Perceptual grouping via binocular disparity: The impact of stereoscopic good continuation
Bibliographic record
Abstract
Stereoscopic contextual effects are widely reported but are generally discussed in terms of 2-D Gestalt grouping principles, e.g., good continuation or closure. We propose that there are disparity-based grouping operations that are separable from 2-D grouping and instead depend on the distribution of binocular disparity information. Two experiments assess the impact of perceptual grouping via good disparity continuation. First, perceived depth magnitude is reduced for a multidot contour with a smooth disparity gradient compared to the end points in isolation. This reduction is eliminated when disparity jitter is introduced to the intermediate dots. Second, observers showed more efficient visual search for the continuous contour versus the discontinuous version. Therefore, when there is spatial support for interpretation of a slanted object, quantitative depth is reduced, but is rapidly detected in visual search. These results reflect the operation of disparity-based grouping, extending the 2-D principle of good continuation into the third dimension.
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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 | 0.003 |
| 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 | 0.001 |
| 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".