People Dynamically Update Trust When Interactively Teaching Robots

Abstract

Human-robot trust research often measures people's trust in robots in individual scenarios. However, humans may update their trust dynamically as they continuously interact with a robot. In a well-powered study (n = 220), we investigate the trust updating process across a 15-trial interaction. In a novel paradigm, participants act in the role of teacher to a simulated robot on a smartphone-based platform, and we assess trust at multiple levels (momentary trust feelings, perceptions of trustworthiness, and intended reliance). Results reveal that people are highly sensitive to the robot's learning progress trial by trial: they take into account both previous-task performance, current-task difficulty, and cumulative learning across training. More integrative perceptions of robot trustworthiness steadily grow as people gather more evidence from observing robot performance, especially of faster-learning robots. Intended reliance on the robot in novel tasks increased only for faster-learning robots.