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This paper proposes a new theory to account for the effects of underload on performance. Malleable attentional resources theory posits that attentional capacity can change size in response to changes in task demands. As such, the performance decrements associated with mental underload can be explained by a lack of appropriate attentional resources. These proposals were explored in a driving simulator experiment. Vehicle automation was manipulated at 4 levels, and mental workload was assessed with a secondary task. Eye movements were also recorded to determine whether attentional capacity varied with mental workload. The results showed a clear decrease in mental workload associated with some levels of automation. Most striking, though, were the results derived from the eye movement recordings, which demonstrated that attentional capacity varies directly with level of mental workload. These data fully supported the predictions of the new theory. Malleable attentional resources theory suggests that future vehicle designers should employ their technology in driver support systems rather than in automation to replace the driver. The implications of this theory are discussed with regard to capacity models of attention as well as to the design of future vehicle systems.

Cover -- Half Title -- The Human Factors of Simulation and Assessment -- Series Editors -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgements -- Editors -- Contributors -- Section I : Introduction -- Chapter 1: Prologue -- 1.1 Advance of Transport Simulators -- 1.2 What Is a Simulator? -- 1.3 Simulator Fidelity and Validity -- 1.4 Benefits of Simulators -- 1.5 Current Trends in Transport Simulators -- 1.6 Structure of This Book -- References -- Section II : Road -- Chapter 2: Driving Simulators for Research -- 2.1 Introduction -- 2.2 Theoretical Considerations for Simulated Driving Studies -- 2.2.1 Reliability -- 2.2.2 Validity -- 2.2.3 Fidelity -- 2.2.4 Presence -- 2.3 Practical Considerations for the Development and Running of a Driving Simulator -- 2.3.1 Safety -- 2.3.2 Simulator Sickness -- 2.3.3 Control -- 2.3.4 Cost -- 2.4 Building and Using a Driving Simulator for Research -- 2.4.1 Building a Simulator -- 2.4.2 Using a Simulator -- 2.4.2.1 Participant Selection -- 2.4.2.2 Incentives for Participants -- 2.4.2.3 Information and Consent -- 2.4.2.4 Familiarisation and Training with the Simulator -- 2.4.2.5 Sickness Prediction, Monitoring and Management -- 2.5 Conclusions -- References -- Chapter 3: Driver Training -- 3.1 Introduction -- 3.2 Types of Simulator Technology for Driver Training -- 3.3 Commercial Driver Training -- 3.4 Fidelity and Validity -- 3.5 Development of a Truck Driving Simulator -- 3.6 Transfer of Training -- 3.7 Simulator Sickness -- 3.8 Conclusion -- References -- Chapter 4: Motorcycle Simulator Solutions for Rider Research -- 4.1 Motorcyclists as Vulnerable Road Users -- 4.2 Human Factors and Research Requirements for Motorcycle Simulation -- 4.3 Review of Motorcycle Simulation Research -- 4.4 Evolution of Motorcycle Simulators -- 4.4.1 Comparisons of Motorcycle Simulators.

1. Introduction -- 2. Modelling a dynamic world -- 3. A complex approach to a complex scenario -- 4. Development of a study -- 5. Extending the potential of information networks : a Bayesian approach -- 6. Can we validate networks derived from incident data through simulation? A pilot study -- 7. Incidents versus accidents : an industrial study -- 8. Conclusion.