A computational approach to situation awareness and mental models in aviation
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Although most modern, highly-computerized flight decks are known to be robust to small disturbances and failures, humans still play a crucial role in advanced decision making in off-nominal situations, and accidents still occur because of poor human-automation interaction. In addition to the physical state of the environment, operators now have to extend their awareness to the state of the automated flight systems. To guarantee the accuracy of this knowledge, humans need to know the dynamics or approximate versions of the dynamics that rule the automation. The operator's situation awareness can decline because of a deficient mental model of the aircraft and an excessive workload. This work describes the creation of a computational human agent model simulating cognitive constructs such as situation awareness and mental models known to capture the symptoms of poor human-automation interaction and provide insight into more comprehensive metrics supporting the validation of automated systems in aviation.