Speed-Accuracy Trade-Off in VR-based Eye-Hand Coordination Training: The Impact of Task Instructions and Physical Abilities

Abstract Virtual Reality (VR) has been explored as a platform for eye–hand coordination training (EHCT), offering an immersive environment that supports motor skill development. Previous work has proposed using Fitts’ law and effective throughput as performance assessment metrics for EHCT, assuming throughput is invariant to the speed–accuracy trade-off. However, despite progress in EHCT research, the most effective ways to train individuals and evaluate their learning progress in VR remain unclear. To address this gap, we conducted a longitudinal user study with 36 participants with varying physical abilities (healthy individuals , athletes, and those with limited movement) to examine how different task instructions (accuracy-focused, error-focused, or neutral) affect user performance during training. Our results showed that throughput does not remain invariant but varies with task instructions and individual performance strategies. This finding reveals inherent limitations of throughput as a reliable indicator of learning progress, particularly in scenarios characterized by a strong speed–accuracy trade-off. These insights highlight the need for alternative performance metrics that more accurately capture user adaptation and learning in VR-based EHCT.