Judging perceptual stability during active rotation and translation in various orientations
Bibliographic record
Abstract
Translation and rotation are detected by different patterns of optic flow and by different divisions of the vestibular system. A given movement (eg. yaw rotation or up/down translation) involves different sensors depending on the orientation of the movement with respect to gravity. Here we assess the contribution of these different sense systems to the “whole system” response to self motion. Our subjects' task was to distinguish self produced from external visual motion during rotation around the yaw, pitch and roll axes and during translation in the x (naso-occipital), y (sideways) and z (up and down) directions. The axis or direction of motion was parallel or orthogonal to the direction of gravity. Subjects wore a helmet-mounted display whose position was monitorred by a mechanical head tracker with minimal lag. The visual display was modified in response to head movement. The ratio between head and image motion was varied randomly using the method of constant stimuli. Subjects indicated whether the display appeared earth-stationary or not. For both rotation and translation there was a large range of ratios that was tolerated as perceptually stable. The ratio most likely to be accepted as stable corresponded to visual motion being faster than head motion. For rotation there were no consistent differences between yaw, pitch or roll axes and the orientation of the axis relative to gravity also had no effect. For translation motion in the x direction was on average matched with less visual motion than y or z motion. Although there was no consistent effect of whether motion was parallel or orthogonal to gravity, posture, relative to gravity, did have an effect. Sponsorred by CRESTech and NSERC.
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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 | 0.001 | 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 | 0.000 |
| 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".