Rethinking Innateness asks the question, "What does it really mean to say that a behavior is innate?" The authors describe a new framework in which interactions, occurring at all levels, give rise to emergent forms and behaviors. These outcomes often may be highly constrained and universal, yet are not themselves directly contained in the genes in any domain-specific way. One of the key contributions of Rethinking Innateness is a taxonomy of ways in which a behavior can be innate. These include constraints at the level of representation, architecture, and timing; typically, behaviors arise through the interaction of constraints at several of these levels.The ideas are explored through dynamic models inspired by a new kind of "developmental connectionism," a marriage of connectionist models and developmental neurobiology, forming a new theoretical framework for the study of behavioral development. While relying heavily on the conceptual and computational tools provided by connectionism, Rethinking Innateness also identifies ways in which these tools need to be enriched by closer attention to biology.

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

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.

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.

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.

Taking a stand midway between Piaget's constructivism and Fodor's nativism, Annette Karmiloff-Smith offers an exciting new theory of developmental change that embraces both approaches. She shows how each can enrich the other and how both are necessary to a fundamental theory of human cognition. Karmiloff-Smith shifts the focus from what cognitive science can offer the study of development to what a developmental perspective can offer cognitive science. In Beyond Modularity she treats cognitive development as a serious theoretical tool, presenting a coherent portrait of the flexibility and creativity of the human mind as it develops from infancy to middle childhood. Language, physics, mathematics, commonsense psychology, drawing, and writing are explored in terms of the relationship between the innate capacities of the human mind and subsequent representational change which allows for such flexibility and creativity. Karmiloff-Smith also takes up the issue of the extent to which development involves domain-specific versus domain-general processes. She concludes with discussions of nativism and domain specificity in relation to Piagetian theory and connectionism, and shows how a developmental perspective can pinpoint what is missing from connectionist models of the mind.

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 Functionis included in the Network Modeling and Connectionism series, edited by Jeffrey Elman.