Newell introduces Soar, an architecture for general cognition. A pioneer system in AI, Soar is the first problem-solver to create its own subgoals and learn continuously from its own experience. Its ability to operate within the real-time constraints of intelligent behavior illustrates important characteristics of human cognition.

It is argued in this book that there are three major approaches to anxiety. First, there is anxiety as an emotional state. Second, there is trait anxiety as a dimension of personality. Third, there is anxiety as a set of anxiety disorders. What is attempted is to produce a unified theory of anxiety which integrates all these major approaches. According to this unified theory, there are four sources of information which influence the level of experienced anxiety: (1) experimental stimulation; (2) internal physiological activity; (3) internal cognitions, (e.g., worries); and (4) one's own behaviour. The unified theory is essentially based on a cognitive approach. More specifically, it is assumed that individual differences in experienced anxiety between those high and low in trait anxiety depend largely on cognitive biases. It is also assumed that the various anxiety disorders depend on cognitive biases, and that the main anxiety disorders differ in terms of the source of information most affected by such biases (e.g., social phobics have biased interpretation of their own behaviour). In sum, this book presents a general theory of anxiety from the cognitive perspective. It is intended that this theory will influence theory and research on emotion, personality, and the anxiety disorders. Correction notice: Christos Halkiopoulos should have been credited for his role as the inventor of the Dot Probe Paradigm and for the design and execution of the experiment discussed in C. D. Spielberger, I. G. Sarason, Z. Kulczar, and J. Van Heck (Eds.), Stress and Emotion, Vol. 14. London: Hemisphere.

Soar: A Cognitive Architecture in Perspective represents a European perspective on Soar with the exception of the special contribution from Allen Newell arguing for Unified Theories of Cognition. The various papers derive from the work of the Soar Research Group that has been active at the University of Groningen, The Netherlands, since 1987. The work reported here has been inspired in particular by two topics that precipitated the group's interest in Soar in the first place -- road user behavior and the temporal organization of behavior, more specifically planning. At the same time, the various contributions go well beyond the simple use of Soar as a convenient medium for modeling human cognitive activity. In every paper one or more fundamental issues are raised that touch upon the very nature and consistency of Soar as an intelligent architecture. As a result the reader will learn about the operator implementation problem, chunking, multitasking, the need to constrain the depth of the goal stack, and induction, etc. Soar is still at a relatively early stage of development. It does, nevertheless, constitute an important breakthrough in the area of computer architectures for general intelligence. Soar shows one important direction that future efforts to build intelligent systems should take if they aim for a comprehensive, and psychologically meaningful, theory of cognition. This is argued in a powerful way by Newell in his contribution to this volume. For this reason, the Soar system will probably play an important integrative role within cognitive science in bringing together important subdomains of psychology, computer science, linguistics, and the neurosciences. Although Soar is not the only `architecture for intelligence', it is one of the most advanced and theoretically best motivated architectures presently available. Soar: A Cognitive Architecture in Perspective is of special interest to researchers in the domains of cognitive science, computer science and artificial intelligence, cognitive psychology, and the philosophy of mind.

Cognitive Neuroscience and Psychotherapy provides a bionetwork theory unifying empirical evidence in cognitive neuroscience and psychopathology to explain how emotion, learning, and reinforcement affect personality and its extremes. The book uses the theory to explain research results in both disciplines and to predict future findings, as well as to suggest what the theory and evidence say about how we should be treating disorders for maximum effectiveness. While theoretical in nature, the book has practical applications, and takes a mathematical approach to proving its own theorems. The book is unapologetically physical in nature, describing everything we think and feel by way of physical mechanisms and reactions in the brain. This unique marrying of cognitive neuroscience and clinical psychology provides an opportunity to better understand both. - Unifying theory for cognitive neuroscience and clinical psychology - Describes the brain in physical terms via mechanistic processes - Systematically uses the theory to explain empirical evidence in both disciplines - Theory has practical applications for psychotherapy - Ancillary material may be found at: http://booksite.elsevier.com/9780124200715 including an additional chapter and supplements

Concern about psychology’s fragmentation is not new, but there has for the past decade been increasing calls for psychologists to acknowledge to the costs associated with fragmentation and to search for ways to unify the discipline. A New Unified Theory of Psychology introduces a new system that addresses psychology’s current theoretical and philosophical difficulties. The new theory consists of four interlocking pieces that together provide—for the first time—a macro-level view that clarifies the nature of psychology’s problems and offers a clear way to unify the various elements of the field. The unified theory provides the field of psychology with a well-defined subject matter, allowing both academic and professional psychologists will be able to develop a shared language and conceptual foundation.

This volume offers an overview of the philosophy of cognitive science that balances breadth and depth, with chapters covering every aspect of the psychology and cognitive anthropology.

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

How to Build a Theory in Cognitive Science specifies the characteristics of fruitful interdisciplinary theories in cognitive science and shows how they differ from the successful theories in the individual disciplines composing the cognitive sciences. It articulates a method for integrating the various disciplines successfully so that unified, truly interdisciplinary theories are possible. This book makes three contributions of utmost importance. First, it provides a long overdue, systematic examination of the field of cognitive science itself. Second, it provides a template for linking domains without loss of autonomy. This philosophical treatment of integration serves as a blueprint for future endeavors. Third, the book provides a solid theoretical foundation that will prevent future missteps and enhance collaboration.

This book achieves a goal that was set 25 years ago when the HAM theory of human memory was published. This theory reflected one of a number of then-current efforts to create a theory of human cognition that met the twin goals of precision and complexity. Up until then the standard for precision had been the mathematical theories of the 1950s and 1960s. These theories took the form of precise models of specific experiments along with some informal, verbally-stated understanding of how they could be extended to new experiments. They seemed to fall far short of capturing the breadth and power of human cognition that was being demonstrated by the new experimental work in human cognition. The next 10 years saw two major efforts to address the problems of scope. In 1976, the ACT theory was first described and included a production rule system of procedural memory to complement HAM's declarative memory. This provided a computationally adequate system which was indeed capable of accounting for all sorts of cognition. In 1993, a new version of ACT--ACT-R--was published. This was an effort to summarize the theoretical progress made on skill acquisition in the intervening 10 years and to tune the subsymbolic level of ACT-R with the insights of the rational analysis of cognition. Although the appearance of generally-available, full-function code set off a series of events which was hardly planned, it resulted in this book. The catalyst for this was the emergence of a user community. Lebiere insisted that assembling a critical mass of users was essential to the ultimate success of the theory and that a physical gathering was the only way to achieve that goal. This resulted in the First Annual ACT-R Summer School and Workshop, held in 1994. In writing the book, the authors became seized by an aspiration that went beyond just describing the theory correctly. They decided to try to display what the theory could do by collecting together and describing some of its in-house applications. This book reflects decades of work in ACT-R accumulated by many researchers. The chapters are authored by the people that did that particular work. No doubt the reader will be impressed by the scope of the research and the quality of the individual work. Less apparent, but no less important, was the effort that everyone put into achieving the overall consistency and technical integrity of the book. This is the first work in cognitive science to precisely model such a wide range of phenomena with a single theory.

A mechanistic theory of the representation and use of semantic knowledge that uses distributed connectionist networks as a starting point for a psychological theory of semantic cognition.