The Moon Illusion in a Unified Theory of Visual Space: Alberta Steinman Gilinsky
Bibliographic record
Abstract
An intriguing possibility suggests that the moon illusion can be related to a general theory of perceived size and distance in visual space (Gilinsky, 1951) and, indeed, predicted as its natural consequence. To be satisfactorily general and unified, an adequate theory of visual space perception must meet the following requirements: (a) It should provide quantitative functional relations between perceived size and physical size; (b) it should provide quantitative functional relations between perceived distance and physical distance; (c) it should establish the functional relations between perceived size and perceived distance; (d) it should be supported by experimental observations that give rise to veridical size and distance perception at near distances and to reduced size and distance at larger distances; (e) it should be consistent with known neurophysiological mechanisms that underlie the human visual system; (f) it should account for the empirical data observed in visual spatial illusions, including the compelling illusions of the perceived size and distance of the moon on the horizon and at the zenith; (g) it should be consistent with our knowledge of perceptual development in immature organisms and account for age-related changes in the perception of visual space; (h) finally, it should take adequate account of the influence of different environmental conditions of illumination, total space volume, and the available sensory indicators of size, shape, and distance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".