Bibliographic record
Abstract
Pictures are surfaces. Pictures show surfaces. But what is a theory of perception of surfaces? Surface perception was first mentioned in experimental psychology by Metzger in Ganzfeld experiments in the 1930s. However, it was first offered as a serious concept in perception theory by Alhazen in his Book of Optics (1039). Remarkably, almost no contemporary theory of perception uses the term. To rectify this omission, a theory of surfaces is presented here, suggesting that surface perception occurs in all 8 of vision’s modes. Optical information for the shapes of surfaces is given by the ratio of azimuth to elevation. Flat surfaces such as the ground have a linear to quadratic ratio. Increase the ratio and hills are seen. Decrease it and the surrounds are a bowl. Sudden changes in the ratio indicate changes in slant. Sudden changes in density without changes in the ratio indicate a drop-off. The theory is applied to outline drawing and to the fact that pictures provide two surfaces (the real surface of the picture and the depicted surface). The two surfaces create illusions. Features on the picture surface cannot be seen correctly. The importance of surface perception is its breadth of application. The theory of surface perception shows why pictures taken on the Moon or Mars are as intelligible as terrestrial pictures. Surfaces allow control of action even for creatures that fly in 3D without touching surfaces during flight, such as bats and birds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".