Modeling Human–System Interaction

Thomas B. Sheridan is Ford Professor Emeritus in the Aeronautics/Astronautics and Mechanical Engineering departments at the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. He directed a research laboratory on human-system interaction at MIT. He served as President of both the IEEE Systems, Man and Cybernetics Society and the Human Factors and Ergonomics Society. He is a member of the National Academy of Engineering and author of Humans and Automation (Wiley, 2002).

• Preface xiIntroduction 11 Knowledge 5Gaining New Knowledge 5Scientific Method: What Is It? 7Further Observations on the Scientific Method 8Reasoning Logically 10Public (Objective) and Private (Subjective) Knowledge 11The Role of Doubt in Doing Science 11Evidence: Its use and Avoidance 12Metaphysics and its Relation to Science 12Objectivity, Advocacy, and Bias 13Analogy and Metaphor 142 What is a Model? 17Defining “Model” 17Model Attributes: A New Taxonomy 20Examples of Models in Terms of the Attributes 25Why Make the Effort to Model? 27Attribute Considerations in Making Models Useful 27Social Choice 30What Models are Not 313 Important Distinctions in Modeling 33Objective and Subjective Models 33Simple and Complex Models 35Descriptive and Prescriptive (Normative) Models 36Static and Dynamic Models 36Deterministic and Probabilistic Models 36Hierarchy of Abstraction 37Some Philosophical Perspectives 384 Forms of Representation 41Verbal Models 41Graphs 42Maps 44Schematic Diagrams 45Logic Diagrams 46Crisp Versus Fuzzy Logic (see also Appendix, Section “Mathematics of Fuzzy Logic”) 48Symbolic Statements and Statistical Inference (see also Appendix, Section “Mathematics of Statistical Inference From Evidence”) 505 Acquiring Information 51Information Communication (see also Appendix, Section “Mathematics of Information Communication”) 51Information Value (see also Appendix, Section “Mathematics of Information Value”) 53Logarithmic‐Like Psychophysical Scales 54Perception Process (see also Appendix, Section “Mathematics of the Brunswik/Kirlik Perception Model”) 54Attention 55Visual Sampling (see also Appendix, Section “Mathematics of How Often to Sample”) 56Signal Detection (see also Appendix, Section “Mathematics of Signal Detection”) 58Situation Awareness 59Mental Workload (see also Appendix, Section “Research Questions Concerning Mental Workload”) 60Experiencing What is Virtual: New Demands for Human–System Modeling (see also Appendix Section “Behavior Research Issues in Virtual Reality”) 646 Analyzing the Information 69Task Analysis 69Judgment Calibration 70Valuation/Utility (see also Appendix, Section “Mathematics of Human Judgment of Utility”) 72Risk and Resilience 73Definition of Risk 73Meaning of Resilience 73Trust 757 Deciding on Action 77What is Achievable 77Decision Under Condition of Certainty (see also Appendix, Section “Mathematics of Decisions Under Certainty”) 78Decision Under Condition of Uncertainty (see also Appendix, Section “Mathematics of Decisions Under Uncertainty”) 79Competitive Decisions: Game Models (see also Appendix “Mathematics of Game Models”) 79Order of Subtask Execution 808 Implementing and Evaluating the Action 83Time to Make a Selection 83Time to Make an Accurate Movement 84Continuous Feedback Control (see also Appendix, Section “Mathematics of Continuous Feedback Control”) 85Looking Ahead (Preview Control) (see also Appendix, Section “Mathematics of Preview Control”) 87Delayed Feedback 88Control by Continuously Updating an Internal Model (see also Appendix, Section “Stepping Through the Kalman Filter System”) 88Expectation of Team Response Time 90Human Error 919 Human–Automation Interaction 95Human–Automation Allocation 95Supervisory Control 96Trading and Sharing 98Adaptive/Adaptable Control 101Model‐Based Failure Detection 10210 Mental Models 105What is a Mental Model? 105Background of Research on Mental Models 106ACT‐R 108Lattice Characterization of a Mental Model 110Neuronal Packet Network as a Model of Understanding 112Modeling of Aircraft Pilot Decision‐Making Under Time Stress 113Mutual Compatibility of Mental, Display, Control, and Computer Models 11411 Can Cognitive Engineering Modeling Contribute to Modeling Large‐Scale Socio‐Technical Systems? 115Basic Questions 115What Large‐Scale Social Systems are we Talking About? 116What Models? 120Potential of Feedback Control Modeling of Large‐Scale Societal Systems 122The STAMP Model for Assessing Errors in Large‐Scale Systems 122Past World Modeling Efforts 123Toward Broader Participation 124Appendix 129Mathematics of Fuzzy Logic 129Mathematics of Statistical Inference from Evidence 131Mathematics of Information Communication 132Mathematics of Information Value 134Mathematics of the Brunswik/Kirlik Perception Model 135Mathematics of How Often to Sample 136Mathematics of Signal Detection 138Research Questions Concerning Mental Workload 141Behavior Research Issues in Virtual Reality 144Mathematics of Human Judgment of Utility 146Mathematics of Decisions Under Certainty 147Mathematics of Decisions Under Uncertainty 149Mathematics of Game Models 150Mathematics of Continuous Feedback Control 152Mathematics of Preview Control 153Stepping Through the Kalman Filter System 154References 159Index 167