Bibliographic record
Abstract
Abstract This chapter contains a review of information that we use to perceive the three‐dimensional structure of the visual world. To a limited extent for near viewing we can use nonvisual signals from the accommodative state of the lens or the state of convergence of the eyes. Visual information available to one eye included image blur arising from out‐of‐focus images; overlap between images of objects at different distances serves as a cue to relative depth. Shading and shadows provide a rich source of information about relative depth, although the sign of depth relations can be ambiguous. When we move past a stationary scene, the relative motion of images of objects at different distances indicates very small differences in depth. Image expansion of an approaching object indicates time to impact and the symmetry of image motion indicates whether the approaching object will hit us. Binocular information available when only both eyes are open includes differences between the images in the two eyes. These binocular disparities provide information about the distances of objects, and the inclination and curvature of surfaces. The chapter ends with a discussion of how different depth cues interact to produce a unified percept of the three‐dimensional structure of the world.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.253 | 0.025 |
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; both teacher heads agree on what is shown here.
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".