Bibliographic record
Abstract
PURPOSE: The well-documented fact that visual acuity and contrast sensitivity in amblyopia are attenuated at high spatial frequencies predicts that amblyopes should perceive objects as blurred, because they do not have the high spatial frequency information necessary to represent sharp edges adequately. In the current study, the representation of blur in amblyopia with blur-discrimination and blur-matching tasks was explored in a series of experiments. METHODS: Monocular blur-discrimination thresholds were measured in a spatial two-alternative forced-choice procedure. Observers were required to discriminate which edge (right or left) appeared to be the lesser blurred. Observers also interocularly matched edges that were identical with those used in the blur-discrimination tasks, with the exception that they were viewed dichoptically at all times. RESULTS: Blur-discrimination thresholds were elevated in both the amblyopic and fellow fixing eyes but were within the normal range for interocular matching thresholds. CONCLUSIONS: The results suggest that blur is veridically represented in the amblyopic visual system. The surprising result is that all amblyopes, even those with the most severe visual loss, veridically matched all blurred edges, including the sharpest ones. This implies that amblyopes are able to represent levels of blur that are defined by spatial structure beyond their resolution limit.
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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 | 0.003 |
| 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 | 0.001 |
| 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".