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

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.

Workload assessment of computer gaming using a single-stimulus event-related potential paradigm

“…ERP measures of primary task difficulty are most apparent when tones were ignored, which indicates that workload monitoring devices based on single stimulus tones should not require that subjects shift resources away from primary task performance to count tones. This finding suggests for the first time that the single-stimulus procedure could be used to develop an objective workload assessment tool that is likely less distracting and psychologically invasive than conventional approaches (Berka et al, 2004;Lal and Craig, 2001;Prinzel et al, 2003;Smith et al, 2001;St. John et al, 2003;Strayer et al, 2006;Trejo et al, 1995a).…”

TX18: Cognitive Workload and Fatigue Study

“…These blocks of data will then be used to compare the performance of many different classification algorithms to see what ones are able to discriminate among the three cognitive states. This research builds on work done by the Air Force in flight simulators (Prinzel, Freeman, Scerbo, Mikulka, & Pope, 2003;Wilson & Russell 2003) as well as research investigating the combination of multiple physiological measures (Noel, Bauer, & Lanning, 2004). In addition, it extends several comparative studies of cognitive state classification to Army-relevant contexts (Luo & Sajda, 2006;Sato et al, 2009).…”

Dynamic difficulty using brain metrics of workload

“…Prinzel et al built an EEG-based adaptive automation system that alternated between manual and automated modes for a joystic tracking task and auditory pitch counting task [31]. Wilson and Russell designed a similar system based on an EEG engagement index for single-operator single-UAV systems and found success with slowing the UAV down or presenting alert in order to maintain engagement levels [43].…”