Bibliographic record
Abstract
PURPOSE: Knowing one's orientation relative to the environment is important for many aspects of vision including object recognition, action planning, and balance. Here we assess how inadequate optical correction for typical refractive errors might influence this. We measured the effect of blur on the perception of orientation as measured by the subjective visual vertical (SVV) and the perceptual upright (PU). METHODS: The SVV and the PU were determined using a tilted line (was the line tilted left or right of vertical?) and the Oriented CHAracter Recognition Test (OCHART; was a character a "p" or a "d"?), respectively, in the presence of tilted visual backgrounds that were blurred using Gaussian blur with a radius of from 0 to 91 arc min. This is approximately equivalent to between 0 and 13 diopters of refractive error. RESULTS: Blur reduced the influence of vision on both the SVV and PU by one just noticeable difference (84%) when vision was blurred by 11 to 13 arc min. That is, visual cues to self-orientation remain effective until vision is degraded to about 20/240 - roughly equivalent of taking off a pair of 2 diopter prescription glasses. CONCLUSIONS: This reduction in the effectiveness of vision for determining orientation has important implications for the visually impaired and the elderly. Attempting tasks that require balance in the presence of uncorrected refractive errors may be more hazardous than expected. The effect of not optically correcting peripheral vision may also be consequential owing to the role of the far periphery in balance control.
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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 | 0.004 |
| 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 | 0.001 |
| 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".