Body posture affects the perception of visually simulated self-motion
Bibliographic record
Abstract
Perceiving one’s self-motion is a multisensory process involving integrating visual, vestibular and other cues. The perception of self-motion can be elicited by visual cues alone (vection) in a stationary observer. In this case, optic flow information compatible with self-motion may be affected by conflicting vestibular cues signaling that the body is not accelerating. Since vestibular cues are less reliable when lying down (Fernandez & Goldberg, 1976), conflicting vestibular cues might bias the self-motion percept less when lying down than when upright. To test this hypothesis, we immersed 20 participants in a virtual reality hallway environment and presented targets at different distances ahead of them. The targets then disappeared, and participants experienced optic flow simulating constant-acceleration, straight-ahead self-motion. They indicated by a button press when they felt they had reached the position of the previously-viewed target. Participants also performed a task that assessed biases in distance perception. We showed them virtual boxes at different simulated distances. On each trial, they judged if the height of the box was bigger or smaller than a reference ruler held in their hands. Perceived distance can be inferred from biases in perceived size. They performed both tasks sitting upright and lying supine. Participants needed less optic flow (perceived they had travelled further) to perceive they had reached the target’s position when supine than when sitting (by 4.8%, bootstrapped 95% CI=[3.5%;6.4%], determined using Linear Mixed Modelling). Participants also judged objects as larger (compatible with closer) when upright than when supine (by 2.5%, 95% CI=[0.03%;4.6%], as above). The bias in traveled distance thus cannot be reduced to a bias in perceived distance. These results suggest that vestibular cues impact self-motion distance perception, as they do heading judgements (MacNeilage, Banks, DeAngelis & Angelaki, 2010), even when the task could be solved with visual cues alone.
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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 | 0.003 |
| 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) | 0.003 | 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".