  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.
