Luminance-contrast properties of contour-shape processing revealed through adaptation
Bibliographic record
Abstract
Aim: We investigated the nature of 1st-order inputs to contour-shape mechanism using the shape-frequency after-effect (SFAE), in which adaptation to a sinusoidally-modulated contour causes a shift in the apparent shape-frequency of a test contour away from that of the adapting stimulus. We measured SFAEs for adapting and test contours that differed in the phase, scale (or blur) and magnitude of luminance contrast. Methods: Adapting and test stimuli were pairs of 2D sinusoidal-shaped contours and edges. The adapting pair were presented above and below fixation and differed in shape frequency by a factor of three. During the test period, subjects indicated whether the upper or lower test contour had the higher perceived shape-frequency, and a staircase procedure estimated the ratio of test shape-frequencies at the point of subjective equality. Results: SFAEs revealed (i) selectivity to luminance contrast polarity for both even-symmetric (contours only) and odd-symmetric (both contours and edges) luminance profiles; (ii) a degree of selectivity to luminance scale (or blur); (iii) higher selectivity to fine-scale (thin) compared to coarse-scale (thick) contours/edges and (iv) a small preference for equal-in-contrast adaptors and tests. Conclusion: Contour/edge shape encoding mechanisms are tuned to many luminance-contrast properties. This implies that contour shape mechanisms make use of a ’feature-rich’ representation, and do not represent contour shapes as super-sparse cartoon-like sketches as might be presumed by local energy, i.e. non-phase-selective models.
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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 | 0.001 |
| 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".