Toward a More Standardized Multi-Directional Tapping Task in VR: the Effect of Target Depth
Bibliographic record
Abstract
The multi-directional tapping task has long served as a foundational tool for evaluating pointing performance in human-computer interaction research. However, its transition from 2D interfaces to virtual reality (VR) raises challenges, especially in standardizing target depth. This study explores how target depth influences performance in VR, focusing on two common techniques: Raycasting and Virtual Hand. We conducted two controlled experiments (each with$\mathrm{n}=20$) to isolate depth effects. In Experiment 1, fixed target size led to visual angle (VA) shifts across depths, affecting performance. Both techniques performed best when VA was between$1-4^{\circ}$; Raycasting peaked at 2 m, Virtual Hand at$0.4-0.5 ~\mathrm{m}$. In Experiment 2, we controlled VA to isolate depth itself. Raycasting remained stable beyond 2 m but degraded at close range due to biomechanical limits. Virtual Hand remained sensitive to depth despite fixed VA, but differences were smaller, with throughput unaffected. These results suggest VA should be the primary parameter for standardizing the task in VR. Depth-specific evaluation remains necessary, except for Raycasting beyond 2 m. We provide depth-aware guidelines to improve standardization and comparability while aligning with ISO protocols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".