The importance of accurate VR head registration on skilled motor performance
Bibliographic record
Abstract
Many virtual reality (VR) researchers consider exact head registration (HR) and an exact multi-sensory alignment between real world and virtual objects to be a critical factor for effective motor performance in VR. Calibration procedures, however, can be error prone, time consuming and sometimes impractical to perform. To better understand the relationship between head registration and fine motor performance, we conducted a series of reciprocal tapping tasks under four conditions: real world tapping, VR with correct HR, VR with mildly perturbed HR, and VR with highly perturbed HR. As might be expected, VR performance was worse than real world performance. There was no effect of HR perturbation on motor performance in the tapping tasks. We believe that sensorimotor adaptation enabled subjects to perform equally well in the three VR conditions despite the incorrect head registration in two of the conditions. This suggests that exact head registration may not be as critically important as previously thought, and that extensive per-user calibration procedures may not be necessary for some VR tasks.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".