Toward a neuroscience of consciousness using advanced meditation
Bibliographic record
Abstract
Despite decades of progress in the neuroscience of consciousness, prevailing empirical paradigms remain largely anchored in the study of typical, content-rich states that are characterized by layered perceptual, cognitive, affective, and self-referential processes. Such complexity may obscure the neural mechanisms that give rise to conscious experience. Here, we propose that advanced meditation-referring to states and stages of practice that unfold progressively with increasing expertise-offers a powerful yet unexplored opportunity to isolate the core features of consciousness through a theory-driven neuroscience approach. We focus on two classes of meditative phenomena: advanced concentrative absorption (related to what have been called jhāna), which involves the preservation of highly abstract forms of awareness alongside the attenuation of typical features of consciousness; and meditative endpoints-namely, cessation events (related to what have been called nirodha)-which involve the temporary suspension of consciousness altogether. These phenomena serve as precise, replicable, and experimentally tractable phenomenological anchors for a minimal model framework, a novel approach aimed at identifying and characterizing the simplest possible form of conscious experience as a principled starting point for a systematic science of consciousness. Within this framework, the integration of advanced meditation into experimental paradigms offers a promising path toward identifying the neural mechanisms that support consciousness in its most reduced and fundamental forms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".