Chromatic properties of texture-shape and of texture-surround suppression of contour-shape mechanisms
Bibliographic record
Abstract
Contour-shape coding is color selective (Gheorghiu & Kingdom, 2007a) and surround textures inhibit the processing of contour shapes (Gheorghiu & Kingdom, 2011; Kingdom & Prins, 2009). These two findings raise two questions: (1) is texture-surround suppression of contour shape color selective, and (2) is texture-shape processing color selective? To answer these questions, we measured the shape-frequency aftereffect using contours constructed from strings of Gabors defined along the red-green, blue-yellow, and luminance axes of cardinal color space. The stimuli were either single sinusoidal-shaped contours or textures made of sinusoidal-shaped contours arranged in parallel. We measured aftereffects for (A) single-contour adaptors and single-contour tests defined along the same versus different cardinal directions, (B) texture adaptors and single-contour tests in which the central-adaptor contour/single-contour test and surround adaptor contours were defined along the same versus different cardinal directions, and (C) texture adaptors and texture tests defined along same versus different cardinal directions. We found that color selectivity was most prominent for contour-shape processing, weaker for texture-surround suppression of contour-shape processing, and absent for texture-shape processing.
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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) | 0.002 | 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".