Local and global components of texture-surround suppression of contour-shape coding
Bibliographic record
Abstract
Evidence that contour-shapes and texture-shapes are processed by different mechanisms included the finding that contour-shape aftereffects are reduced when the adaptation stimulus is a texture made of contours rather than a single contour. This phenomenon has been termed texture-surround suppression of contour-shape, or TSSCS. How does TSSCS operate and over what spatial extent? We measured the postadaptation shift in the apparent shape frequency of a single sinusoidal-shaped contour as a function of the number of contours in the adaptor stimulus. Contours were Gabor strings in which the Gabor orientations were either tangential (snakes) or orthogonal (ladders) to the path of the contour. We found that for extended surrounds, the aftereffect was strongly reduced when the surround contours were the same as the central adaptor contour, but not when the Gabors making up the surround contours were opposite-in-orientation to those of the central adaptor. For near surrounds, the aftereffect in a snake contour was unaffected by same-orientation but strongly suppressed by opposite-orientation surrounds, whereas the aftereffect for a ladder-contour was suppressed equally by both same- and opposite-orientation near surrounds. Finally, the strength of surround suppression decreased gradually with increasing spatial separation between center and surround. These results indicate that there are two components to texture-surround suppression in our shape aftereffect: one that is sensitive to opposite-orientation texture surrounds, operates locally, and disrupts contour-processing; the other that is sensitive to same-orientation texture surrounds, is spatially extended, and prevents the shape of the contour from being processed as a contour. We also demonstrate that the observed shape aftereffects are not due to changes in the apparent shape-frequency of the adaptors or the precision with which their shape-frequency is encoded, indicating that TSSCS is not an instance of crowding.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".