Predicting VR cybersickness and its impact on visuomotor performance using head rotations and field (in)dependence
Bibliographic record
Abstract
Introduction: This exploratory study aims to participate in the development of the VR framework by focusing on the issue of cybersickness. The main objective is to explore the possibilities of predicting cybersickness using i) field dependence-independence measures and ii) head rotations data through automatic analyses. The second objective is to assess the impact of cybersickness on visuomotor performance. Methods: 40 participants completed a 13.5-min VR immersion in a first-person shooter game. Head rotations were analyzed in both their spatial (coefficients of variations) and temporal dimensions (detrended fluctuations analyses). Exploratory correlations, linear regressions and clusters comparison (unsupervised machine learning) analyses were performed to explain cybersickness and visuomotor performance. Traditional VR human factors (sense of presence, state of flow, video game experience, age) were also integrated. Results: Results suggest that field dependence-independence measured before exposure to VR explain ¼ of the variance of cybersickness, while the Disorientation scale of the Simulator Sickness Questionnaire predicts 16.3% of the visuomotor performance. In addition, automatic analyses of head rotations during immersion revealed two different clusters of participants, one of them reporting more cybersickness than the other. Discussion: These results are discussed in terms of sensory integration and a diminution of head rotations as an avoidance behavior of negative symptoms. This study suggests that measuring field dependence-independence using the (Virtual) Rod and Frame Test before immersion and tracking head rotations using internal sensors during immersion might serve as powerful tools for VR actors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".