Modeling and inferring human trust in automation based on real- time eye tracking data

Abstract

Trust miscalibration remains a major challenge for human-machine interaction. It can lead to misuse or disuse of automated systems. To date, most trust research has relied on subjective ratings and behavioral or physiological data to assess trust. Those trust measurements are discrete, disruptive and quite difficult to implement. To better understand the process of trust calibration, we propose eye tracking as an unobtrusive method for inferring trust levels in real time. Using an Unmanned Aerial Vehicle simulation, participants were exposed to varying levels of reliability of an automated target detection system. Eye movement data were captured and labeled as high or low trust based on subjective trust ratings. Feature extraction and raw eye movement data were compared as input for multiple classification modeling methods. Accuracy rates of 92% and 80%, respectively, were achieved with individual-level and group-level modeling, suggesting that eye tracking is a promising technique for tracing trust levels.