A review of mathematical models of human trust in automation - Rodriguez Rodriguez, Lucero; Bustamante Orellana, Carlos E.; Chiou, Erin K.; Huang, Lixiao; Cooke, Nancy; Kang, Yun (Frontiers in Neuroergonomics, 2023)
To realize the promise of better performance and safety in human-autonomy teaming, understanding how people trust the autonomous system is essential. Trust in automation is a rich and complex process that has sparked myriad measures and approaches to study and understand it. Mathematical models have been powerful tools to provide useful insights into dynamical processes of trust in automation. This paper reviews how varied mathematical models have been developed to help us better understand trust in automation and the limitations of those models. Given the consensus view of trust being an interactive and dynamic process with multiple sources of influence, we propose a novel and dynamic approach to address the multi-time scales nature of trust in automation dynamics. To utilize the physiological data, we also propose that machine learning and dynamical modeling should be combined to study trust in automation due to its complex nature.