NSF CAREER Grant: Physiological Modeling of Longitudinal Human Trust in Autonomy for Operational Environments 

Operating large, complex systems increasingly requires humans to work with and trust highly autonomous agents. Accurate trust calibration ensures effective human autonomy teaming, but trust changes with exposure to the autonomous system. The change of trust over weeks, months, or years (longitudinal trust) is an understudied area of literature. The project aims to fill this gap by:

  1. Modeling initial trust with physiological features
  2. Assessing learned trust dynamics after repeated interactions with autonomy
  3. Investigating trust calibration by assessing operators’ trust dynamics when autonomy reliability shifts
  4. Understand the utility of wearable sensors to model trust in operational environments 

These aims will be completed through various qualitative interviews, physiological experiments in the lab and physiological modeling efforts in both a warehouse and aviation setting. In addition, this grant supports training the current human autonomy teaming workforce on trust by providing warehouse employees with training on trusting autonomous systems. The grant aims to increase access and engagement in STEM in rural Colorado by developing and implementing experiential learning modules for high schoolers. 

Amazon robot

Current Students:  

MS: Samuel Kurtin 

Undergraduates: Milo Ruiz, Jaekeun Sung, and Ryan Chen 

Funding: National Science Foundation