Augmented Cognition For Sustaining Attention
Program Description
This basic research project is investigating augmented cognition methods for detecting lapses of attention in real-time and activating countermeasures to sustain attention and task performance.
OPERATIONAL GAP
Monitoring and control tasks, particularly under full or partial automation, may become monotonous and make attention difficult to sustain. Yet these tasks may be critical to mission success. Both long term and real-time solutions are needed to help operators sustain attention and task performance. Augmented cognition offers real-time detection and activation of countermeasures by monitoring operators’ psychophysiology.
VALUE TO THE WARFIGHTER
An augmented cognition system can improve sustained attention and task performance while reducing the stress that sustaining attention can induce. State of the art systems are minimally or noninvasive, unconstraining, and nondistracting from the warfighters’ tasking.
Approach
We have developed an augmented cognition system composed of a battery of eye, head, and EEG-based psychophysiological measures of attention. These measures are combined to predict lapses of attention and activate countermeasures in real-time. We have investigated measures, models for inattention prediction, and countermeasures in the context of a vigilance task that is couched as a video surveillance task. Recently, we have extended the research to visual search tasks.
Results
The combined eye, head, and EEG-based measures successfully predicted inattention and accounted for over 40% of the variance in task performance. In a recent experiment, the augmented cognition system supported 17% better performance on a target detection task than an alternative system in which countermeasures were activated randomly. Participants preferred the augmented cognition system to the random system. Participants also preferred our countermeasure – a simple cognitive task – to alarms, thereby increasing user acceptance of the system.
Watch a video of a closed-loop attention management system