Closed-loop regulation of user state during a boundary avoidance task

Pilot induced oscillations (PIOs) are potentially catastrophic events that occur during flight when pilots attempt to control an aircraft close to a performance or physical boundary. PIO-like behavior is typically observed in boundary avoidance tasks (BAT), which simulate tight performance or physical boundaries and induce high cognitive workload. Our previous research linked the occurrence of PIO-like behavior to network level activity in the brain, where higher states of arousal reduce the flexibility of decision making networks such that less environmental information was incorporated to dynamically adjust action. This led us to hypothesize that down regulating arousal via closed-loop audio feedback of a user state could improve piloting performance by enabling increased decision flexibility. Here we show our initial results testing this hypothesis, where we use a hybrid brain computer interface (hBCI) to dynamically provide feedback to a “pilot” that facilitates their ability to reduce their state of arousal. We conduct a systematic comparison relative to control and sham conditions and test to see if this feedback increases the time a “pilot” can fly before a catastrophic PIO. We find that hBCI feedback, which includes central nervous system components consistent with theta activity in the anterior cingulate cortex (ACC), enables prolonged flight relative to closed-loop control and sham feedback. We also find that this feedback induces changes in pupil diameter which are absent in openloop conditions and closed-loop conditions when feedback is not veridical. Pupil diameter has been reported as a surrogate measure of activity in the locus coeruleus-norepinephrine (LC-NE) system which is also linked to a circuit that includes the ACC. We conclude that the feedback we induce with our hBCI provides preliminary evidence that self-regulation of LC-NE/ACC is possible and can be used to dynamically increase decision flexibility when under high cognitive workload.