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Judging by the Look: The Impact of Robot Gaze Strategies on Human Cooperation

Human eye gaze plays an important role in delivering information, communicating intent, and understanding others' mental states. Previous research shows that a robot's gaze can also affect humans' decision-making and strategy during an interaction. However, limited studies have trained humanoid robots on gaze-based data in human-robot interaction scenarios. Considering gaze impacts the naturalness of social exchanges and alters the decision process of an observer, it should be regarded as a crucial component in human-robot interaction. To investigate the impact of robot gaze on humans, we propose an embodied neural model for performing human-like gaze shifts. This is achieved by extending a social attention model and training it on eye-tracking data, collected by watching humans playing a game. We will compare human behavioral performances in the presence of a robot adopting different gaze strategies in a human-human cooperation game.