Cognitive Modelling of Visual Attention Captures Trust Dynamics in Human–Robot Collaboration

摘要

Understanding how humans perceive and interact with robots is crucial for collaborative scenarios. Trust, a pivotal factor in such interactions, is inherently volatile and subjective, posing significant challenges for robots. However, trust has also been shown to influence specific human bio-signals and behaviours, suggesting that it could be inferred from those indicators. One such indicator is visual attention, the cognitive process of focusing on distinct environmental elements, often manifested through eye gaze. Despite recent research connecting eye gaze and trust in Human–Robot Collaboration scenarios, this relationship remains largely unexplored. This article presents a novel signal, the Attention Arbitration Ratio (AAR), which is shown to be a promising real-time predictor of subjective and objective trust measures. We obtain this signal using a visual attention modelling framework that explicitly emulates the Bottom-Up and Top-Down processes, two key cognitive components. We demonstrate the connection between the AAR and trust using Bayesian data analysis, and we analyse the sensitivity of that connection with different visual attention models. For evaluation purposes, we collected gaze data and trust questionnaires from 49 interactions where 29 participants engaged in a collaborative assistive cooking task with a robot, for a total duration of 24h53 of data collection. The video for this article is available at https://youtu.be/LQ9Oi88YmWk.