Perceived group size is determined by the centroids of the component elements
Bibliographic record
Abstract
To accomplish the deceptively simple task of perceiving the size of objects in the visual scene, the visual system combines information about the retinal size of the object with several other cues, including perceived distance, relative size, and prior knowledge. When local component elements are perceptually grouped to form objects, the task is further complicated because a grouped object does not have a continuous contour from which retinal size can be estimated. Here, we investigate how the visual system solves this problem and makes it possible for observers to judge the size of perceptually grouped objects. We systematically vary the shape and orientation of the component elements in a two-alternative forced-choice task and find that the perceived size of the array of component objects can be almost perfectly predicted from the distance between the centroids of the component elements and the center of the array. This is true whether the global contour forms a circle or a square. When elements were positioned such that the centroids along the global contour were at different distances from the center, perceived size was based on the average distance. These results indicate that perceived size does not depend on the size of individual elements, and that smooth contours formed by the outer edges of the component elements are not used to estimate size. The current study adds to a growing literature highlighting the importance of centroids in visual perception and may have implications for how size is estimated for ensembles of different objects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".