LAUSR

OBJECTIVE Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators. BACKGROUND Military UAV operators use the command-and-control (C2) map for developing mission-relevant situation awareness (SA). Yet C2 maps are overloaded with information, mostly irrelevant to the mission, causing operators to neglect the map altogether. To reduce irrelevant information, an intelligent filtering algorithm was developed. Here we evaluate its effectiveness in reducing operators' cognitive workloads. METHOD Two-stage operational scenarios were conducted with professional ex-military UAV operators, using two filter protocols and a no-filter control. High-end real-time techniques were used to continuously assess workload from muscle behavior and machine learning models. RESULTS Lower cognitive workload was found when applying the algorithm's protocols, especially when fatigue started to accumulate (Stage II). However, concerns about the quality of SA arose. CONCLUSION The algorithm was positively evaluated for its ability to reduce operators' cognitive workloads. More evaluations of operators' SA are required. APPLICATION The algorithm demonstrates the possibility of integrating AI to improve human performance in complex systems, and can be applied to other domains where spatial-temporal information needs to be contextually filtered in real time.