Can People Infer Distance in a 2D Scene Using the Visual Size and Position of an Object?
Bibliographic record
Abstract
Depth information is limited in a 2D scene and for people to perceive the distance of an object, they need to rely on pictorial cues such as perspective, size constancy and elevation in the scene. In this study, we tested whether people could use an object's size and its position in a 2D image to determine its distance. In a series of online experiments, participants viewed a target representing their smartphone rendered within a 2D scene. They either positioned it in the scene at the distance they thought was correct based on its size or adjusted the target to the correct size based on its position in the scene. In all experiments, the adjusted target size and positions were not consistent with their initially presented positions and sizes and were made larger and moved further away on average. Familiar objects influenced adjusted position from size but not adjusted size from position. These results suggest that in a 2D scene, (1) people cannot use an object's visual size and position relative to the horizon to infer distance reliably and (2) familiar objects in the scene affect perceived size and distance differently. The differences found demonstrate that size and distance perception processes may be independent.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".