Orientation tuned curvature detectors revealed by the shape-amplitude after-effect
Bibliographic record
Abstract
Aim: Contour shape after-effects have been used to reveal the mechanisms that process and represent curvature-defined shape. Here we use the shape-amplitude after-effect, or SAAE, to explore whether curvature detectors are tuned for the overall orientation of a curve. Methods: We measured the perceived amplitude of curved contours in the upper and lower visual fields as a function of the orientation of adapting contours, which were respectively higher and lower in amplitude than the test. Results: SAAEs (i) are greatest when the adaptor and test are the same orientation, (ii) decrease rapidly as the orientation of the adapting contours is rotated away from the test, the data being well fit by a Gaussian function with a standard deviation of 15°, (iii) increase again when the adapting contours are rotated 180° relative to the test contours. The increase at 180° is not consistent with curvature opponency. Control experiments show that the shape of the tuning function cannot be explained by local orientation adaptation. Conclusions: Curvature encoding mechanisms are tuned for orientation. The slight increase in SAAEs when adaptor and test differ by 180° could be explained by the combined operation of polarity-selective and polarity-non-selective curvature mechanisms. The results are discussed in relation to recent psychophysical and physiological models of form 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 | 0.001 |
| 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".