Sighting ocular dominance magnitude varies with test distance
Bibliographic record
Abstract
BACKGROUND: Ocular dominance can be defined as the preference of an individual for viewing with one eye over the other for particular visual tasks. It is relevant to monovision contact lens wear, cataract surgery and sports vision. Clinically, the measurement of ocular dominance is typically done at an arbitrary distance using a sighting test, such as the hole-in-card method that has a binary outcome. We investigated the effect of test distance on ocular dominance measured using a binocular sighting test that provided a continuous measurement of dominance. METHODS: Ten participants with normal binocular vision took part in this study. Their binocular sighting ocular dominance and phorias were measured at one, two, four, eight and 10 metres. During the dominance tests participants made a binocular alignment judgment and then were asked to indicate the relative alignment of each eye using a visual analogue scale as a reference. RESULTS: Eight participants had strong ocular dominance (five right, three left). For these participants, there was a significant increase in the magnitude of dominance with increasing test distance (p < 0.001). This could not be fully explained by changes in convergence demand. Two participants showed very weak ocular dominance across all test distances (p > 0.05), despite changes in convergence demand. CONCLUSION: When ocular dominance is present, its magnitude varies significantly with test distance. This has significant implications for the accurate measurement of ocular dominance in the clinic and may reflect the neural processes that influence eye preference.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".