Can people determine object distance from its visual size and position in a correctly scaled 2D scene displayed on a large screen with aligned ground plane?
Bibliographic record
Abstract
INTRODUCTION: In our previous experiment (J Vis. 2022; 22(14):3310), we demonstrated that people cannot infer distance of an object reliably from its visual size and position in a 2D scene when viewed on a computer screen. Here we repeated the experiment with the visual stimuli projected onto a wall, fully displaying a simulated scene at natural size with the ground plane aligned with the floor. Might such a realistically scaled environment improve people’s distance perception in a 2D scene? METHOD: Participants sat on a chair and viewed a hallway scene projected on a wall 2m away with their chin on a chinrest. They were asked to imagine they were looking into an actual hallway. Participants held a reference object (a cereal box) in their hands and compared its size to the image of a corresponding object in the scene. Participants adjusted either its SIZE based on its position in the scene (position-to-size task) or its POSITION based on its visual size (size-to-position task) to match the reference size. They did the tasks either binocularly or monocularly. RESULTS: In general, the adjusted target position was consistent with the object’s size. The adjusted size, however, was consistently larger than geometrically correct when viewed binocularly but not monocularly. DISCUSSION: These results suggest that participants were more accurate at judging size based on position when conflicting stereo cues were removed despite reporting that using one eye was more difficult. Stereoscopic vision conflicts with reality when viewing a 2D simulation of a 3D scene and may underly the errors we see when judging the size of an object. The fact that we observed errors only when determining size and not position support the idea that size and distance perceptions use different mechanisms as suggested in our previous paper (Kim & Harris, 2022, Vision 6, 25).
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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".