Speech markers of psychedelic-induced psychological change
Bibliographic record
Abstract
Abstract 5-MeO-DMT, a potent, short-acting psychedelic, induces profound shifts in cognition, affect, and self-awareness. Because language explicitly expresses these domains and voice implicitly conveys them, both may serve as potential ‘biomarkers’ of behavioural change. This study introduces a novel framework for analysing baseline language and vocal features, pre- to post-psychedelic changes, assessing their potential to predict subjective experiences and psychological outcomes. Daily voice journals from 29 participants were collected via ‘RetreatBot’ for two weeks before and after 5-MeO-DMT (1×12 mg). Transcripts were analysed using NLP (bag-of-words for vocabulary; transformer model for textual affect), and acoustic features (e.g., pitch, jitter, shimmer) were extracted to assess vocal dynamics. Following 5-MeO-DMT, speech markers revealed increased cognitive language, decreased social words, and altered voice quality (increased jitter/shimmer). Baseline speech patterns predicted psychological preparedness, ego dissolution anxiety, emotional breakthrough, and post-experience well-being. This first longitudinal analysis of speech markers following psychedelic use demonstrates a shift from external focus to introspection. Speech markers predicted and tracked psychological transformation, establishing vocal journaling as a valuable framework for monitoring psychedelic-induced changes and facilitating integration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".