Bibliographic record
Abstract
In animals with binocular depth vision, or stereopsis, the visual fields of the two eyes overlap, shrinking the overall field of view. Eye movements increase the field of view, but they also complicate the first stage of stereopsis: the search for corresponding images on the two retinas. If the eyes were stationary in the head, corresponding images would always lie on retina-fixed bands called epipolar lines. Because the eyes rotate, the epipolar lines move on the retinas. Therefore, the stereoptic system has a choice: it may monitor eye position to keep track of the epipolar lines, or it may give up on tracking epipolar lines and instead search for matches over retina-fixed regions, but in that case the search regions must be 2-D patches, large enough to encompass all possible locations of the epipolar lines in all usual eye positions. We use a new type of random-dot stereogram to show that human stereopsis uses large, retina-fixed search zones. We show that the brain somewhat reduces the size of these search zones by rotating the eyes about their lines of sight in a way that reduces the motion of the epipolar lines. These findings show the link between sensory and motor processes: by considering eye motion we can understand why the brain searches for matching images over 2-D retinal regions rather than along epipolar lines; and by considering retinal correspondence we appreciate why the eyes rotate as they do about their lines of sight.
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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 | 0.006 |
| 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) | 0.003 | 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".