Dichoptic Attentive Motion Tracking is Biased Toward the Nonamblyopic Eye in Strabismic Amblyopia
Bibliographic record
Abstract
Purpose: To determine whether attention is biased toward the nonamblyopic eye under binocular viewing conditions in adults with anisometropic or strabismic amblyopia. We first determined whether attention could be allocated preferentially to one eye in visually normal observers performing a dichoptic attentive motion tracking task. We then assessed dichoptic attentive motion tracking in amblyopia. Methods: Participants performed a multiple-object tracking task under the following three viewing conditions: target dots to the dominant eye and distractor dots to the nondominant eye (DE condition), vice versa (NDE condition), or all dots to both eyes (binocular condition). Interocular attentional asymmetry scores were computed as the difference in accuracy between DE and NDE conditions. An interocular contrast difference favoring the amblyopic eye was used for all conditions to neutralize amblyopic eye suppression. To test for confounding effects of suppression, participants completed a separate dot enumeration task under dichoptic presentation conditions to obtain an interocular enumeration asymmetry score. Results: Participants with normal vision demonstrated similar accuracy between the DE and NDE conditions and exhibited slightly impaired performance under dichoptic compared with binocular viewing conditions. Participants with strabismic/mixed amblyopia had significantly higher interocular attentional asymmetry than participants with normal vision or with anisometropic amblyopia, whereby attention was biased toward the nonamblyopic eye. The latter two groups did not exhibit a bias in interocular attention. No interocular asymmetries for the enumeration task were observed for any group. Conclusions: A nonamblyopic eye bias in the interocular allocation of attention may contribute to the binocular vision impairments caused by strabismic amblyopia.
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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".