Cognitive Modeling for Closed-Loop Task Mitigation

As flightdeck equipment becomes more sophisticated and complex, operations become significantly more cognitively demanding. When tasks demands exceed the operator's available cognitive resources, potentially costly errors occur. A task mitigation system that is able to monitor the task and the operator's functional state (OFS) and implement task mitigation strategies before an operator becomes overloaded could significantly reduce errors and allow operators to work more efficiently. This proposal describes the development of a closed-loop task mitigation system that uses advanced regression techniques to identify the relationships between the OFS, the physiological measurements, the mission-related context, and the task mitigation strategies. To maximize accuracy, we use task analysis to develop a computational cognitive model of the planned mission profile, which is then used to train the regression model. The computational cognitive model describes the OFS as a continuous function along four dimensions: executive function, spatial working memory, verbal working memory and attention. The task analysis is also used to develop task mitigation strategies for each psychological dimension that assist the operator with task switching, maintaining awareness of multiple task "threads", and performing cognitively demanding tasks. Finally, the task mitigation strategies enable the system to dynamically allocate tasks among multiple operators.