The Oxford Handbook of Cognitive Engineering

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  • Format: Hardcover
  • Copyright: 2013-02-12
  • Publisher: Oxford University Press

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Cognitive engineering is an interdisciplinary approach to the analysis, modeling, and design of engineered systems or workplaces in which humans and technologies jointly operate to achieve system goals. As individuals, teams, and organizations become increasingly reliant on information technology and automation, it is more important than ever for system and workplace design to be maximally informed by state-of-the-art cognitive engineering research. This volume is the first authoritative handbook to cover this recent and rapidly growing field. The handbook collects and organizes contemporary cognitive engineering research, drawing on the original research of more than 60 contributing experts. Coverage of human factors, human-computer interaction, and the conceptual foundations of cognitive engineering is extensive, addressing not only cognitive engineering in broader organizations and communities, but also focusing on individual cognition, addressing topics of attention, decision making, and multi-tasking. This thorough approach speaks to the broad scope of cognitive engineering, spanning the individual operator to teams and organizations, with a focus on how systems of people and technology, often in the form of automation, influences performance. By collecting the best of cognitive engineering research in one volume, this book serves as both a convenient reference guide and as a useful entry point to the large and diverse research literature. As such, this handbook will be a valuable resource for researchers, students, and practitioners in cognitive engineering and a variety of related fields in need of guidance for how to put their products, systems, and services into the hands of human users, performers, and customers.

Author Biography

John D. Lee, Ph.D., is the Emerson Electric professor in the Department of Industrial and Systems Engineering at the University of Wisconsin, Madison and is Director of the Cognitive Systems Laboratory.

Alex Kirlik, Ph.D., is a professor in the Departments of Computer Science, Psychology, and Systems Engineering at the University of Illinois at Urbana-Champaign, where he is also member of the Beckman Institute for Advanced Science and Technology.

Table of Contents

Cognitive Engineering: History and Foundations
Introduction to the Handbook
Cognition in Engineered Systems
The Closed-Loop Dynamics of Cognitive Work
Judgment and Prediction
Situation Awareness
Trust, Reliance, and Compliance
Learning and Retention
Neuroergonomics: Brain-inspired Cognitive Engineering
Communication in Socio-Technology Systems
Team Cognition: Coordination across Individuals and Machines
Organizational Design and Cognitive Work
Cognitive Engineering Methods
Cognitive Task Analysis
Cognitive Work Analysis
Decision-Centered Design
Situation Awareness Oriented Design
Cognitive Engineering to Support Successful Aging
Artifact Analysis as a Way to Understand Cognition
Evaluation: Does the Cognitive Engineering Effort Do What It Was Envisioned to Do?
Microworld Experimentation with Teams
Simulation to Assess Human Responses to Critical Events
Simulation to Assess Safety in Complex Work Environments
Metrics for Supervisory Control System Evaluation
Multi-tasking and Multi-Robot Management
Human-Machine Cooperation
Learning from Failure
Cognitive Engineering Models
Computational Cognitive Modeling of Interactive Performance
Computational Process Modeling and Cognitive Stressors: Background and Prospects for Application in Cognitive Engineering
Modeling and Formal Analysis of Human-Machine Interaction
Queuing and Network Models
Bayesian and Signal Detection Models
Judgment Analysis
Modeling Decision Heuristics
Establishing the Micro-to-Macro Link in Cognitive Engineering: Multilevel Models of Socio-Computer Interaction
Cognitive Technologies in Engineered Systems
Configural and Pictorial Displays
Emergence in Organizations and Human Collective Intelligence
Multimodal Displays: Conceptual Basis, Design Guidance, and Research Needs
Ecological Interfaces
Uncertainty Visualization and Related Techniques
Adaptive Automation
Distributed Communities of Practice
Table of Contents provided by Publisher. All Rights Reserved.

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