Decoding target discriminability and time pressure using eye and head movement features in a foraging search task

In military operations, rapid and accurate decision-making is crucial, especially in visually complex and high-pressure environments. This study investigates how eye and head movement metrics can infer changes in search behavior during a naturalistic shooting scenario in virtual reality (VR). Thirty-one participants performed a foraging search task using a head-mounted display (HMD) with integrated eye tracking. Participants searched for targets among distractors under varying levels of target discriminability (easy vs. hard) and time pressure (low vs. high). As expected, behavioral results indicated that increased discrimination difficulty and greater time pressure negatively impacted performance, leading to slower response times and reduced d-prime. Support vector classifiers assigned a search condition, discriminability and time pressure, to each trial based on eye and head movement features. Combined eye and head features produced the most accurate classification model for capturing tasked-induced changes in search behavior, with the combined model outperforming those based on eye or head features alone. While eye features demonstrated strong predictive power, the inclusion of head features significantly enhanced model performance. Across the ensemble of eye metrics, fixation-related features were the most robust for classifying target discriminability, while saccadic-related features played a similar role for time pressure. In contrast, models constrained to head metrics emphasized global movement (amplitude, velocity) for classifying discriminability but shifted toward kinematic intensity (acceleration, jerk) in time pressure condition. Together these results speak to the complementary role of eye and head movements in understanding search behavior under changing task parameters.