Contour inflections are adaptable features
Bibliographic record
Abstract
An object's shape is a strong cue for visual recognition. Most models of shape coding emphasize the role of oriented lines and curves for coding an object's shape. Yet inflection points, which occur at the junction of two oppositely signed curves, are ubiquitous features in natural scenes and carry important information about the shape of an object. Using a visual aftereffect in which the perceived shape of a contour is changed following prolonged viewing of a slightly different-shaped contour, we demonstrate a specific aftereffect for a contour inflection. Control conditions show that this aftereffect cannot be explained by adaptation to either the component curves or to the local orientation at the point of inflection. Further, we show that the aftereffect transfers weakly to a compound curve without an inflection, ruling out a general compound curvature detector as an explanation of our findings. We assume however that there are adaptable mechanisms for coding other specific forms of compound curves. Taken together, our findings provide evidence that the human visual system contains specific mechanisms for coding contour inflections, further highlighting their role in shape and object coding.
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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 | 0.003 |
| 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".