The role of visual background orientation on the perceptual upright during microgravity
Bibliographic record
Abstract
The perceptual upright (PU) — the orientation in which an object is most easily and naturally recognized — is determined by a combination of the orientation of the body, the visual background, and gravity. PU can be assessed by identifying a character the identity of which depends on its orientation (the Oriented Character Recognition Test: OCHART, Dyde et al. VSS 2004. J. Vision, 4(8), 385a). Using OCHART we measured the influence of the orientation of the visual background on the PU in the fronto-parallel plane under conditions where gravity was irrelevant (when the character was presented orthogonal to gravity, with the subject lying supine); or not present (during exposure to microgravity created during parabolic flight). When supine in 1g the influence of the background on the PU was reliably greater than when the observer was upright in 1g. In microgravity the influence of the background on PU was reliably less than in the equivalent 1g state; curiously a similar reduction relative to the 1g condition was also found during the hyper-gravity phase of parabolic flight. These perceptual changes are consistent with an increase in the use of the body as a reference frame when gravity is changed. The effects of microgravity in the fronto-parallel plane cannot be simulated by simply arranging gravity to be orthogonal to that plane by lying supine.
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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 | 0.000 |
| 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".