Predictive robot eyes shape visual attention, performance, and trust in interaction with an industrial CoBot

Abstract Collaborative robots increasingly share workspaces with humans, making the predictability of robot actions critical for efficient and safe coordination. Predictive visual cues, such as gaze or arrows, may reduce uncertainty, yet their effectiveness and robustness, especially under failure conditions, remain insufficiently understood. Across two laboratory studies with an industrial CoBot, we examined how predictive cues influence visual attention, prediction, and trust. Participants predicted the movement target of a robotic arm displaying eyes, arrows, or no cues across pre-failure, failure, and post-failure phases, while eye movements, reaction times, and trust were recorded. The second study served as a full replication. Across both studies, gaze cues accelerated visual orienting toward the target, as reflected in earlier target fixations, and improved prediction time when cues were reliable. A cue-action mismatch led to a temporary delayed target fixation and slower responses in both cue conditions. Importantly, performance and attentional guidance largely recovered during subsequent error-free interaction, with gaze cues tending to regain their advantage over no cues. Trust mostly showed the expected dynamic pattern of formation, dissolution, and restoration. Together, the findings demonstrate that predictive cues in industrial HRI enhance action anticipation by guiding visual attention, but their benefits are sensitive to cue reliability.