Regaining Visual Acuity Does Not Restore Motion Extrapolation Deficits in Amblyopia
Bibliographic record
Abstract
Purpose: We previously reported that amblyopes exhibit deficits in motion extrapolation and in correcting for overextrapolation. In this study, we explored whether these motion deficits remain when normal visual acuity is restored after successful standard treatment in adults with former unilateral amblyopia. Methods: Eleven clinically treated adult amblyopes and 11 control subjects participated in the study. We assessed visual motion processing functions by using two motion illusion paradigms: the flash-lag effect (FLE) and the flash-grab effect (FGE). We measured the monocular FLE and FGE magnitudes for all participants. Two contrast conditions (0.2 and 1) of FLE and two spatial frequencies (2 and 8 cycles) of FGE were tested. Results: Compared to controls, treated amblyopes still exhibited a smaller FLE magnitude at the 0.2 contrast (F1,20 = 5.69, P = 0.027) in both the former fellow eye (FFE) and the former amblyopic eye (FAE). Treated amblyopes had a larger FGE magnitude than controls (P ≤ 0.008) in both eyes, and the FGE magnitude of FAE was larger than that of FFE (F1,20 = 15.9, P = 0.003). The FLE and FGE magnitudes were significantly correlated in most conditions in controls (P ≤ 0.035) but not in treated amblyopia (P ≥ 0.201). Conclusions: We observed a smaller FLE and a larger FGE in treated amblyopes, suggesting that the motion extrapolation and the correction for extrapolation remain impaired in their brain. These may be due to processing delays and defective temporal integration from residual abnormal cortical connections in the amblyopic visual system.
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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".