Contour Grouping and Natural Shapes: Beyond Local Cues
Bibliographic record
Abstract
The perception of boundary shape depends upon the organization of local orientation signals into global contours. Models generally assume that grouping is based upon local Gestalt relationships such as proximity and good continuation. While there have been reports that the global property of contour closure is involved in this process (e.g., Kovacs & Julesz 1993), a more recent study suggests otherwise (Tversky, Geisler & Perry 2004). This raises the question: is contour grouping completely insensitive to global properties of the stimulus, depending only upon local Gestalt cues? To address this question, we conducted a psychophysical experiment in which observers were asked to detect briefly-presented target contours in noise. Contours were represented as sequences of short line segments, and the noise was composed of randomly positioned and oriented segments of the same length. We used QUEST to estimate the threshold number of noise elements at 75% correct performance in a present/absent task. Three conditions were tested. In Condition 1, targets were the closed bounding contours of 391 animal shapes derived from the Hemera object database. These contours afford local Gestalt properties but also a host of global properties, including closure. In Condition 2, we created first-order metamers of these contours by randomly shuffling the order of the angles between neighbouring segments. This preserves all local Gestalt properties exactly, but destroys all higher-order properties. In Condition 3, we also randomized the signs of the angles, thus removing a convexity bias. While noise thresholds were similar for Conditions 2 and 3, they were significantly higher for Condition 1, suggesting a global influence on grouping. Further analysis suggests that this difference cannot be explained by differences in stimulus eccentricity, element density, or contour intersections produced in shuffled stimuli. Instead the results point to a process of perceptual organization that goes beyond local, first-order cues.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".