Empirical Study of Gaze Behavior in Children and Young Adults Using Deep Neural Networks and Robot Implementation: A Comparative Analysis of Social Situations

Abstract: In a preliminary exploratory study, our goal was to train deep neural network models to mimic children's and/or adults' gaze behavior in certain social situations to reach this objective. Additionally, we aim to identify potential differences in gaze behavior between these two age groups based on our participants' gaze data. Furthermore, we aimed to assess the practical effectiveness of our adult and children models by deploying them on a Nao robot in real-life settings. To achieve this, we first created two video clips, one animation and one live-action, to depict some social situations. Using an eye-tracking device, we collected eye-tracking data from 24 participants, including 12 children and 12 adults. Then, we utilized deep neural networks, specifically LSTM and Transformer Networks, to analyze and model the gaze patterns of each group of participants. Our results indicate that when the models attempted to predict people's locations (in the next frame), they had an accuracy in the range of 62%-70% with one attempt, which increased by ~20% when attempted twice (i.e. the two highest-ranked predicted labels as outputs). As expected, the result underscores that gaze behavior is not a wholly unique phenomenon. We obtained feedback from 57 new participants to evaluate the robot's functionality. These participants were asked to watch two videos of the robot's performance in each mode and then complete a comprehensive questionnaire. The questionnaire results indicate that the participants expressed satisfaction with the robot's interaction, including its attention, intelligence, and responsiveness to human actions. However, they did not perceive the robot as a social companion comparable to a human. This exploratory study tries to address/show potentials of the social acceptance of robots based on human nonverbal behavioral cues for future research.