Intact Perceptual Interactions of Interocular Temporal Phase and Contrast Disparities in Amblyopia
Bibliographic record
Abstract
Purpose: Interocular temporal and contrast differences have been applied separately to improve binocular integration and treat amblyopia. However, the extent to which binocular integration in amblyopia can be improved by combining interocular differences in timing and contrast has yet to be tested. We evaluated how these parameters interact in individuals with amblyopia and in normally sighted controls. Methods: We developed an interocular flicker integration task in which a pair of dichoptic gratings flickered sinusoidally in counterphase (2 hertz [Hz], 90-degree spatial and temporal offset), producing the appearance of motion. We determined the interocular delay required for optimal integration by adding phase delays to the 90-degree phase offset (left or right eye leading, -80 degrees to +80 degrees). Delays were tested across different interocular contrast conditions: 50%-50%, 70%-30%, or 85%-15%. Amblyopic (n = 12) and control (n = 12) participants reported the perceived motion direction. Results: Both groups showed broad temporal tuning of flicker integration. Accuracy in the contrast-balanced condition was highest with no added phase delay, and Gaussian fits to the data showed peak performance at a negligible delay (-0.3 degrees). Across both groups, an 85%-15% contrast disparity reduced overall accuracy and shifted peak accuracy to a delay of 28.6 degrees in the higher contrast eye. Conclusions: Our data demonstrate that interocular temporal and contrast disparities interact similarly in normally sighted individuals and those with amblyopia. In this task, temporal delays and contrast imbalance manipulations interact predictably, such that the effects of imbalanced contrast are counteracted by a leading temporal offset for the eye with the lower contrast stimulus.
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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".