Stereoscopic slant contrast revisited
Bibliographic record
Abstract
The perceived slant of a stereoscopic surface is altered by the presence of a surrounding surface, a phenomenon termed stereo slant contrast. Previous studies have shown that a slanted surround causes a fronto-parallel surface to appear slanted in the opposite direction, an instance of "bidirectional" contrast. A few studies have examined slant contrast using slanted as opposed to fronto-parallel test surfaces, and these also have shown slant contrast. Here, we use a matching method to examine slant contrast over a wide range of combinations of surround and test slants, one aim being to determine whether stereo slant contrast transfers across opposite directions of test and surround slant. We also examine the effect of the test on the perceived slant of the surround. Test slant contrast was found to be bidirectional in virtually all test-surround combinations and transferred across opposite test and surround slants, with little or no decline in magnitude as the test-surround slant difference approached the limit. There was a weak bidirectional effect of the test slant on the perceived slant of the surround. We consider how our results might be explained by four mechanisms: (a) normalization of stereo slant to vertical; (b) divisive normalization of stereo slant channels in a manner analogous to the tilt illusion; (c) interactions between center and surround disparity-gradient detectors; and (d) uncertainty in slant estimation. We conclude that the third of these (interactions between center and surround disparity-gradient detectors) is the most likely cause of stereo slant contrast.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".