Bibliographic record
Abstract
The curvature of a smooth curve in the plane is defined mathematically as the rotation of the tangent vector over an infinitesimal interval of the curve, but what is perceptual curvature? It must be something different, as the eye cannot resolve infinitesimals. Here we seek to understand how humans perceive the local curvature of natural shapes. Five observers viewed a sequence of white outline animal shapes at two viewing distances (75cm and 150cm). Each shape was scaled to have standard deviation of 1.56deg at 75cm. A random point on each shape was highlighted and the observer judged the curvature at this point. To avoid obscuring the contour, the judgement was made by placing a red dot at the perceived centre of curvature. We considered three models of perceptual curvature, each a function of an interval of the contour centred at the point of interest, based on the turning angle formed by the point of interest and the bounding points of the interval, and the sine and tangent of this angle. We first determined the subset of trials on which the signs of objective and perceptual curvature agreed, and then analyzed the correlation between the magnitudes of objective and perceptual curvature for this subset, as a function of the neighbourhood size. We found the turning angle model to be most predictive of perceptual curvature, with an optimal contour neighbourhood of 29arcmin at a viewing distance of 75cm. We expected the optimal neighbourhood at 150cm viewing distance to be either constant in a retinal frame (29arcmin) or in an object frame (14.5arcmin). What we found was intermediate (20arcmin), suggesting influence by both physiological and contextual factors. Meeting abstract presented at VSS 2016
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".