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...
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...
Psychedelics probably alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Individual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6850 free-form testimonials about 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then...