Biophysical visual virtual reality in retinotopic visual areas
Bibliographic record
Abstract
Abstract Previously, we have pointed out that biophoton production can be a controlled process that originates from regulated redox/radical reactions. Our biophoton experiments support the notion that various visual related phenomena such as discrete retinal noise, retinal phosphenes as well as negative afterimages are due to biophotons. We have also suggested a new model, stating that the brain is able to create biophysical pictures in retinotopic visual areas via redox regulated biophotons of synchronized neurons. According to our interpretation, visualization (imagery) is a special kind of representation i.e., visual imagery requires peculiar inherent biophysical processes. Our idea of biophysical visual virtual reality in retinotopic areas may be a possible biophysical basis of Kosslyn's reality simulation principle in the case of visual imagery. Long-term visual memories are not stored as biophysical pictures but as epigenetic codes. During visual imagery, top-down processes control the epigenetic encoded long-term visual information. Then, according to retrieved epigenetic information, synchronized retinotopic neurons generate dynamic patterns of biophotons via redox reactions that can produce biophysical pictures. We have also presented an iterative model involving a biophysical picture-representation without homunculus during visual imagery.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".