Global Factors in Perceptual Shape Completion
Bibliographic record
Abstract
Humans are known to rely profoundly on bounding contours for object detection, segmentation and recognition. This task is complicated by the interposition of other objects in the visual field that lead to occlusion: partial blocking of one object by another. However, the impact of occlusion on human perception is mitigated by the human ability to perceptually complete partially occluded bounding contours, i.e., to fill-in the missing shape information. Here we examine the degree to which the human brain uses local and non-local cues to solve this perceptual completion task. Each visual stimulus consisted of a sequence of dots regularly sampling the outline of a 2D shape. To simulate occlusion, a contiguous interval of 10-50% of the dot pattern was extinguished. Observers were asked to adjust a probe dot along a linear axis orthogonal to the gap until the dot appeared to lie where the contour would be, were it visible. Two classes of shape were employed: animal shapes, which afford both local and global cues to completion, and metamer shapes, which match the curvature statistics of the animal shapes but are otherwise random, thus affording local but not global cues to completion. Mean absolute error was lower for animal shapes than for metamer shapes and, while completions tended to be negatively (inward) biased for both, the bias was less for animals than metamers. Local linear and elastica models were better able to account for human completions of metamer shapes than animal shapes. Together these findings point to a contribution of non-local shape cues to perceptual contour completion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".