Grouping of shape by perceptual closure: Effects of spatial proximity and collinearity
Bibliographic record
Abstract
Time course of grouping of shape by perceptual closure as a function of spatial proximity and collinearity between the closure-inducing fragments was examined in three experiments using primed-matching. When only closure was available, early priming of the global shape was observed for spatially close fragments, but not for spatially distant fragments. When closure and collinearity were available, the global shape of both spatially close and spatially distant fragments was primed at brief exposures. These results indicate that spatial proximity is critical for the rapid grouping of shape by perceptual closure in the absence of collinearity, but collinearity facilitates the rapid grouping of shape when the closure-inducing line segments are spatially distant. These findings suggest a rapid computation of collinearity between closure-inducing line segments that is insensitive to spatial proximity within a certain range. This fast-occurring mechanism enables efficient image descriptions and apparently is crucial for a fast, reliable interpretation of the visual scene. The results also showed, however, that stable priming effects of the global shape over time were observed only when the closure-inducing fragments were collinear and spatially close, suggesting that maintaining a stable representation of shape beyond the first stages of visual processing depends both on spatial proximity and collinearity between the closure-inducing fragments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".