Inter-brain synchronization in the practice of Tibetan monastic debate
Bibliographic record
Abstract
Objectives: Although mindfulness meditation is the familiar and researched form of mental training derived from Buddhism, it represents but one form of practice. Monastic debate is an interactive and dyadic analytical meditation practice that originates from the Tibetan Buddhist tradition where monastics seek to jointly deepen their understanding of complicated philosophical issues. To date, monastic debate and analytic meditation have yet to be examined in the context of scientific investigation. Methods: In the current study, we examined the neural correlates of this analytical meditation practice by means of hyperscanning electroencephalography, a method well-suited for examining social interactions. Results: Consistent with the idea that analytical meditation helps to train concentration, we observed that over the course of the debate, mid-frontal theta oscillations—a correlate of absorption—increased significantly. This increase was stronger for more experienced monks as compared to monks at the beginning of their education. In addition, we found evidence for increases in synchrony in frontal alpha oscillations between paired debaters during moments of agreement as compared to disagreement on a set of premises. Conclusions: Together, these findings provide an initial understanding of Tibetan monastic debate and analytical meditation using neuroscientific methods.
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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".