Monocular eye-cueing shifts eye balance in amblyopia
Bibliographic record
Abstract
Here, we investigate the shift in eye balance in response to monocular cueing in adults with amblyopia. In normally sighted adults, biasing attention toward one eye, by presenting a monocular visual stimulus to it, can shift eye balance toward the stimulated eye, as measured by binocular rivalry. We investigated whether we can modulate eye balance by directing monocular stimulation/attention in adults with clinical binocular deficits associated with amblyopia and larger eye imbalances. In a dual-task paradigm, eight participants continuously reported ongoing rivalry percepts and simultaneously performed a task related to the cueing stimulus. Time series of eye balance dynamics, aligned to cue onset, are averaged across trials and participants. In different time series, we tested the effect of monocular cueing on the amblyopic and fellow eyes (compared to a binocular control condition) and the effect of an active versus passive task. Overall, we found a significant shift in eye balance toward the monocularly cued eye, when both the fellow eye or the amblyopic eye were cued, F(2, 14) = 27.649, p < 0.01, ω2 = 0.590. This was independent of whether, during the binocular rivalry, the cue stimulus was presented to the perceiving eye or the non-perceiving eye. Performing an active task tended to produce a larger eye balance change, but this effect did not reach significance. Our results suggest that the eye imbalance in adults with binocular deficits, such as amblyopia, can be transiently reduced by monocularly directed stimulation, at least through activation of bottom-up attentional processes.
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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 | 0.000 |
| 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".