The search for instantaneous vection: An oscillating visual prime reduces vection onset latency
Bibliographic record
Abstract
Typically it takes up to 10 seconds or more to induce a visual illusion of self-motion ("vection"). However, for this vection to be most useful in virtual reality and vehicle simulation, it needs to be induced quickly, if not immediately. This study examined whether vection onset latency could be reduced towards zero using visual display manipulations alone. In the main experiments, visual self-motion simulations were presented to observers via either a large external display or a head-mounted display (HMD). Priming observers with visually simulated viewpoint oscillation for just ten seconds before the main self-motion display was found to markedly reduce vection onset latencies (and also increase ratings of vection strength) in both experiments. As in earlier studies, incorporating this simulated viewpoint oscillation into the self-motion displays themselves was also found to improve vection. Average onset latencies were reduced from 8-9s in the no oscillating control condition to as little as 4.6 s (for external displays) or 1.7 s (for HMDs) in the combined oscillation condition (when both the visual prime and the main self-motion display were oscillating). As these display manipulations did not appear to increase the likelihood or severity of motion sickness in the current study, they could possibly be used to enhance computer generated simulation experiences and training in the future, at no additional cost.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".