Abstract

The effect of the Vergence–Accommodation Conflict (VAC) in state-of-the-art head-mounted displays on 3D interaction performance has been well studied in scenarios where targets remain stationary at a fixed depth. However, many VR applications include targets that move continuously toward or away from the user, causing vergence demand to change over time while accommodation remains fixed at the display focal plane. We study this scenario using an ISO 9241-411 multidirectional target selection task under four conditions: No VAC (targets at the display focal plane), Constant VAC (targets located further from the focal plane), Varying VAC (targets appear at both No VAC and Constant VAC depths across selections), and Dynamic VAC (target depth changes continuously during acquisition). In a within-subjects study with 24 participants, Dynamic VAC resulted in slower selections, higher error rates, lower accuracy, and reduced throughput compared to the No VAC condition. To account for target movements in depth, we propose an extension of Fitts’ law that models the additional performance costs introduced by continuously changing vergence demand. Our results provide guidance for modeling and designing interaction techniques in depth-varying 3D environments.