Computational Cognitive Models in Adaptive Automation: Supporting Operator Learning and Skill Retention

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Automation aids are becoming increasingly prevalent in the workplace and everyday life. For example, autopilot systems can fly planes; autonomous cars are expected to hit the market within 10 years. A profound safety concern of automation use is that human operators can become less actively involved and gradually lose skill proficiency. In the Air France Flight 447 accident that killed 228 people onboard, a major causing factor is "the absence of any training, at high altitude, in manual aeroplane handling", due to extensive use of autopilot (AF 447 Final Report, 2012). To improve the safety and reliability of human-automation systems, I will develop adaptive automation that uses computational cognitive models to predict operators' skill retention rates and mental workload. The resulting adaptive automation will be able to adjust the level of automated aid in a "smart" way, providing operators with opportunities to practice their skills only when their workloads are not too high.*** Focusing on the transportation domain, this research program will investigate three scenarios: indoor navigation, driving, and air traffic control. They are selected to comprehensively examine the cognitive modeling approach to adaptive automation. In each scenario, I will conduct experimental investigation, model development, and adaptive automation design and test. The results will have both practical and theoretical value.*** The resulting methods and prototypes are expected to generate intellectual property and be transferred to industry for the production of smarter and safer automation systems. *** The cognitive modeling approach will allow early design evaluation of human-automation systems in simulation environments, reducing the cost and risk of human test. *** The modeling theory and mechanisms developed in this program will advance the theories of unified cognition and human performance modeling.*** The results will also inform and benefit applications in other domains such as education, healthcare, law enforcement, and entertainment, where human performance and user experience are among the most important concerns. *** This program will train HQP (4 Master's and 1 PhD) for the future transportation sector in Canada. They will master cognitive architecture modeling, systems integration, automation reliability test, and user experiment skills trained in this multidisciplinary research program. These skills enable them to quantitatively analyze and predict human-automation system performance, and the skills will be essential for their future innovative research and design work transforming the transportation sector. The knowledge gained in this program is expected to be published in top-tier journals and taught in class to prepare the Canadian workforce in the competitive global economy.**