Mind the Gap: The Effect of Support Ratio and Retinal Size on Contour Interpolation
Bibliographic record
Abstract
Adults see bounded figures even when local image information fails to specify the contours, such as in cases of partial occlusion and illusory contours. Here, we examined the effects of support ratio (the ratio of the physically specified contour to the total edge length) and absolute size on interpolation strength. In Experiment 1, adults (n = 24) discriminated fat from skinny shapes formed by real contours, partially occluded contours, or illusory contours. Across conditions, support ratio and absolute size were varied. We formed fat and skinny shapes by rotating the corners of the shape (Ringach & Shapley, 1996). In a 3-down, 1-up staircase procedure, the angle of rotation of the corners increased or decreased over trials, producing various curvatures of the shape. The strength of interpolation was measured by the smallest angle of rotation of the corners for which the shape was discriminated accurately as fat or skinny. Interpolation was better for higher support ratios (p<0.001), and had more effect on illusory than on partially occluded contours (p<0.01). Thresholds were affected minimally by changes in size, except for worse thresholds for the smallest size at the lowest support ratio (p<0.001). In Experiment 2, we used a subset of conditions to test children aged 6- and 9-years (n = 24 per age and contour type) and adults (n = 12 per contour type tested to date) with partially occluded and illusory contours. Preliminary results indicate that, in contrast to adults who show greater effects of support ratio for illusory than partially occluded contours, the interpolation of both 6- and 9-year-olds was affected equally by support ratio for the two types of contours (ps>0.40). Thus, interpolation of contours is still immature at 9 years of age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".