Description
Human Performance Models for Computer-Aided Engineering is a collection of papers that deals with the relationship between scientific theories of human performance and practical engineering. This collection describes the emergence of a scientific engineering paradigm that uses computational theories in computational design aids. This book also considers computational human factors such as human performance models and their application in computer-based engineering designs. This text then presents applications of these models to some helicopter flight problems. This book also explains the four requirements in programming a computer-based model of the sensory performance of a pilot as 1) prediction capability; 2) measurement capability; 3) provision of compatible computer algorithms; and 4) image driven. This collection also describes cognitive structures—aspects of the human information processing system. This text then discusses resource management and time-sharing issues that is related to competition of scarce resources, which can be predictive of the quality of information processing. This book also describes other modeling scenarios such as those predicting human errors, decision making, and shape modeling. This text can prove valuable for computer programmers, engineers, physicists, and research scientists dealing with psychophysics.
Table of Contents
ForewordPrefacePart I 1 Introduction Helicopter Flight Problems and Applications of Human Performance Models Detectability and Visibility Surface and Motion Estimation Object Recognition Hetero-Ocular Vision Workload and Pilot Performance Decision Theory Memory Overload Skill Acquisition Human Error References 2 Preview of Models Framework Assessment of Models 3 Use and Integration of Models Design Process Toolbox Framework Selecting Tools and Models Engineering Analyses Discussion Afterword ReferencesPart II 4 Introduction to Vision Models 5 Models in Early Vision Overview Introduction What is a Model? Model Attributes Spatial Vision Temporal Sensitivity Motion Processing Summary References 6 Models of Static Form Perception Image Generation Image Analysis Potential Applications References 7 Structure from Motion Overview Introduction Models Conclusion Research Needs: Structure from Motion References 8 Motion-Based State Estimation and Shape Modeling Introduction and Summary Framework for Motion-Based State Estimation and Shape Modeling Review of Research in Motion-Based State Estimation and Shape Modeling Model Applications and Limitations Future Research References 9 Real-Time Human Image Understanding in Pilot Performance Models Theories of Object Recognition Model-Based Matching: Lowe's SCERPO and UUman's Alignment Models Perception of Multiobject Displays References 10 Manipulation Of Visual Information Summary Introduction Transformations on Information Presented in a Static Visual Display Memory for Positions in a Sequence of Static Displays Extrapolation of Perceptually Driven Spatial Transformations Judgments of Object Structure from Partial Views Future Research References 11 Combining Views Integration of Successive Views Binocular Combination References 12 Afterword 13 Introduction to Cognition Models 14 Cognitive Architectures Symbolist Architectures Connectionist Models References 15 Resource Management and Time-Sharing Overview Serial Allocation Parallel Allocation Serial Competition Parallel Competition Synthesis of the Optimal Model Conclusion References 16 Models of Working Memory Phenomena of Working Memory Models of Working Memory References 17 Training Models to Estimate Training Costs for New Systems Overview Skill Development Models for Predicting Human Performance Engineering Guidance without an All-inclusive Model Use of Rapid Prototyping and Quick Empirical Evaluations Needed Research References 18 Modeling Scenarios for Action Fixed Scenarios Scenarios with Simple Contingencies Modeling More Complex Scenarios References 19 Modeling and Predicting Human Error Introduction Error Modeling References 20 Modeling Decision Making for System Design Why Decision Making Seems Easy to Model—Sometimes Implication for Modeling Operator Performance Modeling without Optimality Making Behavior More Model-like Testing the Limits of Decision Making References 21 Knowledge Elicitation and Representation Knowledge Elicitation Knowledge Representation Mental Models and Design De
