Natural language analysis of the structure of altered states of consciousness
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it