Immersive virtual reality (VR) is increasingly used to analyze perceptual-motor coordination. This study leverages VR's ability to precisely control task constraints to investigate motor actions in 3D space, focusing on the depth dimension. The research utilizes a visuomotor tracking task, requiring continuous perceptual-motor regulation to maintain the effector (controller) near a moving target within a virtual cube. This task involves prospective control, minimizing the gap between current and desired behavior. Prior research on tracking tasks, mostly in 2D, has highlighted the impact of factors like target speed and feedback delay on accuracy and neural processes involved. Studies on stroke patients revealed a shift from feedback to feedforward control with increased speed, alongside reduced accuracy. Analysis of ataxic patients using a visuomotor tracking task provided parameters for assessing cerebellar ataxia. The inclusion of a depth component is crucial for examining 3D interactions, as humans utilize binocular and monocular cues for depth perception (occlusion, height, size, density, perspective, binocular disparities, accommodation, convergence, and motion parallax). Studies have demonstrated the impact of trajectory dimension (1D, 2D, 3D) on sensorimotor control, with accuracy decreasing as dimension increases. Previous research in 3D VR examined reaching performance based on environmental quality and depth cues, finding improvements with enhanced cues like stereopsis and motion parallax. Research on 3D tracking tasks in healthy adults showed decreased initial peak velocity with increased target speed, and faster peak velocity attainment in the dominant hand. Limited research exists on the effect of gain (real-to-virtual movement transformation) in VR, with some studies showing accuracy degradation and increased motion sickness at higher gain levels. This study addresses the gap in knowledge regarding the effects of gain, interaction space size, and movement speed in a precisely controlled 3D VR tracking task, providing valuable insights for VR device design and applications like neuromotor rehabilitation. The central research question is: What is the impact of various task constraints on target tracking in a 3D virtual environment? Hypotheses include: participants will adapt to the task with limited practice; manipulating constraints increases task difficulty; depth is the primary source of difficulty; and depth-dimension target displacement involves elbow coupling.

The literature review extensively covers existing research on visuomotor tracking tasks, predominantly in 2D environments. Studies exploring the effects of target speed, feedback delay, and gain transformation on tracking accuracy and neural correlates were examined. The impact of depth perception in 3D environments was also reviewed, analyzing how binocular and monocular cues influence reaching and grasping tasks. Previous research highlighted challenges in depth perception in VR due to limitations in providing accurate depth cues, such as the vergence-accommodation conflict. Existing studies on the use of 3D virtual environments for studying visuomotor control and the impact of task constraints like gain and target speed on task performance were also reviewed.

Twenty-three participants (12 males, 11 females; 32.8 ± 11.7 years) with no VR experience or physical disabilities performed a visuomotor tracking task using an HTC Vive Pro Eye VR headset. The task involved tracking a virtual target moving within a cube using a hand-held controller. Three task constraints were manipulated: gain (4 levels transforming real to virtual movement), size (4 cube sizes), and speed (4 target speeds). Each participant completed 120 trials (10 trials/condition x 4 conditions/constraint x 3 constraints). The HTC Vive system recorded the positions of markers on the shoulder, elbow, and controller. The target-effector distance and elbow angle were calculated. The study employed a within-subjects design with counterbalancing to control for order effects. Data analysis involved ANOVAs on the target-effector distance (absolute and in x, y, z dimensions) and maximal elbow range of motion, considering blocks of 10 trials. Regression analyses determined the coupling between target position in each dimension and elbow angle. Post hoc tests using the Bonferroni method were employed for comparisons.

Participants quickly adapted to the task, showing improved accuracy (increased target-effector contact and decreased distance) within the first 30 trials. Increased task constraints (higher gain, larger cube size, faster speed) significantly increased the absolute target-effector distance. Higher gain conditions led to a reduction in maximal elbow range of motion; however, this did not lead to higher accuracy. Increased cube size and target speed resulted in a larger maximal elbow range of motion. The depth dimension (z-axis) consistently posed the greatest challenge, showing the highest target-effector distance and the strongest coupling between elbow angle and target movement in the depth direction across all constraints. The R² values from regression analyses consistently indicated a stronger correlation between elbow angle and target position in the z dimension compared to x and y dimensions.

The findings support the hypotheses that task constraints increase difficulty (higher distance from target), and that depth perception significantly impacts task performance and elbow movement. The rapid adaptation observed suggests the task's clarity and feedback facilitated quick adjustments. The inverse relationship between gain and elbow range of motion, yet the increased error rate at high gain levels suggest an optimal range for smooth interaction. The depth dimension’s dominant role highlights the importance of accurate depth cues in VR. The observed vergence-accommodation conflict could partly explain the increased difficulty in depth perception, highlighting a potential limitation of current VR systems. The findings are valuable for designing VR systems and rehabilitation programs, suggesting customizable task constraints to tailor interventions for various upper-limb disabilities.

This study confirms the impact of task constraints on 3D visuomotor tracking performance in VR, particularly emphasizing the challenge posed by the depth dimension. The findings provide insights for optimizing VR system design and developing effective rehabilitation programs, enabling customizable task difficulty for patients with upper-limb impairments. Further research could investigate the impact of different types of depth cues and the long-term effect of VR-based rehabilitation on functional recovery.

The study's sample size was relatively small. The vergence-accommodation conflict inherent in VR headsets might have influenced depth perception and could be further explored. The study focused on novice VR users, and results might differ for experienced users. Future studies could investigate the effects of prolonged training and explore the transfer of learned skills to real-world tasks.