Eye Tracking: A Process-Oriented Method for Inferring Trust in Automation as a Function of Priming and System Reliability

Trust miscalibration, a mismatch between a person's trust in automation and the system's actual reliability, can lead to either misuse or disuse of automation. Existing techniques to measure trust (e.g., subjective ratings) tend to be discrete and/or disruptive. To better understand and support the process of trust calibration, a nonintrusive continuous measure of trust is needed. The present study investigated whether eye tracking can serve this purpose. In the context of an unmanned aerial vehicle simulation, participants monitored six video feeds to detect predefined targets with the assistance of onboard automation. Automation reliability (95% versus 50% reliable) and priming (reliability information provided or not) were manipulated. Eye movement data, subjective trust ratings, and performance data were collected. The eye tracking data show that people visit more frequently and spend more time on low reliable automation. Priming information could also affect the participants’ trust level and trigger different types of searching behavior, as reflected in eye tracking data such as mean saccade amplitude. In summary, these findings confirm that eye tracking is a very promising tool for inferring trust and supporting future research into trust calibration.