Presentation
46. Improving Predictions of Cognitive States for an Adaptive Autonomous System
SessionPoster Session 2
DescriptionFuture 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 due to 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. Results indicate that a model with no operator background information or eye-tracking data performed comparably to a model with all feature categories. These simplified estimates enhance feasibility for an autonomous system to change modes in real-time to accommodate an operator’s cognitive states.
Event Type
Poster
TimeThursday, September 12th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
Aging
Augmented Cognition
Children's Issues
Communications
Cybersecurity
Education
Environmental Design
General Sessions
Human AI Robot Teaming (AI)
Macroergonomics
Occupational Ergonomics
Student Forum
Surface Transportation
Sustainability
System Development