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Despite the high degree of variability in the meaningfulness and communicative nature of gaze shifts, humans have a remarkable sensitivity to parse the gaze information of others so as to make predictions about a social partner’s behaviour and effectively interact with them. A person seemingly gazing towards an apple may be signalling admiration for the apple, an intention to grasp and eat it, or may actually be looking into the space beyond the apple while they are distracted or contemplating their day. The engagement of higher-order social-cognitive representations during gaze-based interactions is necessary given that unlike other, more spatially-precise and unambiguous non-verbal spatial cues (e.g., hand pointing or object grasping), the communicative intent and significance of gaze shifts can be highly ambiguous. Inferring people’s mental states from observing their gaze requires the ability to detect where they are looking and interpret the observed gaze as an expression of a desire or goal-directed behaviour.

The reported findings expand current models of gaze perception and may have important implications for human–human and human–robot collaboration. We suggest that the human-like shape of an agent and its physical capabilities facilitate the prediction of an upcoming action. Moreover, task instructions that focus on the visual and motor consequences of the observed gaze were found to influence mentalising abilities. Crucially, this cue had no impact on people’s ability to predict the upcoming behaviour of the triangle. We report that biasing an observer's attention, by showing just one object an agent can interact with, can improve people’s ability to understand what humanoid robots will do.

Participants observed goal-directed and non-goal directed gaze shifts made by human and robot agents, as well as directional cues displayed by a triangle. We conducted six separate experiments to investigate how spatial cues and task instructions modulate people’s ability to understand what a robot is doing. The machine-like appearance of robots, as well as contextual information, may influence people’s ability to anticipate the behaviour of robots. The future of human–robot collaboration relies on people’s ability to understand and predict robots' actions.

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