Contributions of vision, gravity, and the body during misestimations of orientation
Bibliographic record
Abstract
When immersed in an upright (relative to them) visual scene, supine or prone viewers can experience a visual reorientation illusion (VRI) where they actually feel upright. When people report a VRI, visually-induced self-motion (vection) is enhanced (McManus & Harris, 2019 VSS). This might be due to 1) higher visual weightings in individuals who report a VRI compared to those who do not, or 2) a misinterpretation of the vestibular cue as motion instead of tilt or 3) a greater sensitivity to visual-vestibular conflict. Here we investigated the connection between VRIs and sensory weighting using virtual reality. Participant’s sensitivity to VRIs was measured over 1 minute where they continuously pressed a button if they perceived themselves as upright while lying supine (VRI) with an upright display. They were divided into VRI and non-VRI groups. The perceptual upright (PU) was then measured while sitting or lying on their side to obtain the weightings of vision, body, and gravity. Participants reported whether an ambiguous symbol in various orientations appeared as a “p” or “d” as the visual background orientation was varied. The PU was defined as midway between the orientations of maximum ambiguity and the weighting of each cue determined. The weightings of vision (mean difference= 6.22%, SE= 5.89, p=0.301) and body (mean difference= 4.45%, SE= 7.32, p=0.55) did not differ between the VRI and non-VRI groups, however the VRI group had a significantly higher weighting of gravity (mean difference= 10.67%, SE= 4.23, p=0.03). It appears that despite their reported orientation being more influenced by visual cues and enhanced vection, VRI-sensitive people’s perceptual upright is more influenced by gravity. This finding is counter to the conclusion by Howard and Hu (2001) who supposed that during a VRI participants must be ignoring the gravity vector and perhaps indicative of greater sensitivity to conflict.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".