Familiar objects affect size and distance judgements differently when viewing an object in a 2D
Bibliographic record
Abstract
The invariable relationship between the absolute distance of an object and its retinal size is widely known and people use this for their size and distance perception in the 3D world. Familiar objects of known size can help size/distance perception. But often we experience the 3D world through 2D images such as pictures or television. In this study, we tested whether images of familiar objects can improve people’s perception of an object’s size and distance in a 2D scene. In a series of online experiments run on Pavlovia, participants viewed a target black rectangle 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 (P/S task), or made the target the correct size based on its position in the scene (S/P task). Some participants saw the scene with familiar objects (n= 84) and the others saw the scene without (n= 134). In the group that saw the scene with familiar objects, participants made the targets significantly smaller for the S/P task compared to the target size they had chosen at those same positions during the P/S task (p= .004). However, there was no difference between tasks for the group without familiar objects (p= 1.000). Further evaluation revealed that the targets were made significantly smaller for the P/S task in the presence of familiar objects compared to without familiar objects (p= .001), but not for the S/P task (p= .598). This result show that familiar objects may act as anchors for observers to use when determining an object’s size and distance in a 2D scene beyond the simple geometry provided by the horizon and ground plane, but they may affect the perception of size and distance differently in a 2D scene.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".