Bibliographic record
Abstract
In many studies of contour integration, the task is to detect a contour consisting of spatially separated Gabor elements positioned along a smooth path (e.g., Field, Hayes, & Hess, 1993, Vision Research, 33, 173–193). The elements can be aligned with the path (“snakes”) or perpendicular to it (“ladders”). With foveal viewing, ladders are generally harder to detect than snakes but, as long as they are fairly straight, ladders can still be detected quite easily. We found a striking deficit in detection of ladders in the periphery. Completely straight ladders were undetectable at an eccentricity of 6 degrees of visual angle, whereas performance on straight snakes at this eccentricity was at or close to 100%. This suggests that ladder detection is disproportionately impaired in the periphery, but an alternative explanation is that there is a general impairment of ladder detection that only shows up in the periphery, where performance falls away from ceiling. To address this issue, we brought performance away from ceiling in the fovea by jittering the orientations of the elements. For two subjects, foveal performance was matched for snakes and ladders with the same orientation jitter levels. In both cases, detection of ladders fell to chance at an eccentricity of 4 deg, whereas detection of snakes remained significantly above chance up to and including the largest eccentricity that we tested (8 deg). The failure to detect ladders at such small eccentricities may partly explain the relative difficulty in detecting ladders that has been reported in previous studies: in all of these studies, the position of the contour has been randomized to some extent. The difference in the effect of eccentric viewing on snakes and ladders means that any positional randomization would have caused a greater disruption to detection of ladders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".