Trajectories of sentiment in 11,816 psychoactive narratives
Human Psychopharmacology Clinical and Experimental – December 20, 2023
Source: OpenAlex
Summary
Machine learning has unveiled striking correlations between sentiment and psychoactive experiences across a diverse range of 52 drugs. Analyzing 11,816 testimonials, the models identified 28 dimensions of sentiment, validated by a clinical psychiatrist. Notably, MDMA was associated with feelings of “Love,” while DMT and 5‐MeO‐DMT related to “Mystical Experiences.” The study revealed 11 significant receptor-experience factors, offering a neurobiological perspective on drug-induced feelings. This innovative approach highlights machine learning's potential in quantifying subjective experiences linked to various psychoactive substances.
Abstract
Abstract Objective Can machine learning (ML) enable data‐driven discovery of how changes in sentiment correlate with different psychoactive experiences? We investigate by training models directly on text testimonials from a diverse 52‐drug pharmacopeia. Methods Using large language models (i.e. BERT) and 11,816 publicly‐available testimonials, we predicted 28‐dimensions of sentiment across each narrative, and then validated these predictions with adjudication by a clinical psychiatrist. BERT was then fine‐tuned to predict biochemical and demographic information from these narratives. Lastly, canonical correlation analysis linked the drugs' receptor affinities with word usage, revealing 11 statistically‐significant latent receptor‐experience factors, each mapped to a 3D cortical Atlas. Results These methods elucidate a neurobiologically‐informed, sequence‐sensitive portrait of drug‐induced subjective experiences. The models' results converged, revealing a pervasive distinction between the universal psychedelic heights of feeling in contrast to the grim, mundane, and personal experiences of addiction and mental illness. Notably, MDMA was linked to “Love”, DMT and 5‐MeO‐DMT to “Mystical Experiences” and “Entities and Beings”, and other tryptamines to “Surprise”, “Curiosity” and “Realization". Conclusions ML methods can create unified and robust quantifications of subjective experiences with many different psychoactive substances and timescales. The representations learned are evocative and mutually confirmatory, indicating great potential for ML in characterizing psychoactivity.