Effects of a Psychophysiological System for Adaptive Automation on Performance, Workload, and the Event-Related Potential P300 Component

Lawrence J. Prinzel · Frederick G. Freeman · Mark W. Scerbo · Peter J. Mikulka · Alan Pope

145 citations0/0 files storedHuman Factors The Journal of the Human Factors and Ergonomics Society

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Human-Automation Interaction and Safety EEG and Brain-Computer Interfaces Neural and Behavioral Psychology Studies

Abstract

The present study examined the effects of an electroencephalographic- (EEG-) based system for adaptive automation on tracking performance and workload. In addition, event-related potentials (ERPs) to a secondary task were derived to determine whether they would provide an additional degree of workload specificity. Participants were run in an adaptive automation condition, in which the system switched between manual and automatic task modes based on the value of each individual's own EEG engagement index; a yoked control condition; or another control group, in which task mode switches followed a random pattern. Adaptive automation improved performance and resulted in lower levels of workload. Further, the P300 component of the ERP paralleled the sensitivity to task demands of the performance and subjective measures across conditions. These results indicate that it is possible to improve performance with a psychophysiological adaptive automation system and that ERPs may provide an alternative means for distinguishing among levels of cognitive task demand in such systems. Actual or potential applications of this research include improved methods for assessing operator workload and performance.

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References

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