Vection and Performance During Attention-Demanding Tasks in Virtual Reality
Bibliographic record
Abstract
We investigated how attention-demanding aural and visual discrimination tasks attenuate visually-induced self-motion (vection) and how task accuracy and response time are affected by experiencing various levels of vection-inducing motion in a virtual environment. Seventeen seated observers were presented simulated motion at various virtual camera speeds from stationary to 15 m/s in a straight virtual corridor through a Vive Pro Virtual Reality headset as they performed aural, visual discrimination tasks, or no task at all. Observers generally perceived less vection at all motion levels when they performed visual discrimination tasks compared to when they had no task. Increased vection was associated with reduced accuracy on the visual task and increased response time to the aural task. These results suggest that the amount of vection perceived in virtual reality simulators can be attenuated when users perform attention-demanding tasks related to visual processing, and, conversely, vection-producing motion can affect performance in attention-demanding 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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".