Understanding individual differences in non-ordinary state of consciousness: Relationship between phenomenological experiences and autonomic nervous system
Bibliographic record
Abstract
Non-ordinary states of consciousness offer a unique opportunity to explore the interplay between phenomenological experiences and physiological processes. This study investigated individual differences in phenomenological and autonomic nervous system changes between a resting state condition and a non-ordinary state of consciousness (auto-induced cognitive trance, AICT). Specifically, it examined the relationship between self-reported experiences (e.g., absorption, visual representations) and heart rate variability (HRV). Twenty-seven participants underwent electrocardiography recordings and completed self-report questionnaires during rest and AICT. A machine learning framework distinguished the rest and AICT states based on self-reported measures and HRV metrics. A linear mixed-effects model assessed inter-individual differences in HRV and self-reported phenomenology between the two states. Finally, the relationship between relative change in HRV and self-reported experiences was explored. Results showed changes in self-reported phenomenology (accuracy=86 %; p <.001) and HRV (accuracy=73 %; p <.001) characterizing the AICT state compared to rest. The baseline level in phenomenology or HRV was associated with change amplitude during AICT. Moreover, relative change in HRV was associated with change in phenomenology. The findings suggest that inter-individual differences at rest revealed a functional mechanism between phenomenology and the autonomic nervous system during non-ordinary states of consciousness, offering a novel perspective on how physiological mechanisms shape subjective experiences.
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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 | 0.004 |
| 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) | 0.001 | 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".