  Previous research on scanpath prediction has mainly focused on group models,
disregarding the fact that the scanpaths and attentional behaviors of
individuals are diverse. The disregard of these differences is especially
detrimental to social human-robot interaction, whereby robots commonly emulate
human gaze based on heuristics or predefined patterns. However, human gaze
patterns are heterogeneous and varying behaviors can significantly affect the
outcomes of such human-robot interactions. To fill this gap, we developed a
deep learning-based social cue integration model for saliency prediction to
instead predict scanpaths in videos. Our model learned scanpaths by recursively
integrating fixation history and social cues through a gating mechanism and
sequential attention. We evaluated our approach on gaze datasets of dynamic
social scenes, observed under the free-viewing condition. The introduction of
fixation history into our models makes it possible to train a single unified
model rather than the resource-intensive approach of training individual models
for each set of scanpaths. We observed that the late neural integration
approach surpasses early fusion when training models on a large dataset, in
comparison to a smaller dataset with a similar distribution. Results also
indicate that a single unified model, trained on all the observers' scanpaths,
performs on par or better than individually trained models. We hypothesize that
this outcome is a result of the group saliency representations instilling
universal attention in the model, while the supervisory signal and fixation
history guide it to learn personalized attentional behaviors, providing the
unified model a benefit over individual models due to its implicit
representation of universal attention.
