The roles of polarity and symmetry in the perceptual grouping of contour fragments
Bibliographic record
Abstract
We describe two experiments that investigate the roles of polarity and symmetry in the perceptual grouping of contour fragments. Observers viewed, for one second on each presentation, arrays of oriented, spatial-frequency band-pass, elements, in which a subset of the elements was aligned along a twisting curve. In each of five conditions we measured observers' ability to detect aligned combinations of even- and odd-symmetric elements, of the same and different polarities, against a background of 'noise' elements. As with previous experiments we found that the 'path' could be reliably detected, even when the elements of the path were oriented at angles of up to +/- 60 deg relative to each other. Detection of the path was still possible when the polarity of path elements alternated. However, the probability of detection of the path was raised significantly when the path elements were all of the same polarity. Perceptual grouping of even-symmetric elements was no different to perceptual grouping of odd-symmetric elements. The results provide evidence, that in achieving integration of contour fragments, the visual system uses a process that is to some degree phase selective. We use the results to describe how the visual system may resolve natural contours when they occur against backgrounds that vary over a wide range of intensities. The data presented here have been published in conference-abstract form (Hayes et al., 1993; Field et al., 1997).
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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 | 0.001 | 0.010 |
| 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 | 0.002 |
| 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".