Language Models Learn Sentiment and Substance from 11,000 Psychoactive Experiences

Machine learning applied to 11,816 publicly available drug testimonials reveals distinct subjective experiences linked to specific substances. Using BERT, a transformer model, the study predicted 28 dimensions of sentiment across narratives, validated by psychiatrist annotations. Canonical correlation analysis connected 52 drugs' receptor affinities with word usage, uncovering 11 latent receptor-experience factors mapped to a 3D cortical atlas. Results distinguished lucid from mundane phenomena: MDMA was linked to "Love," DMT and 5-MeO-DMT to "Mystical Experiences," and other tryptamines to "Surprise," "Curiosity," and "Realization." These models could potentially guide real-time biofeedback in therapeutic sessions.

Study at a glance

Abstract

Abstract With novel hallucinogens poised to enter psychiatry, we lack a unified framework for quantifying which changes in consciousness are optimal for treatment. Using transformers (i.e. BERT) and 11,816 publicly-available drug testimonials, we first predicted 28-dimensions of sentiment across each narrative, validated with psychiatrist annotations. Secondly, BERT was trained to predict biochemical and demographic information from testimonials. Thirdly, canonical correlation analysis (CCA) linked 52 drugs' receptor affinities with testimonial word usage, revealing 11 latent receptor-experience factors, mapped to a 3D cortical atlas. Together, these 3 machine learning methods elucidate a neurobiologically-informed, temporally-sensitive portrait of drug-induced subjective experiences. Different models’ results converged, revealing a pervasive distinction between lucid and mundane phenomena. MDMA was linked to "Love", DMT and 5-MeO-DMT to "Mystical Experiences", and other tryptamines to "Surprise", "Curiosity" and "Realization". Applying these models to real-time biofeedback, practitioners could harness them to guide the course of therapeutic sessions.