Influence of head orientation and viewpoint oscillation on linear vection1
Bibliographic record
Abstract
Sensory conflict theories predict that adding simulated viewpoint oscillation to self-motion displays should generate significant and sustained visual-vestibular conflict and reduce the likelihood of illusory self-motion (vection). However, research shows that viewpoint oscillation enhances vection in upright observers. This study examined whether the oscillation advantage for vection depends on head orientation with respect to gravity. Displays that simulated forward/backward self-motion with/without horizontal and vertical viewpoint oscillation were presented to observers in upright (seated and standing) and lying (supine, prone, and left side down) body postures. Viewpoint oscillation was found to enhance vection for all of the body postures tested. Vection also tended to be stronger in upright postures than in lying postures. Changing the orientation of the head with respect to gravity was expected to alter the degree/saliency of the sensory conflict, which may explain the overall posture-based differences in vection strength. However, this does not explain why the oscillation advantage for vection persisted for all postures. Thus, the current postural and oscillation based vection findings appear to be better explained by ecology: Upright postures and oscillating flow (that are the norm during self-motion) improved vection, whereas lying postures and smooth optic flows (which are less common) impaired vection.
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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 | 0.002 |
| 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) | 0.002 | 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".