Bibliographic record
Abstract
We previously found that the retinal peripheries may not signal the human visual awareness when ‘spatial wrapping’ stimulus has very low contrast, or when the human subjects perform deep inhalation, see reference 19 and 20. In this study, we found that those techniques are able to significantly reduce the motion aftereffect (MAE) too. Namely, when we reduce the contrast of the MAE stimulator, the MAE is significantly reduced. Similarly, when we ask the subjects to perform deep inhalation in the end of viewing a ‘high contrast’ MAE stimulator, the MAE is drastically attenuated. The neurophysiological processes of the previous techniques are vastly different, as explained in our previous work. Namely, significant contrast reduction deactivates the retinal peripheries due to the following reasons; first, extremely low contrast stimulus constricts the pupil that disallows the retinal peripheries from receiving enough light rays to signal the brain actively, second, for extremely low contrast conditions, the center-surround antagonism process in the retinal peripheries might not signal the brain at all. Deep inhalation, however, may cause idle links between the retinal peripheries and their corresponding neurological pathways that eventually signal the visual awareness, a process that is also found to weaken the MAE.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".