Binocular Imbalance in Amblyopia Depends on Spatial Frequency in Binocular Combination
Bibliographic record
Abstract
Purpose: To assess the role of spatial frequency on binocular imbalance in binocular combination in adults with amblyopia. Methods: Ten amblyopes (23 ± 4.9 [SD] years old; one deprivation, two mixed, seven anisometropia patients) and 10 age-matched normal adults (23 ± 2.3 years old) participated. The interocular contrast ratio (fellow eye/amblyopic eye, i.e., the balance point [BP]) that resulted in an equal contribution of both eyes in binocular combination was measured using a binocular orientation combination task at 0.5, 1, 2, and 4 cycles per degree (c/d). The extent of binocular imbalance was quantified as the absolute value of the BP on log scale (i.e., |logBP|). Results: When the base contrast of the amblyopic eye was set at 100% (Experiment 1), the |logBP| was found to be significantly affected by stimulus spatial frequency (F(1.44, 26.01) = 51.6, P < 0.001, \({\rm{\eta }}_g^2\)= 0.40) and group (F(1, 18) = 66.97, P < 0.001, \({\rm{\eta }}_g^2\) = 0.74), the interaction between spatial frequency and group was also significant (F(1.44, 26.01) = 38.12, P < 0.001, \({\rm{\eta }}_g^2\)= 0.33). Such spatial frequency-dependent binocular imbalance remained present, even when the base contrast of the amblyopic eye was set at equal suprathreshold contrast levels across spatial frequencies (Experiment 2). Conclusions: Binocular balance was more disrupted at higher spatial frequencies in binocular combination in amblyopia. This imbalance might not originate solely from the amblyopic eye's deficit in contrast sensitivity but is likely to be related to the difference in contrast sensitivity between the eyes.
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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 | 0.001 | 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) | 0.001 | 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".