From Trust to Trust Dynamics: Combining Empirical and Computational Approaches to Model and Predict Trust Dynamics In Human-Autonomy Interaction

AbstractTrust in automation has been identified as one central factor in effective human-autonomy interaction. Despite active research in the past 30 years, most studies have used a “snapshot” view of trust and evaluated trust using questionnaires administered at the end of an experiment. This “snapshot” view does not fully acknowledge that trust is a dynamic variable that can strengthen and decay over time. With few exceptions, we have little understanding of the temporal dynamics of trust formation and evolution, nor of how trust changes over time as a result of moment-to-moment interactions with autonomy. In this chapter, we present and synthesize the results of two studies examining trust dynamics in human-autonomy interaction. In study 1, we identify and define three properties of trust dynamics, namely continuity, negativity bias, and stabilization. The three properties characterize a human agent’s trust formation and evolution process de facto. In study 2, we propose a computational model of trust dynamics that adheres to the three properties and evaluate the computational model against existing trust inference models. Results show that our model provides superior prediction performance, and moreover, guarantees good model explainability and generalizability.KeywordsTrust dynamicsBayesian inferenceTrust in automationHuman-robot interactionHuman-automation interaction