Enhancement of visual cues to self-motion during a visual/vestibular conflict
Bibliographic record
Abstract
Perceiving our orientation and motion requires sensory information provided by vision, our body and acceleration. Normally, these cues are redundant however in some situations they can conflict. Here, we created a visual-vestibular conflict by simulating a body-upright virtual world while participants were either standing (no conflict), supine or prone (conflict) and assessed the perception of "forward" distance travelled induced by visual motion. Some participants felt they were standing upright even when lying, indicating a visual reorientation illusion (VRI). We previously showed that when experiencing a VRI, visually induced self-motion is enhanced. Here, we determined if there was a relationship between VRI vulnerability and sensory weighting. Confirming our previous findings, the VRI-vulnerable group showed enhanced self-motion perception. We then assessed the relative weightings of visual and non-visual cues in VRI-vulnerable and VRI-resistant individuals using the Oriented Character Recognition Test. Surprisingly, VRI-vulnerable individuals weighted visual cues less and gravity cues more compared to VRI-resistant individuals. These findings are in line with robust integration where, when the difference between two cues is large, the discrepant cue (here gravity) is ignored. Ignoring the gravity cue then leads to relatively more emphasis being placed on visual information and thus a higher gain.
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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 | 0.001 |
| 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".