The Potential for Excess Correlation (Entanglement) between Flow States in Pairs of Gamers Sharing Specific Circumcerebral Rotating Magnetic Fields
Bibliographic record
Abstract
Excess correlation between the activity or properties of two particles separated by non-local distances has been demonstrated for photons, shifts in relative proton and hydroxyl ratios in spring water, and the distribution of values from random number generators if both loci shared a specific type of rotational magnetic field. Previous experiments had shown that specific responses from pairs of people who shared circumcerebral magnetic fields with changing angular velocities revealed significant excess correlation. The most significant differences occurred during the component of the field exposure that has previously been associated with “excess correlation”. In the present experiments, we found evidence of excess correlation of performance (serial in-game scores) occurred between pairs (separated by 10 m) of experienced gamers during the relative measures for the central portion of the protocol but was diminished when the “excess correlation” electromagnetic fields were activated, suggesting a similar competing mechanism. The results are consistent with the interpretation that shared video systems and activities may enhance excess correlation of responses. This can be simulated in novice players by experimentally inducing excess correlation through appropriate application of changing, circumcerebral angular velocity magnetic fields that were similar in magnitude to those associated with computer systems and time frames that define human consciousness.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".