Improving Predictions of Cognitive States for an Adaptive Autonomous System

Abstract

Future crewed deep space missions will be challenged by substantial communication latency with Earth. Autonomous systems will likely augment the role of mission control, enabling a more Earth-independent crew. To improve the performance of human-autonomy teams, autonomous systems can adapt in real-time to accommodate changes to an operator’s cognitive states caused by dynamic spaceflight events. The aim of this work was to determine the most important feature categories to accurately predict an operator’s cognitive states in real-time as they work with an autonomous system. We utilized data from a human-autonomy teaming experiment in which trust, mental workload, and situation awareness were predicted as participants completed a spaceflight-relevant task. In cognitive state predictions of unseen operators, a model with no operator background information or eye-tracking data outperformed models that included these features. These simplified models enhance feasibility for an autonomous system to adapt in real-time to accommodate an operator’s cognitive states.