Psychophysiological measures can be used to monitor cognitive workload. Since they can be recorded without interrupting task performance and can be analyzed in real time, they are useful in providing estimates of an operator's functional state. These estimates could be used to determine if and when system intervention should be provided to assist the operator. This paper investigates if psychophysiologically-driven real-time adaptive aiding significantly enhances performance in a complex aviation task. A second goal was to assess the importance of individual operator capabilities when providing adaptive aiding. In the study, adaptive automation was implemented while operators performed an uninhabited aerial vehicle task. Psychophysiological data were collected and an artificial neural network was used to detect periods of high and low mental workload in real time. The high-difficulty task levels used to initiate the adaptive automation were determined separately for each operator, and a group-derived mean difficulty level was also used. Results showed that psychophysiologically-determined aiding significantly improved performance when compared with the no-aiding conditions or when aiding was randomly provided. Improvement also was greater when adaptive aiding was provided based on individualized criteria rather than on group-derived criteria.
Abstract