Binocular vision and fixational eye movements
Bibliographic record
Abstract
The aim of this study was to assess the relationship between binocular vision and fixation stability (FS). Across three experiments, we investigated (a) whether fixation was more stable during binocular versus monocular viewing across a range of stimulus contrasts in normal observers (n = 11), (b) whether binocular rivalry affected FS in normal observers (n = 14), and (c) whether FS was affected by interocular contrast differences in normal observers (n = 8) and patients with anisometropic amblyopia (n = 5). FS was quantified using global bivariate contour ellipse area, and microsaccades were detected using an unsupervised cluster-detection method. In normal observers, binocular viewing showed more stable fixation at all stimulus contrasts, and binocular rivalry did not affect FS. When interocular contrast was manipulated under dichoptic viewing conditions, normal observers exhibited less stable fixation for an eye that viewed 0% contrast (no fixation target). In anisometropic amblyopia, fixation was less stable in both eyes when the fellow eye viewed at 0% contrast. No effects were observed at other interocular contrast differences. Overall, binocular FS was impaired in both eyes in anisometropic amblyopia compared to normal observers. We conclude that binocular vision influences FS in normal observers but in an all-or-nothing fashion, whereby the presence or absence of a binocular target is important rather than the relative contrast of the targets in each eye. In anisometropic amblyopia, the fellow eye appears to control FS of both eyes under dichoptic viewing conditions.
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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".