Bibliographic record
Abstract
Scientists reportedly had been reporting that indefinite stable termination of stable binocular rivalry (BR) is impossible; namely, the frequency of the rivalry must follow the gamma distribution. This is a manifestation of binocular rivalry data (Levelt, 1965; Fox & Herrmann, 1967; Logothetis, 1998); a signature that is used to discover the neural substrates of binocular rivalry. In this report, however, against the common agreement of the statistical representation of the BR data that includes but not limited the effect of voluntary action on binocular rivalry (see Blake, etal. 2007); we provide several exhibits that reveal indefinite stoppage against binocular rivalry; and thus, the literature of binocular competition should be revisited. We superficially principlize ‘the hidden dynamics’ behind these phenomenal terminations, and this should shed the light on the importance of Dönner, 2008, 2013 articles. It also casts attention on the essence of Walker, 1998 article, where binocular cross-orientation suppression is not found in the striate cortex, but prior the conversion of the two monocular streams. Since no retinal studies are established in binocular rivalry experiments, we therefore believe that the governors of the perceptual rivalry cannot be concluded. But since the results of our psychophysical experiments disobey the statistical representation of the BR data; we suggest that the rivalry could not happen without an extra physical layer as an affirming playground to these hidden dynamics ‘the governors of the perceptual rivalry’. We end this statement by offering applications that shall benefit medicine and engineering through the utilization of that phenomenal stoppage.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".