Multimodal feedback enhances human belief updating performance and reduces cognitive load in urban drone operation

Supervisory control of autonomous drones in cluttered urban environments requires operators to update beliefs about dynamic hazards, such as localized wind changes, from imperfect and time-varying cues. To examine how interface design shapes this belief-updating process, four feedback conditions were compared: a baseline information panel (Control), augmented visual cues (Visual), upper-body haptic cues (Haptic), and their combination (Multimodal). Thirty participants supervised an autonomous drone in a Unity-based high-fidelity Virtual Reality simulation, with integrated eye tracking sampled at 90 Hz to derive an objective cognitive-load index. Belief updating was captured through change reporting and quantified using reaction latency and a Bayesian Beta-Binomial "local probability" metric that estimates time-resolved correctness, alongside subjective workload measured via NASA-TLX. Reaction latency decreased monotonically from Control (3.23 s) to Visual (2.61 s) to Haptic (1.67 s) to Multimodal (1.08 s). Multimodal was faster than Control (p < 0.001) and faster than Haptic (p = 0.045). Time-resolved correctness similarly improved, with mean local probability rising from 0.17 (Control) to 0.24 (Visual) and 0.22 (Haptic), reaching 0.43 under Multimodal. Eye-tracking comparisons indicated higher cognitive load in Haptic relative to Visual (p = 0.0201) and Multimodal (p = 0.0026). Together, the findings indicate a multimodal synergy that improves both speed and reliability of belief updates without the cognitive-load elevation observed under haptic-only feedback, supporting multimodal interface design for safer and more dependable human-autonomy teaming in urban drone operations.