How far away is your phone in this picture? Determining object distance and size in a 2D scene
Bibliographic record
Abstract
How do we judge the distance of familiar objects seen within a picture? If the physical size is known, the object’s distance can be judged relative to other objects in the scene, but without such references the viewer must estimate its distance solely from their knowledge of its size. How well can we do that? Using remote testing via PAVLOVIA, participants viewed a rectangular block, scaled to the size of their personal smartphone, placed upright in a hallway scene that provided ambiguous distance information, displayed on a computer screen viewed at 50cm. In exp1 (n=74), they were given 5 sizes of block and adjusted their distances to look correct based on the known physical phone size. In a second task, they adjusted its visual size when it was placed at the same distances they had set in the first task. In exp2 (n=60), the task order was reversed, and distances were also given in meters. Participants could differentiate the distances and sizes of their phone in the correct order (small farthest) but consistently set its size larger than expected (p < .001). When setting its size at the distance they had previously chosen as consistent with a given size, they surprisingly set it significantly larger (p < .001). In exp2, where they set size first, the distances set in task 2 were not significantly different from task 1 (p = .936). Participants could not use distance given in meters. Our results suggest that people misjudge the visual size of a familiar object based on its perceived distance, setting it much too big. When determining object distance based on visual size, however, they are quite consistent. The perceptual relationship between size and distance seems to break down when an object of known physical size is placed in a 2D scene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".