Binocular visual training to promote recovery from monocular deprivation
Bibliographic record
Abstract
Abnormal early visual experience often leads to poor vision, a condition called amblyopia. Two recent approaches to treating amblyopia include binocular therapies and intensive visual training. These reflect the emerging view that amblyopia is a binocular deficit caused by increased neural noise and poor signal-in-noise integration. Most perceptual learning studies have used monocular training; however, a recent study has shown that binocular training is effective for improving acuity in adult human amblyopes. We used an animal model of amblyopia, based on monocular deprivation, to compare the effect of binocular training either during or after the critical period for ocular dominance plasticity (early binocular training vs. late binocular training). We used a high-contrast, orientation-in-noise stimulus to drive the visual cortex because neurophysiological findings suggest that binocular training may allow the nondeprived eye to teach the deprived eye's circuits to function. We found that both early and late binocular training promoted good visual recovery. Surprisingly, we found that monocular deprivation caused a permanent deficit in the vision of both eyes, which became evident only as a sleeper effect following many weeks of visual training.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".