Familiar size affects size and distance perception for real objects, even in the presence of oculomotor cues.
Bibliographic record
Abstract
In the real world, do we rely on our knowledge of object size (i.e., familiar size) for space perception or do we compute object dimensions from a combination of the retinal image and oculomotor cues? Under restricted viewing conditions (i.e., a monocular pinhole to minimize oculomotor cues), when an object’s retinal angle is the only cue to size and distance, the visual system relies on familiar size. In this case, when presented with objects, for which the actual size is inconsistent with the familiar size (e.g., unusually large chairs), perception is inaccurate (e.g., Ittelson, 1951). In contrast, under unrestricted binocular viewing in natural environments, familiar size may have little or no effect on perceived size (e.g., Predebon, Wenderoth, & Curthoys, 1974). We examined size and distance perception while manipulating not just the physical size and distance of objects from the viewer, but also the congruency of objects with their familiar sizes and the availability of oculomotor cues. We presented real Rubik’s cubes and dice, each either in a size congruent with expectations (5.7-cm Rubik’s cube and 1.6-cm die) or the reverse or incongruent size (5.7-cm die and 1.6-cm Rubik’s cube), at two distances (25 cm and 91 cm). Participants viewed one object at a time in a dark tunnel (to eliminate pictorial cues), either monocularly through a 1-mm pinhole (to eliminate oculomotor cues) or binocularly (with full oculomotor cues). Participants indicated the perceived size and distance of an object by moving their fingers apart (manual estimation). Regardless of the presence or absence of oculomotor cues, familiar size affected both size and distance perception: Rubik’s cubes were perceived as larger and farther than dice, even when objects had identical dimensions. In sum, familiar size is a potent visual cue that affects object perception even during binocular viewing.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".