Processing vertical size disparities in distinct depth planes
Bibliographic record
Abstract
A textured surface appears slanted about a vertical axis when the image in one eye is horizontally enlarged relative to the image in the other eye. The surface appears slanted in the opposite direction when the same image is vertically enlarged. Two superimposed textured surfaces with different horizontal size disparities appear as two surfaces that differ in slant. Superimposed textured surfaces with equal and opposite vertical size disparities appear as a single frontal surface. The vertical disparities are averaged. We investigated whether vertical size disparities are averaged across two superimposed textured surfaces in different depth planes or whether they induce distinct slants in the two depth planes. In Experiment 1, two superimposed textured surfaces with different vertical size disparities were presented in two depth planes defined by horizontal disparity. The surfaces induced distinct slants when the horizontal disparity was more than ±5 arcmin. Thus, vertical size disparities are not averaged over surfaces with different horizontal disparities. In Experiment 2 we confirmed that vertical size disparities are processed in surfaces away from the horopter, so the results of Experiment 1 cannot be explained by the processing of vertical size disparities in a fixated surface only. Together, these results show that vertical size disparities are processed separately in distinct depth planes. The results also suggest that vertical size disparities are not used to register slant globally by their effect on the registration of binocular direction of gaze.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".