AB102. Image blur perception in amblyopia: beyond edges
Bibliographic record
Abstract
Background: Understanding the neurophysiological mechanisms of Amblyopia, a neurodevelopmental disorder of the visual cortex, will bring us closer to full recovery. Past findings have been contradictory. Results have shown that despite having severe acuity impairment, amblyopes can nonetheless perceive sharp edges. In this study, we explore the representation of blur through a series of image blur-discrimination and matching tasks, to understand more about the amblyopes’ visual system. Methods: Monocular image blur-discrimination thresholds were measured in a spatial two-alternative forced-choice procedure whereby subjects had to decide which image was the blurriest. Subjects also had to interocularly match pictures that were identical to those used for the image blur discrimination task. Ten amblyopes, as well as a group of ten controls were under study. Results: Data on amblyopes and controls will be presented for both experiments. According to previous research that was done on blur-edge discrimination and matching, we predict that subjects’ performance will follow a dipper function, that is, all observers will be better at discriminating between both images when a small amount of blur is applied rather than when the image is either sharp or very blurry. We also predict that amblyopes’ blur discrimination will be noisier, but that they will paradoxically be able to match the sharpness of the images presented in the matching task. Conclusions: This would confirm our hypothesis about amblyopes’ visual system, that they can represent blur levels defined by spatial frequencies that are beyond their resolution limit, and would also raise interesting questions about the visual system in general regarding the different perceptions driven by images versus edges.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".