Ron Sun – författare

This book aims to understand human cognition and psychology through a comprehensive computational theory of the human mind, namely, a computational "cognitive architecture" (or more specifically, the CLARION cognitive architecture). The goal of this work is to develop a unified framework for understanding the human mind, and within the unified framework, to develop process-based, mechanistic explanations of a large variety of psychological phenomena. Specifically, the book first describes the essential CLARION framework and its cognitive-psychological justifications, then its computational instantiations, and finally its applications to capturing, simulating, and explaining various psychological phenomena and empirical data. The book shows how the models and simulations shed light on psychological mechanisms and processes through the lens of a unified framework.In fields ranging from cognitive science, to psychology, to artificial intelligence, and even to philosophy, researchers, graduate and undergraduate students, and practitioners of various kinds may have interest in topics covered by this book. The book may also be suitable for seminars or courses, at graduate or undergraduate levels, on cognitive architectures or cognitive modeling (i.e. computational psychology).

This book is a condensation of a large body of work concerning human learning carried out over a period of more than five years by Dr. Sun and his collaborators. In a nutshell, this work is concerned with a broad framework for studying human cognition based on a new approach that is characterized by its focus on the dichotomy of, and the interaction between, explicit and implicit cognition and a computational model that implements this framework. In this work, a broad, generic computational model was developed that instantiates Dr. Sun's framework and enables the testing of his theoretical approach in a variety of ways. With this model, simulation results were matched with data of human cognition in a variety of different domains. Formal (mathematical and computational) analyses were also carried out to further explore the model and its numerous implementational details. Furthermore, this book addresses some of the most significant theoretical issues, such as symbol grounding, intentionality, social cognition, consciousness, and other theoretical issues in relation to the framework. The general framework and the model developed generate interesting insights into these theoretical issues.

This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Given the complexity of the human mind and its manifestation in behavioral flexibility, process-based computational models may be necessary to explicate and elucidate the intricate details of the mind. The key to understanding cognitive processes is often in fine details. Computational models provide algorithmic specificity: detailed, exactly specified, and carefully thought-out steps, arranged in precise yet flexible sequences. These models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many examples.

This book explores the intersection between cognitive sciences and social sciences. In particular, it explores the intersection between individual cognitive modeling and modeling of multi-agent interaction (social stimulation). The two contributing fields - individual cognitive modeling (especially cognitive architectures) and modeling of multi-agent interaction (including social simulation and, to some extent, multi-agent systems) - have seen phenomenal growth in recent years. However, the interaction of these two fields has not been sufficiently developed. We believe that the interaction of the two may be more significant than either alone. They bring with them enormous intellectual capitals. These intellectual capitals can be profitably leveraged in creating true synergy between the two fields, leading to more in-depth studies and better understanding of both individual cognition and sociocultural processes. It is possible that an integrative field of study in cognitive and social sciences is emerging and we are laying the foundation for it.

This book explores the intersection between cognitive sciences and social sciences. In particular, it explores the intersection between individual cognitive modeling and modeling of multi-agent interaction (social stimulation). The two contributing fields - individual cognitive modeling (especially cognitive architectures) and modeling of multi-agent interaction (including social simulation and, to some extent, multi-agent systems) - have seen phenomenal growth in recent years. However, the interaction of these two fields has not been sufficiently developed. We believe that the interaction of the two may be more significant than either alone. They bring with them enormous intellectual capitals. These intellectual capitals can be profitably leveraged in creating true synergy between the two fields, leading to more in-depth studies and better understanding of both individual cognition and sociocultural processes. It is possible that an integrative field of study in cognitive and social sciences is emerging and we are laying the foundation for it.

This text focuses on a currently emerging body of research. With the re-emergence of neural networks in the 1980s with their emphasis on overcoming some of the limitations of symbolic AI, there is clearly a need to support some form of high-level symbolic processing in connectionist networks. As argued by many researchers, on both the symbolic AI and connectionist sides, many cognitive tasks, e.g. language understanding and common sense reasoning, seem to require high-level symbolic capabilities. How these capabilities are realized in connectionist networks is a difficult question and it constitutes the focus of this book. It addresses the underlying architectural aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent approaches and provide insight for further developments in this field, this book presents: (1) an examination of specific architectures (grouped together according to their approaches), their strengths and weaknesses, why they work, and what they predict, and (2) a critique/comparison of these approaches.This text should be of interest to researchers, graduate students, and interested laymen, in areas such as cognitive science, artificial intelligence, computer science, cognitive psychology, and neurocomputing, in keeping up-to-date with the newest research trends. It should provide a comprehensive introduction to this new emerging field.

A variety of ideas, approaches, and techniques exist -- in terms of both architecture and learning -- and this abundance seems to lead to many exciting possibilities in terms of theoretical advances and application potentials. Despite the apparent diversity, there is clearly an underlying unifying theme: architectures that bring together symbolic and connectionist models to achieve a synthesis and synergy of the two different paradigms, and the learning and knowledge acquisition methods for developing such architectures. More effort needs to be extended to exploit the possibilities and opportunities in this area. This book is the outgrowth of The IJCAI Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, held in conjunction with the fourteenth International Joint Conference on Artificial Intelligence (IJCAI '95). Featuring various presentations and discussions, this two-day workshop brought to light many new ideas, controversies, and syntheses which lead to the present volume. This book is concerned with the development, analysis, and application of hybrid connectionist-symbolic models in artificial intelligence and cognitive science. Drawing contributions from a large international group of experts, it describes and compares a variety of models in this area. The types of models discussed cover a wide range of the evolving spectrum of hybrid models, thus serving as a well-balanced progress report on the state of the art. As such, this volume provides an information clearinghouse for various proposed approaches and models that share the common belief that connectionist and symbolic models can be usefully combined and integrated, and such integration may lead to significant advances in understanding intelligence.

This book is a condensation of a large body of work concerning human learning carried out over a period of more than five years by Dr. Sun and his collaborators. In a nutshell, this work is concerned with a broad framework for studying human cognition based on a new approach that is characterized by its focus on the dichotomy of, and the interaction between, explicit and implicit cognition and a computational model that implements this framework. In this work, a broad, generic computational model was developed that instantiates Dr. Sun's framework and enables the testing of his theoretical approach in a variety of ways. With this model, simulation results were matched with data of human cognition in a variety of different domains. Formal (mathematical and computational) analyses were also carried out to further explore the model and its numerous implementational details. Furthermore, this book addresses some of the most significant theoretical issues, such as symbol grounding, intentionality, social cognition, consciousness, and other theoretical issues in relation to the framework. The general framework and the model developed generate interesting insights into these theoretical issues.

The Cambridge Handbook of Computational Cognitive Sciences is a comprehensive reference for this rapidly developing and highly interdisciplinary field. Written with both newcomers and experts in mind, it provides an accessible introduction of paradigms, methodologies, approaches, and models, with ample detail and illustrated by examples. It should appeal to researchers and students working within the computational cognitive sciences, as well as those working in adjacent fields including philosophy, psychology, linguistics, anthropology, education, neuroscience, artificial intelligence, computer science, and more.

Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art focuses on a currently emerging body of research. With the reemergence of neural networks in the 1980s with their emphasis on overcoming some of the limitations of symbolic AI, there is clearly a need to support some form of high-level symbolic processing in connectionist networks. As argued by many researchers, on both the symbolic AI and connectionist sides, many cognitive tasks, e.g. language understanding and common sense reasoning, seem to require high-level symbolic capabilities. How these capabilities are realized in connectionist networks is a difficult question and it constitutes the focus of this book. Computational Architectures Integrating Neural and Symbolic Processes addresses the underlying architectural aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent approaches and provide insight for further developments in this field, this book presents: (1) an examination of specific architectures (grouped together according to their approaches), their strengths and weaknesses, why they work, and what they predict, and (2) a critique/comparison of these approaches. Computational Architectures Integrating Neural and Symbolic Processes is of interest to researchers, graduate students, and interested laymen, in areas such as cognitive science, artificial intelligence, computer science, cognitive psychology, and neurocomputing, in keeping up-to-date with the newest research trends. It is a comprehensive, in-depth introduction to this new emerging field.

Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.

Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems.The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches.

This volume contains the papers selected for presentation at The 2010 Int- national Conference on Brain Informatics (BI 2010) held at York University, Toronto, Canada, during August 28-30, 2010. It was organized by the Web - telligenceConsortium(WIC),theIEEEComputationalIntelligenceSocietyTask Force on Brain Informatics (IEEE-CIS TF-BI), and York University. The c- ference washeld jointly with the 2010InternationalConference on Active Media Technology (AMT 2010). Brain informatics (BI) hasemergedasaninterdisciplinaryresearch?eld that focuses on studying the mechanisms underlying the human information proce- ing system (HIPS). It investigates the essential functions of the brain, ranging from perception to thinking, and encompassing such areas as multi-perception, attention, memory, language, computation, heuristic search, reasoning, pl- ning, decision-making, problem-solving, learning, discovery, and creativity.The goal of BI is to develop and demonstrate a systematic approach to achieving an integrated understanding of both macroscopic and microscopic-level working principles of the brain, by means of experimental, computational, and cognitive neuroscience studies, as well as utilizing advanced Web intelligence (WI)-centric information technologies. BI represents a potentially revolutionary shift in the way that research is undertaken. It attempts to capture new forms of colla- rative and interdisciplinary work. In this vision, new kinds of BI methods and global research communities will emerge, through infrastructure on the wisdom Web and knowledge grids that enable high-speed and distributed, large-scale analysis and computations, and radically new ways of sharing data/knowledge. TheBrainInformaticsConferencesstartedwiththeFirstWICIInternational Workshop on Web Intelligence meets Brain Informatics (WImBI 2006), held at Beijing,China,December15-16,2006.