Internal representations of the canonical real-world distance of objects
Bibliographic record
Abstract
In the real world, every object has its canonical distance from observers. For example, airplanes are usually far away from us, whereas eyeglasses are close to us. Do we have an internal representation of the canonical real-world distance of objects in our cognitive system? If we do, does the canonical distance influence the perceived size of an object? Here, we conducted two experiments to address these questions. In Experiment 1, we first asked participants to rate the canonical distance of objects. Participants gave consistent ratings to each object. Then, pairs of object images were presented one by one in a trial, and participants were asked to rate the distance of the second object (i.e., a priming paradigm). We found that the rating of the perceived distance of the target object was modulated by the canonical real-world distance of the prime. In Experiment 2, participants were asked to judge the perceived size of canonically near or far objects that were presented at the converging end (i.e., far location) or the opening end (i.e., near location) of a background image with converging lines. We found that regardless of the presentation location, participants perceived the canonically near object as smaller than the canonically far object even though their retinal and real-world sizes were matched. In all, our results suggest that we have an internal representation of the canonical real-world distance of objects, which affects the perceived distance of subsequent objects and the perceived size of the objects themselves.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".