Spatial properties of curvature encoding revealed by the shape-frequency and shape-amplitude after-effects
Bibliographic record
Abstract
Aim: The shape-frequency and shape-amplitude after-effects, or SFAE and SAAE, are the phenomena in which adaptation to a sine-wave-shaped contour causes a shift in respectively the apparent shape-frequency and shape-amplitude of a test contour in a direction away from that of the adapting stimulus. We have examined whether the SFAE and SAAE manifest selectivity to (a) local curvature, (b) curvature polarity (or sign), and (c) whether the contours were selective to local orientation. We also investigated (d) whether curvature encoders are arranged in a curvature-opponent manner and (e) whether the high- and low-shape-frequency shape components of complex shapes are processed independently or not. Methods: These included measuring SFAEs/SAAEs for adapting and test contours that were either the same or different in a given spatial property (e.g. same-polarity or opposite-polarity half-wave rectified sinusoidal curves to test for curvature-polarity specificity) the rationale being that if the after-effects were smaller when adaptor and test differed along a particular spatial property then curvature encoders must be selective for that property. Results: SFAEs and SAAEs (i) are mediated by mechanisms sensitive to contour fragments that have a constant sign of curvature (i.e. half-a-cycle of the test contour in ± cosine phase); (ii) show a degree of selectivity to curvature polarity (or sign); (iii) show a degree of selectivity to local orientation; (iv) reveal some evidence for curvature-opponency, and (v) reveal that the high and low shape-frequency shape components of a complex shape are separately adaptable. Conclusion: Curvature is encoded by mechanisms that are selective to a variety of spatial properties.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".