  Effective collaboration between humans and AIs hinges on transparent
communication and alignment of mental models. However, explicit, verbal
communication is not always feasible. Under such circumstances, human-human
teams often depend on implicit, nonverbal cues to glean important information
about their teammates such as intent and expertise, thereby bolstering team
alignment and adaptability. Among these implicit cues, two of the most salient
and fundamental are a human's actions in the environment and their visual
attention. In this paper, we present a novel method to combine eye gaze data
and behavioral data, and evaluate their respective predictive power for human
proficiency, trust, and intent. We first collect a dataset of paired eye gaze
and gameplay data in the fast-paced collaborative "Overcooked" environment. We
then train models on this dataset to compare how the predictive powers differ
between gaze data, gameplay data, and their combination. We additionally
compare our method to prior works that aggregate eye gaze data and demonstrate
how these aggregation methods can substantially reduce the predictive ability
of eye gaze. Our results indicate that, while eye gaze data and gameplay data
excel in different situations, a model that integrates both types consistently
outperforms all baselines. This work paves the way for developing intuitive and
responsive agents that can efficiently adapt to new teammates.
