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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".