Orientation tuning of curvature adaptation reveals both curvature-polarity-selective and non-selective mechanisms
Bibliographic record
Abstract
We have used a curvature after-effect, or CAE, to explore whether curvature detectors are tuned for the overall orientation of a curve. CAEs were measured for half-cycle cosine-shaped contours as a function of adaptor contour orientation for a fixed test contour orientation. CAEs (i) were greatest when the adaptor and test contours had the same orientation, (ii) decreased rapidly as the orientation of the adapting contours rotated away from the test, the data being well fit by a Gaussian function with a standard deviation of 16 degrees , (iii) increased again to a secondary peak when the adapting contours were rotated 180 degrees relative to the test. Control experiments showed that the shape of the curvature-orientation tuning function could not be explained by local orientation adaptation, and that instead curvature encoding mechanisms are tuned for orientation. The secondary peak in the CAE at 180 degrees is argued to be inconsistent with curvature opponency and instead a result of the combination of polarity-selective and polarity-non-selective curvature mechanisms. The results are discussed in relation to recent psychophysical and physiological models of form processing and the possible significance of the findings with regard to symmetry 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 | 0.001 |
| 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".