Bibliographic record
Abstract
There currently exists an extensive body of literature devoted to understanding how the visual system integrates spatially segmented elements into contours, either artificially generated, or modeled after the contour statistics of natural scene imagery. However, little is known about the orientation tuning of the integration mechanism and whether such tuning changes as a function of contour curvature. To address those issues, we employed stimuli consisting of texture fields made up of pseudo-randomly distributed band-pass filtered noise elements (band-pass in spatial frequency and orientation), some of which, by virtue of their orientation alignment, formed a contour path. The orientation bandwidth of all filtered noise elements was varied, and the local spatial orientation misalignment (element-to-path angle) between the local element orientation and the contour path itself was systematically manipulated. The task consisted of a standard psychophysical 2AFC paradigm where observers were required to indicate which stimulus interval contained a contour. The results indicated that the local element orientation bandwidth needed to integrate low curvature contours was quite broad (ranging between 40° to 60°). For contours possessing a high degree of curvature, this bandwidth was significantly narrower (ranging between 20° to 30). However, the element-to-path angle varied very little as a function of contour curvature, ranging between 15° to 25° for all curvatures. The results indicate that the local element orientation tuning of the human visual contour integration mechanism is dependent on contour curvature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".