TY - JOUR
T1 - Understanding the Effect of Latency on User Performance of Target Selection in Virtual Reality
AU - Wei, Yushi
AU - Shi, Rongkai
AU - Xu, Kemu
AU - Wang, Jialin
AU - Gao, Bo Yu
AU - Hui, Pan
AU - Yu, Lingyun
AU - Liang, Hai Ning
N1 - Publisher Copyright:
© 1995-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - High latency is often introduced due to limited computational capabilities and high hardware demands. It has proven to significantly impair user performance in target selection, a fundamental interaction task. Existing research has established that latency negatively impacts selection times and success rates in 2D interactive systems; however, the underlying behavioral mechanisms remain unclear. This article investigates the effects of latency on selection times, success rates, and endpoint distributions in Virtual Reality (VR) with controller-based raycasting and bare-hand direct touch—the two most common selection methods. Our results from a user study (N = 31) revealed distinct patterns between the two methods, leading to two novel mathematical models that account for latency, target width, and movement amplitude. These two models were validated via a new dataset collected from a second user study (N = 16) and were demonstrated to outperform the existing models. Our findings provide actionable recommendations to mitigate the negative impacts of latency and improve user experience in VR interface design.
AB - High latency is often introduced due to limited computational capabilities and high hardware demands. It has proven to significantly impair user performance in target selection, a fundamental interaction task. Existing research has established that latency negatively impacts selection times and success rates in 2D interactive systems; however, the underlying behavioral mechanisms remain unclear. This article investigates the effects of latency on selection times, success rates, and endpoint distributions in Virtual Reality (VR) with controller-based raycasting and bare-hand direct touch—the two most common selection methods. Our results from a user study (N = 31) revealed distinct patterns between the two methods, leading to two novel mathematical models that account for latency, target width, and movement amplitude. These two models were validated via a new dataset collected from a second user study (N = 16) and were demonstrated to outperform the existing models. Our findings provide actionable recommendations to mitigate the negative impacts of latency and improve user experience in VR interface design.
KW - endpoint
KW - head-mounted display
KW - human performance modeling
KW - latency
KW - Virtual reality
UR - https://www.scopus.com/pages/publications/105022711105
U2 - 10.1109/TVCG.2025.3634367
DO - 10.1109/TVCG.2025.3634367
M3 - Article
C2 - 41269812
AN - SCOPUS:105022711105
SN - 1077-2626
VL - 32
SP - 2200
EP - 2215
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 2
ER -