-01
Connectionist Models of Development is an edited collection of essays on the current work concerning connectionist or neural network models of human development. The brain comprises millions of nerve cells that share myriad connections, and this book looks at how human development in these systems is typically characterised as adaptive changes to the strengths of these connections. The traditional accounts of connectionist learning, based on adaptive changes to weighted connections, are explored alongside the dynamic accounts in which networks generate their own structures as learning proceeds. Unlike most connectionist accounts of psychological processes which deal with the fully-mature system, this text brings to the fore a discussion of developmental processes. To investigate human cognitive and perceptual development, connectionist models of learning and representation are adopted alongside various aspects of language and knowledge acquisition. There are sections on artificial intelligence and how computer programs have been designed to mimic the development processes, as well as chapters which describe what is currently known about how real brains develop. This book is a much-needed addition to the existing literature on connectionist development as it includes up-to-date examples of research on current controversies in the field as well as new features such as genetic connectionism and biological theories of the brain. It will be invaluable to academic researchers, post-graduates and undergraduates in developmental psychology and those researching connectionist/neural networks as well as those in related fields such as psycholinguistics.
"Horgan and Tienson's Connectionism and the Philosophy of Psychology develops an outline of a truly original theory of cognition. No one interested in the theory of cognitive architecture can afford to ignore this book." -- Brian P. McLaughlin, Professor of Philosophy, Rutgers University "A fascinating read. The book is original and thought-provoking. Horgan and Tienson has staked out a new and sophisticated position on cognition, which is likely to find a very wide audience indeed in both philosophy and cognitive science." -- Michael Tye, Professor of Philosophy, Temple University; Visiting Professor of Philosophy, King's College, London Human cognition is soft. It is too flexible, too rich, and too open-ended to be captured by hard (precise, exceptionless) rules of the sort that can constitute a computer program. In Connectionism and the Philosophy of Psychology, Horgan and Tienson articulate and defend a new view of cognition. In place of the classical paradigm that take the mind to be a computer (or a group of linked computers), they propose that the mind is best understood as a dynamical system realized in a neural network. Although Horgan and Tienson assert that cognition cannot be understood in classical terms of the algorithm-governed manipulation of symbols, they don't abandon syntax. Instead, they insist that human cognition is symbolic, and that cognitive processes are sensitive to the structure of symbols in the brain: the very richness of cognition requires a system of mental representations within which there are syntactically complex symbols and structure-sensitive processing. However, syntactic constituentsneed not be parts of complex representations, and structure sensitive processes need not conform to algorithms. Cognition requires a language of thought, but a language of thought implicated in processes that are not governed by hard rules. Instead, symbols are generated and transformed in response to interacting cognitive forces, which are determined by multiple, simultaneous, (robustly) soft constraints. Thus, cognitive processes conform to soft (ceteris paribus) laws, rather than to hard laws. Cognitive forces are subserved by, but not identical with, physical forces in a network; the organization and the interaction of cognitive forces are best understood in terms of the mathematical theory of dynamical systems. The concluding chapter elaborates the authors' proposed dynamical cognition framework. "A Bradford Book"
Author: Ronald T. Kellogg
Release Date: 2003
"This is a very thorough and complete text that is very well written. I was particularly impressed that the book incorporated and integrated the literatures on neuroscience and individual differences." -- Randall Engle, Georgia Institute of Technology As with his best-selling First Edition, Ronald T. Kellogg seeks to provide students with a synthesis of cognitive psychology at its best, encapsulating relevant background, theory, and research within each chapter. Understanding cognitive psychology now requires a deeper understanding of the brain than was true in the past. In his thoroughly revised Second Edition, the author highlights the tremendous contributions from the neurosciences, most notably neuroimaging, in recent years and approaches cognition in the context of both its development and its biological, bodily substrate. An Instructor's Manual on CD-ROM is available to qualified adopters.
Author: John Andrew Bullinaria
Publisher: World Scientific
Release Date: 2002
Connectionist Models of Cognition and Perception collects together refereed versions of twenty-three papers presented at the Seventh Neural Computation and Psychology Workshop (NCPW7). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on connectionist modelling in psychology.The articles have the main theme of connectionist modelling of cognition and perception, and are organised into six sections, on: cell assemblies, representation, memory, perception, vision and language. This book is an invaluable resource for researchers interested in neural models of psychological phenomena.