Different mechanisms encode the shapes of contours and contour-textures
Bibliographic record
Abstract
Aim. It is often assumed that curved contours, and textures made from parallel curved contours, are processed by the same mechanism. However, recent evidence from primate neurophysiology and brain-imaging studies suggests that contours and textures might be processed by different mechanisms. We used an adaptation paradigm to test whether the shapes of contours and contour-textures were encoded by the same or by different mechanisms. Method. Subjects adapted to pairs of sinusoidally-shaped contours or contour-textures. The two stimuli from each pair were an octave apart in shape frequency and presented above and below fixation. During the test phase, subjects indicated which of two test contours/contour-textures had the highest shape frequency, and an adaptive procedure found their PSE. Results. Adaptation to contours produced significant shifts in the perceived shape frequency of contours, but relatively little shift in the perceived shape frequency of contour-textures. Adaptation to contour-textures produced significant shifts in the perceived shape frequency of textures, but relatively little shift in the perceived shape frequency of contours. Conclusion. The shapes of contours and contour-textures are encoded by different mechanisms.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".