Binocular contrast, stereopsis, and rivalry: Toward a dynamical synthesis
Bibliographic record
Abstract
It is well known that small orientation differences between two monocular gratings fuse to generate a stereoscopic perception of tilt, while large differences trigger binocular rivalry. In addition, unequal monocular contrasts combine nonlinearly to generate binocular contrast. A nonlinear neural model is developed here to account for binocular contrast, fusion at small orientation differences, and rivalry at large differences. The model also accounts for hysteresis in the transition between fusion and rivalry. Finally, the model predicts that interocular contrast differences between fusible gratings will produce a reduced tilt percept, and experiments reported here support this. Key to the model is the presence of two classes of inhibitory interneurons: one operating on similar orientations to normalize interocular contrast (IN), and one operating across large orientation differences to generate rivalry (IR). Critically, the IN neurons switch off the IR neurons driven by the other eye, thus permitting fusion of binocular plaids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".