Spatial properties of texture-surround suppression of contour-shape coding
Bibliographic record
Abstract
Aim. Although evidence suggests that contour-shapes and texture-shapes are processed by different mechanisms, they nevertheless interact in an important way. Specifically, textures can inhibit the processing of the shapes of contours they surround; this is termed ‘texture-surround suppression of contour-shape’. How does this suppression operate and what is its spatial extent? Method. Subjects adapted to pairs of sinusoidal-shaped textures or of single contours that differed in shape frequency, and the resulting shift in the apparent shape-frequency of single-contour test pairs was measured. All contours consisted of strings of Gabor microelements that were oriented either parallel (‘snakes’) or perpendicular (‘ladders’) to the path of the contour. The texture adaptors consisted of a central contour and a surround made of a series of contours arranged in parallel. We varied (i) the number of contours in the surround-texture and (ii) the orientation of Gabors in the texture-surround relative to the central-contour. Results. We found that (i) for extended texture-surrounds, the coding of snake contour-shapes is strongly suppressed by snake surrounds, and ladder contours by ladder surrounds, but the suppression is much reduced if the center and surround contours are of opposite type. (ii) Both snake and ladder surrounds with 7 contours or less have the same suppressive effect on a ladder contour. (iii) Near ladder-surrounds suppress the coding of snake contour-shapes more than do near snake-surrounds. Conclusion. There are two components to texture-surround suppression: one operates locally, is broadband in orientation and disrupts contour-linking, the other is spatially extended and prevents the shape of the contour from being processed as a contour.
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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) | 0.000 | 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) | 0.001 | 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".