  To enhance human-robot social interaction, it is essential for robots to
process multiple social cues in a complex real-world environment. However,
incongruency of input information across modalities is inevitable and could be
challenging for robots to process. To tackle this challenge, our study adopted
the neurorobotic paradigm of crossmodal conflict resolution to make a robot
express human-like social attention. A behavioural experiment was conducted on
37 participants for the human study. We designed a round-table meeting scenario
with three animated avatars to improve ecological validity. Each avatar wore a
medical mask to obscure the facial cues of the nose, mouth, and jaw. The
central avatar shifted its eye gaze while the peripheral avatars generated
sound. Gaze direction and sound locations were either spatially congruent or
incongruent. We observed that the central avatar's dynamic gaze could trigger
crossmodal social attention responses. In particular, human performances are
better under the congruent audio-visual condition than the incongruent
condition. Our saliency prediction model was trained to detect social cues,
predict audio-visual saliency, and attend selectively for the robot study.
After mounting the trained model on the iCub, the robot was exposed to
laboratory conditions similar to the human experiment. While the human
performances were overall superior, our trained model demonstrated that it
could replicate attention responses similar to humans.
