Delayed Correction for Extrapolation in Amblyopia
Bibliographic record
Abstract
Purpose: It has been suggested that amblyopes present impaired motion extrapolation mechanisms. In this study, we used the flash grab effect (FGE), the illusory mislocalization of a briefly flashed stimulus in the direction of a reversing moving background, to investigate whether the amblyopic visual system can correct overextrapolation. Methods: Thirteen amblyopes and 13 control subjects participated in the experiment. We measured the monocular FGE magnitude for each subject. Two spatial frequency (2 and 8 cycles), two texture configurations (square wave or sine wave), and two speed conditions (270 degrees/s and 67.5 degrees/s) were tested. In addition, control subjects were further tested in reduced luminance conditions. Results: Compared with controls, amblyopes exhibited a larger FGE magnitude both in their fellow eye (FE) and amblyopic eye (AE). The FGE magnitude of their AE was significantly larger than that of the FE. In a control experiment, we observed that the FGE magnitude increases with the decreasing of the luminance. The FGE magnitude of amblyopes fall into the same range as that of controls under reduced luminance conditions. Conclusions: We observed a lager FGE in patients with amblyopia, which indicates that the amblyopic visual system does not accurately correct the overextrapolation when a moving object abruptly reverses its direction. This spatiotemporal processing deficit could be ascribed to delayed visual processing 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".