Interactions between binocular rivalry and depth in plaid patterns
Bibliographic record
Abstract
In binocular rivalry produced using orthogonal diagonal gratings, if a matching diagonal grating is added to one eye then rivalry is eliminated. We found that in fact rivalry is not eliminated for certain spatial frequency combinations for matched and rivalrous diagonal patterns involving differences of one octave or more. We investigated whether this effect generalizes to plaids which are the sum of: (1) rivalrous orthogonal diagonal gratings and (2) identical vertical gratings in the two eyes. Over 100 s trials, observers pressed one key when the left grating predominated, or another key when it was not visible. For 2 and 4 cpd patterns, rivalry occurred if the spatial frequency of the vertical gratings was more than 0.4 octaves above or below that for the diagonal gratings (or 0.35 octaves for 8 cpd gratings). Following these measurements for the bandwidth for rivalry, we investigated the interaction between rivalry and depth. Similar plaids were used, with the additional manipulation that depth was produced in the vertical components using three methods: (1) an orientation disparity of plus or minus 4 degrees was used to produce tilt forwards or backwards; (2) slant was produced using a spatial frequency difference between the two eyes; (3) a phase offset in the vertical grating was used so that the top and bottom of the image were in different depth planes. Observers performed a match for the apparent depth of the vertical components in the plaids using the method of adjustment. In all cases depth and rivalry coexisted when the spatial frequency difference between the vertical and diagonal gratings was greater than 0.8 octaves, but neither depth nor rivalry was present for smaller differences. These results place constraints on models of stereopsis and rivalry.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".