Natural language signatures of psilocybin microdosing
OpenAlex – February 22, 2022
Source: OpenAlex
Summary
Artificial intelligence can accurately detect a psilocybin microdose from speech. A double-blind, placebo-controlled experiment explored how this hallucinogen, a chemical synthesis and alkaloid, affects natural language. Participants received either a 0.5g psilocybin mushroom microdose or a placebo. Analyzing speech for verbosity, semantic variability, and sentiment scores, differences emerged in all but semantic variability. Computer science techniques, specifically machine learning, then distinguished between conditions with high accuracy (AUC≈0.8). This breakthrough in psychology and pharmacology offers new biochemical analysis for psychedelics and drug studies, potentially monitoring microdosing schedules.
Abstract
Abstract Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics (“microdosing”) on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose. Following a double-blind and placebo-controlled experimental design, we explored natural language as a resource to identify speech produced under the acute effects of psilocybin microdoses, focusing on variables known to be affected by higher doses: verbosity, semantic variability and sentiment scores. Except for semantic variability, these metrics presented significant differences between a typical active microdose of 0.5 g of psilocybin mushrooms and an inactive placebo condition. Moreover, machine learning classifiers trained using these metrics were capable of distinguishing between conditions with high accuracy (AUC≈0.8). Our results constitute first proof that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules.