This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the computational cognitive neuroscience. The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software.

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.

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.

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.

This volume considers how children's thinking evolves during development, with a focus on the role of experience in causing change. It brings together cutting-edge research by leaders in the psychology and neurobiology of child development to examine the processes by which children learn and those that make children ready and able to learn at particular points in development.
Behavioral approaches include research on the "microgenesis" of cognitive change over short time periods (e.g., several hour-long sessions) in specific task situations. Research on cognitive change over longer time scales (months and years) is also presented, as well as research that uses computational modeling and dynamical systems approaches to understand learning and development.
Neural approaches include the study of how neuronal activity and connectivity change during acquisition of cognitive skills in children and adults. Other investigations consider the possible emergence of cognitive abilities through the maturation of brain structures and the effects of experience on the organization of functions in the brain. Developmental anomalies, such as autism and attention deficit disorder are also examined as windows on normal development.
Four questions drive the volume:
*Why do cognitive abilities emerge when they do during development?
*What are the sources of developmental and individual differences, and of developmental anomalies in learning?
*What happens in the brain when people learn?
*How can experiences be ordered and timed to optimize learning?
The answers to these questions have strong implications for how we educate children and remediate deficits that have impeded the development of thinking abilities. These implications are explored in several chapters in the volume, as well as in the commentaries by leading discussants.

This volume considers how children's thinking evolves during development, with a focus on the role of experience in causing change. It brings together cutting-edge research by leaders in the psychology and neurobiology of child development to examine the processes by which children learn and those that make children ready and able to learn at particular points in development.
Behavioral approaches include research on the "microgenesis" of cognitive change over short time periods (e.g., several hour-long sessions) in specific task situations. Research on cognitive change over longer time scales (months and years) is also presented, as well as research that uses computational modeling and dynamical systems approaches to understand learning and development.
Neural approaches include the study of how neuronal activity and connectivity change during acquisition of cognitive skills in children and adults. Other investigations consider the possible emergence of cognitive abilities through the maturation of brain structures and the effects of experience on the organization of functions in the brain. Developmental anomalies, such as autism and attention deficit disorder are also examined as windows on normal development.
Four questions drive the volume:
*Why do cognitive abilities emerge when they do during development?
*What are the sources of developmental and individual differences, and of developmental anomalies in learning?
*What happens in the brain when people learn?
*How can experiences be ordered and timed to optimize learning?
The answers to these questions have strong implications for how we educate children and remediate deficits that have impeded the development of thinking abilities. These implications are explored in several chapters in the volume, as well as in the commentaries by leading discussants.