Bibliographic record
Abstract
Perceptual organization of natural scenes is effortless and immediate despite fragmentation and obscuration of object boundaries caused by occlusions and clutter. The standard model for boundary grouping is based upon an 'association field' that governs local grouping, and a Markov or transitivity assumption that allows global contours to be inferred solely from local properties. Psychophysical 'path detection' experiments have revealed sensitivity to local geometry consistent with this association field hypothesis. Recently, however, it has been shown that detection of natural animal shapes is more efficient than detection of 'metamer' contours that match the natural contours in their local geometry but contain no global regularities (Elder et al, 2010, J Vis, 10(7):1171). This suggests that global shape also plays a role in contour grouping. Yet, since these metamer controls are open contours, the findings may derive purely from the closure of the natural shapes. To address this issue, we employ a novel MCMC methodology (Fründ & Elder, 2013, J Vis, 13(9):119) to produce closed metamer contours, statistically matched in their local geometry to natural animal shapes, but otherwise maximum entropy, containing no global regularities beyond closure. We conducted path detection experiments using these 3 stimuli (open metamers, closed metamers, natural shapes) at different contour sampling rates (number of elements per contour), and estimated noise threshold for criterion performance. At low sampling rates, performance was similar for the three conditions. However, as sampling rate was increased to 20 elements per contour, performance for natural shapes and closed metamers exceeded performance for open metamers (t(7)>2.4, p<0.04), and at 40 elements, performance for natural shapes exceeded performance for closed metamers (t(10)=2.6, p=0.02). These results show that the visual system exploits both closure and additional global properties of natural shape in the segmentation of bounding contours from cluttered scenes, challenging purely local accounts of contour grouping. Meeting abstract presented at VSS 2014
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 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".