Natural language analysis of the structure of altered states of consciousness
Bibliographic record
Abstract
Abstract Background and aims Altered states of consciousness (ASC) represent acute and marked deviations from normal waking consciousness. Investigations into ASC are significant to problems in medicine, science, and philosophy, including the structure of conscious experience. Here, we conducted a preliminary investigation into the structure of ASC while addressing the role of psychedelics, which purportedly manifest features of mind. Methods We performed quantitative and qualitative analyses of 300 narrative reports across 12 ASC induction methods: meditation, float tank, psilocybin, lysergic acid diethylamide (LSD), N,N-dimethyltryptamine (DMT), 5-methoxy-N,N-DMT (5-MeO-DMT), ketamine, salvia, 3,4-methylenedioxymethamphetamine (MDMA), cannabis, datura, and diphenhydramine (DPH). We hypothesized that reports from the psychedelics (serotonin 5-HT 2A receptor agonists) would contain similar content with non-pharmacological induction methods, alongside greater positive sentiment and reported authenticity relative to reports from other substances. Results In quantitative analysis, most psychedelics, except LSD, as well as salvia and ketamine, shared similar content with non-pharmacological methods. In qualitative analysis, most psychedelics, except LSD, were deemed both positive and authentic, with authenticity predicting positive sentiment across the 12 ASC induction methods ( R = 0.68; p = 0.015). We uncovered latent themes charting a trajectory of ASC from baseline to metaphysical experience, incorporating text-to-image generative artificial intelligence to illustrate underlying phenomenological structure. Conclusions Our findings suggest that reproducible structural observations may be externally validated across methods to support a “mind-manifesting” characterization for some ASC induction methods, such as salvia, ketamine, or 5-MeO-DMT, but not for others, such as LSD, datura, or DPH, together informing future studies of psychedelics, ASC, and structuralism.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".