Closing the Gap: Sensitivity to Real and Illusory Contours in Patients treated for Bilateral Congenital Cataracts
Bibliographic record
Abstract
Early visual deprivation impairs many aspects of vision, including the perception of global form and motion (Lewis et al., 2002; Hadad et al., submitted). We examined the effect of abnormal early visual input on later sensitivity to shapes formed by real and illusory contours. Patients treated for bilateral congenital cataracts (M age = 21.6 years, range = 12.8–30.1 years; n = 9) and comparably aged visually normal controls (n = 14/age group) discriminated fat from skinny shapes formed by real and illusory contours at high support (0.5) and low support (0.2) ratios (the ratio of the physically specified contour to the total edge length). The angle of rotation of the shapes' corners increased or decreased over trials, producing fatter or skinnier shapes. We defined threshold as the smallest angle of rotation for which the shape was discriminated accurately as fat or skinny. Testing was monocular, with the patients' results divided into those for the better and worse eye, as defined by acuity and alignment history. Z-scores, based on age-appropriate norms, indicated a large deficit in the perception of both real and illusory contours in the worse eye (all ps < 0.05) but not in the better eye (all ps > 0.10) of deprived patients. To account for the deficit in processing real contours, we calculated interpolation cost (interpolated contour minus real contour divided by real contour). For each eye of patients and controls, we found a significant effect of support ratio, with higher support associated with lower cost (all ps < 0.001). However, interpolation cost did not differ significantly between patients and controls for either eye (all ps > 0.10). Therefore, although patients with early visual deprivation show overall deficits in sensitivity to shape, there is no additional loss in sensitivity to shapes formed by illusory contours.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".