When we think about expertise, we usually consider people who master tasks at a level not reachable by most other people. Although we rarely realise it, however, most humans are experts in many aspects of everyday life. This expertise enables us to find our way through a complex environment that is our life. For instance, we can instantly recognise multiple objects and relations between them to form a meaningful unit, such as an office. Thus, research on expertise is not only important to investigate the cognitive and neural processes within an “elite” group, but it is also a powerful tool to understand how everyone can acquire complex skills. The goal of this Research Topic is to shed further light on the common and distinct neural mechanisms that implement various kinds of expertise. We broadly define expertise as skill in any perceptual, cognitive, social or motor domain, with the common core being optimised information processing due to knowledge acquired from repeated experiences. Thus, we are interested in the full range of mental processes modulated or modified by expertise, from “simple” object or pattern recognition to complex decision making or problem solving in a particular domain. These domains can range from everyday or occupational expertise to sports and rather artificial domains such as board games. In all cases, the aim should be to elucidate how the brain implements these sometimes incredible feats. We are particularly interested in connecting cognitive theories about expertise and expertise-related performance differences with models and data on the neural implementation of expertise. We welcome original research contributions using the full range of behavioural neuroscience methods, as well as theoretical, methodological or historical reviews, and opinion papers focusing on any of the above-mentioned aspects.

The Neuroscience of Expertise examines the ways in which the brain accommodates the incredible feats of experts. It builds on a tradition of cognitive research to explain how the processes of perception, attention, and memory come together to enable experts' outstanding performance. The text explains how the brain adapts to enable the complex cognitive machinery behind expertise, and provides a unifying framework to illuminate the seemingly unconnected performance of experts in different domains. Whether it is a radiologist who must spot a pathology in a split second, a chess grandmaster who finds the right path in a jungle of possible continuations, or a tennis professional who reacts impossibly quickly to return a serve, The Neuroscience of Expertise offers insight into the universal cognitive and neural mechanisms behind these achievements.

Offering the broadest review of psychological perspectives on human expertise to date, this volume covers behavioral, computational, neural, and genetic approaches to understanding complex skill. The chapters show how performance in music, the arts, sports, games, medicine, and other domains reflects basic traits such as personality and intelligence, as well as knowledge and skills acquired through training. In doing so, this book moves the field of expertise beyond the duality of "nature vs. nurture" toward an integrative understanding of complex skill. This book is an invaluable resource for researchers and students interested in expertise, and for professionals seeking current reviews of psychological research on expertise.

In this book, some of the world's foremost 'experts on expertise' provide scientific knowledge on expertise and expert performance.

The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation. Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies. - Fuzzy system techniques applied to neural networks for modeling and control - Systematic design procedures for realizing fuzzy neural systems - Techniques for the design of rule-based expert systems - Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets - System identification and control - Nonparametric, nonlinear estimation Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies

This book presents the Proceedings of the Tenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, focusing on the theoretical aspects of intelligent systems research as well as extensions of theory of intelligent thinking machines.

The Handbook of Applied Expert Systems is a landmark work dedicated solely to this rapidly advancing area of study. Edited by Jay Liebowitz, a professor, author, and consultant known around the world for his work in the field, this authoritative source covers the latest expert system technologies, applications, methodologies, and practices. The book features contributions from more than 40 of the world's foremost expert systems authorities in industry, government, and academia. The Handbook is organized into two major sections. The first section explains expert systems technologies while the second section focuses on applied examples in a wide variety of industries. Key topics covered include fuzzy systems, genetic algorithm development, machine learning, knowledge representation, and much more.

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