The size of objects in visual space compared to pictorial space
Bibliographic record
Abstract
The visual space in front of our eyes and the pictorial space that we see in a photo or painting of the same scene behave differently in many ways. Here, we used virtual reality (VR) to investigate size perception of objects in visual space and their projections in the picture plane. We hypothesize that perceived changes in the size of objects that subtend identical visual angles in pictorial and visual space are due to the dual nature of pictures: The flatness and location of the picture “cross-talks” (Sedgwick, 2003) with the perception of the depicted three-dimensional space. If the picture is at distance dpic and the depicted object at dobj, size-distance relations influence perceived relative sizes. The picture is expected to be scaled by a factor c*(dobj / dpic − 1) + 1 to match the object, where c is a constant between 0 and 1. In a VR environment, eight participants toggled back and forth between a view of an object seen through a window in an adjacent room, and a picture that replaced the window. Participants adjusted the picture scale to match the size of the object through 60 trials varying dobj and dpic. A multilevel regression indicated that the above model does not hold. Rather, we found a striking asymmetry between the roles of object and picture. If dobj was greater than dpic (object behind picture) then c was 0.005 (t(7) = 7.80, p < 0.001). In contrast, if dobj was less than dpic (object in front of picture), c was 0.33 (t(7) = 3, p < 0.001). We discuss this result in the context of a number of different theories that address the particular nature by which the flatness of the picture plane influences the perception of pictorial space.
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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 | 0.013 |
| 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 | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".