Luminance spatial scale facilitates stereoscopic depth segmentation
Bibliographic record
Abstract
Are differences in luminance spatial frequency between surfaces that overlap in depth useful for surface segmentation? We examined this question, using a novel stimulus termed a dual-surface disparity grating. The dual-surface grating was made from Gabor micropatterns and consisted of two superimposed sinusoidal disparity gratings of identical disparity-modulation spatial frequency and orientation but of opposite spatial phase. Corrugation amplitude thresholds for discrimination of the orientation of the dual-surface grating were obtained as a function of the difference in Gabor (luminance) spatial frequency between the two surfaces. When the Gabor micropatterns on the two surfaces were identical in spatial frequency, thresholds were very high and in some instances impossible to obtain. However, with as little as a 1-octave difference in spatial frequency between the surfaces, thresholds fell sharply to near-asymptotic levels. The fall in thresholds paralleled a change in the appearance of the stimulus from one of irregular depth to stereo transparency. The most parsimonious explanation for this finding is that the introduction of a between-surface luminance spatial-frequency difference reduces the number of spurious cross-surface binocular matches, thus helping to reveal the three-dimensional structure of the stimulus.
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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) | 0.001 | 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".