Evidence against an ecological explanation of the jitter advantage for vection
Bibliographic record
Abstract
Visual-vestibular conflicts have been traditionally used to explain both perceptions of self-motion and experiences of motion sickness. However, sensory conflict theories have been challenged by findings that adding simulated viewpoint jitter to inducing displays enhances (rather than reduces or destroys) visual illusions of self-motion experienced by stationary observers. One possible explanation of this jitter advantage for vection is that jittering optic flows are more ecological than smooth displays. Despite the intuitive appeal of this idea, it has proven difficult to test. Here we compared subjective experiences generated by jittering and smooth radial flows when observers were exposed to either visual-only or multisensory self-motion stimulations. The display jitter (if present) was generated in real-time by updating the virtual computer-graphics camera position to match the observer's tracked head motions when treadmill walking or walking in place, or was a playback of these head motions when standing still. As expected, the (more naturalistic) treadmill walking and the (less naturalistic) walking in place were found to generate very different physical head jitters. However, contrary to the ecological account of the phenomenon, playbacks of treadmill walking and walking in place display jitter both enhanced visually induced illusions of self-motion to a similar degree (compared to smooth displays).
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".