Detection of vertical interocular phase disparities using luster as cue
Bibliographic record
Abstract
Interocular disparities in contrast generate an impression of binocular luster, providing a cue for their detection. Disparities in the carrier spatial phase of horizontally oriented Gabor patches also generate an impression of luster, so the question arises as to whether it is the disparities in local contrast that accompany the phase disparities that give rise to the luster. We examined this idea by comparing the detection of interocular spatial phase disparities with that of interocular contrast disparities in Gabor patches, in the latter case that differed in overall contrast rather than phase between the eyes. When bandwidth was held constant and Gabor spatial frequency was varied, the detection of phase and contrast disparities followed a similar pattern. However, when spatial frequency was fixed and Gabor envelope standard deviation (and hence number of modulation cycles) was varied, thresholds for detecting phase disparities followed a U-shaped function of Gabor standard deviation, whereas thresholds for contrast disparities, following an initial decline, were more-or-less constant as a function of Gabor standard deviation. After reviewing a number of possible explanations for the U-shape found with phase disparities, we suggest that the likely cause is binocular sensory fusion, the strength of which increases with the number of modulation cycles. Binocular sensory fusion would operate to reduce phase but not contrast disparities, thus selectively elevating phase disparity thresholds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".