Bibliographic record
Abstract
We have recently reported that natural contours induce perceptual distortions in neighbouring regions of visual space (Goren & Elder VSS2013). However, the magnitude of these distortions has yet to be quantified, and the precise conditions necessary to generate them remain unclear. Here we employ a new quantitative method to measure the size of these perceptual distortions, and systematically vary the complexity of the inducing contours to determine whether contour shape modulates their genesis. Methods. The stimulus consisted of a collinear triplet of dots orthogonal to a nearby contour. Observers used a mouse to move the central dot along the virtual line connecting the flankers until it was perceived to bisect them. 11 positions of the dot probe were evaluated, ranging from one side of the contour to the other. Contours included horizontal and vertical lines and line segments, circles and arcs of circles, as well as natural animal shapes. Results. The induced distortion of perceptual space was found to be highly similar for both simple and complex contours. Generally, space was perceived as compressed in the immediate vicinity of the contour and expanded at intermediate distances, and the magnitude of these distortions generally peaked at roughly 7 arcmin, or 0.8% of the half-width of our 2.8 deg dot probe. Space was generally undistorted at points distant from the contour. These findings suggest that the observed distortions may be determined by local properties of contours rather than global shape. Meeting abstract presented at VSS 2015
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".