Closed-loop Attention Management: Using Augmented Cognition to Sustain Vigilance

Vigilance tasks, from driving to surveillance to security remain important and frequent tasks for the US Army. Yet the difficulty users have sustaining vigilance is well known. Augmented cognition offers new methods for supporting sustained vigilance via a closed-loop attention management system (CLAM). A CLAM system monitors operators’ psychophysiology for signs of inattention and then triggers a countermeasure to rouse operators and help them sustain vigilance and good task performance. Here, we report an evaluation of a complete closed-loop system composed of a combination of eye, head, and EEG measures and a novel countermeasure composed of a cognitively demanding secondary task. In order to evaluate the CLAM system, the secondary task was triggered either when inattention was detected (CLAM) or at random intervals throughout a 40 minute vigilance task. While participants in both conditions demonstrated a vigilance decrement, as measured by an increase in misses over the course of the session, the CLAM condition produced 17% fewer misses overall than the random condition. These results indicate successful real-time detection of inattention and an effective countermeasure for rousing participants and sustaining vigilance and task performance. The results inform our understanding of how human vigilance operates and the technology for its detection and manipulation.