AFRL-HE-WP-SR-2000-0010 UNITED STATES AIR FORCE RESEARCH LABORATORY MODELING MENTAL WORKLOAD

The primary objective of this research project was to investigate models for monitoring and predicting subjective workload in the control of complex systems. Such models would enable systems to use workload levels to distribute tasks optimally in addition to identifying levels of workload which could lead to a serious breakdown in performance. In the aircraft-pilot system, for example, such capabilities could provide warnings to the pilot of high workload levels and could also assess ways of reducing the pilot's workload by offering to assume control of some ongoing tasks. In this initial project, we tried to determine how well a model can assess workload using information about task requirements and task performance. Participants rated subjective workload levels after each block of trials. The blocks consisted of various combinations of three tasks with varying levels of difficulty. The workload ratings and the performance data were used to create a database for developing models. The tasks were: (a) a continuous tracking task with a random forcing function and three different updating speeds; (b) a discrete tracking task in which response keys were pressed to indicate the position of a target in one of four different locations; and (c) a tone-counting task which required counting the number of higher pitched tones in a series of tones of 800 or 1200 Hz. Neural net models applied to group data consisting of eight individuals were able to achieve 85-95% accuracy in predicting a "redline" workload level in training data. On completely new data, accuracy was in the 70-75% range. The redline value was adopted from earlier work (Reid & Colle, 1988) showing that at that value of workload, performance measures begin to show effects of workload. Linear models (no hidden units) performed about as well as nonlinear ones in prediction using new data. Thus, for the cases we studied, linear regression models would do as well as nonlinear neural network models.

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