Modeling Human Behavior With Integrated Cognitive Architectures: Comparison, Evaluation, and Validation

by ;
Edition: CD
Format: Nonspecific Binding
Pub. Date: 2005-07-20
Publisher(s): Psychology Pres
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Summary

Resulting from the need for greater realism in models of human and organizational behavior in military simulations, there has been increased interest in research on integrative models of human performance, both within the cognitive science community generally, and within the defense and aerospace industries in particular. This book documents accomplishments and lessons learned in a multi-year project to examine the ability of a range of integrated cognitive modeling architectures to explain and predict human behavior in a common task environment that requires multi-tasking and concept learning. This unique project, called the Agent-Based Modeling and Behavior Representation (AMBR) Model Comparison, involved a series of human performance model evaluations in which the processes and performance levels of computational cognitive models were compared to each other and to human operators performing the identical tasks. In addition to quantitative data comparing the performance of the models and real human performance, the book also presents a qualitatively oriented discussion of the practical and scientific considerations that arise in the course of attempting this kind of model development and validation effort. The primary audiences for this book are people in academia, industry, and the military who are interested in explaining and predicting complex human behavior using computational cognitive modeling approaches. The book should be of particular interest to individuals in any sector working in Psychology, Cognitive Science, Artificial Intelligence, Industrial Engineering, System Engineering, Human Factors, Ergonomics and Operations Research. Any technically or scientifically oriented professional or student should find the material fully accessible without extensive mathematical background.

Table of Contents

Contributors xi
Preface xiii
Acknowledgments xv
PART I OVERVIEW, EXPERIMENTS, AND SOFTWARE
1 Background, Structure, and Preview of the Model Comparison
3(10)
Kevin A. Gluck, Richard W. Pew, and Michael J. Young
2 The AMBR Experiments: Methodology and Human Benchmark Results
13(32)
Yvette J. Tenney, David E. Diller, Stephen Deutsch, and Katherine Godfrey
3 The Simulation Environment for the AMBR Experiments
45(18)
Stephen Deutsch, David E. Diller, Brett Benyo, and Laura Feinerman
PART II MODELS OF MULTITASKING AND CATEGORY LEARNING
4 Constrained Functionality: Application of the ACT-R Cognitive Architecture to the AMBR Modeling Comparison
63(50)
Christian Lebiere
5 A COGNET/iGEN Cognitive Model That Mimics Human Performance and Learning in a Simulated Work Environment
113(64)
Wayne Zachary, Joan Ryder, James Stokes, Floyd Glenn, Jean-Christophe Le Mentec, and Thomas Santarelli
6 Distributed Cognition and Situated Behavior
177(60)
Robert G. Eggleston, Katherine L. McCreight, and Michael J. Young
7 Inheriting Constraint in Hybrid Cognitive Architectures: Applying the EASE Architecture to Performance and Learning in a Simplified Air Traffic Control Task
237(70)
Ronald S. Chong and Robert E. Wray
SECTION III CONCLUSIONS, LESSONS LEARNED, AND IMPLICATIONS
8 Comparison, Convergence, and Divergence in Models of Multitasking and Category Learning, and in the Architectures Used to Create Them
307(44)
David E. Diller, Kevin A. Gluck, Yvette J. Tenney, and Katherine Godfrey
9 In Vivo or In Vitro: Cognitive Architectures and Task-Specific Models
351(14)
Bradley C. Love
10 HBR Validation: Integrating Lessons Learned From Multiple Academic Disciplines, Applied Communities, and the AMBR Project
365(32)
Gwendolyn E. Campbell and Amy E. Bolton
11 Accomplishments, Challenges, and Future Directions for Human Behavior Representation
397(18)
Richard W. Pew, Kevin A. Gluck, and Stephen Deutsch
Author Index 415(6)
Subject Index 421

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