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Natural language signatures of psilocybin microdosing.

Camila Sanz, Federico Cavanna, Stephanie Müller, Laura De la Fuente, Federico Zamberlán, Matías Palmucci, Lucie Janeckova, Martin Kuchař, Facundo Carrillo, Adolfo M García, Carla Pallavicini, Enzo Tagliazucchi

Psychopharmacology September 1, 2022 DOI: 10.1007/s00213-022-06170-0 (opens in new tab)

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Study at a glance

AI-extracted from the abstract
Characteristics Randomized controlled trial Placebo-controlled Double-blind Peer reviewed
Sample size 34
Population Healthy adult volunteers
Intervention Psilocybin mushrooms
Dose 0.5 g of psilocybin mushrooms
Duration Two measurement weeks per participant, with doses on Wednesdays and Fridays each week
Topics Microdosing Psilocybin
Keywords Language Machine learning Psychedelics
Citations 1
Key findings Machine learning classifiers can distinguish between psilocybin microdose and placebo conditions from natural speech with high accuracy (AUC ~0.8).

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. Determine whether unconstrained speech contains signatures capable of identifying the acute effects of psilocybin microdoses. Natural speech under psilocybin microdoses (0.5 g of psilocybin mushrooms) was acquired from thirty-four healthy adult volunteers (11 females: 32.09 ± 3.53 years; 23 males: 30.87 ± 4.64 years) following a double-blind and placebo-controlled experimental design with two measurement weeks per participant. On Wednesdays and Fridays of each week, participants consumed either the active dose (psilocybin) or the placebo (edible mushrooms). Features of interest were defined based on variables known to be affected by higher doses: verbosity, semantic variability, and sentiment scores. Machine learning models were used to discriminate between conditions. Classifiers were trained and tested using stratified cross-validation to compute the AUC and p-values. Except for semantic variability, these metrics presented significant differences between a typical active microdose and the inactive placebo condition. Machine learning classifiers were capable of distinguishing between conditions with high accuracy (AUC [Formula: see text] 0.8). These results constitute first evidence 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.

Comparable studies

Other randomized controlled trials on psilocybin and microdosing, most cited first.

Study Year Design Participants
Microdosing with psilocybin mushrooms: a double-blind placebo-controlled study Individuals starting to microdose with psilocybin mushrooms (Psilocybe cubensis) 2022 Double-blind placebo-controlled experimental design n = 34
Psilocybin microdosing does not affect emotion-related symptoms and processing: A preregistered field and lab-based study 2021 Randomized controlled trial
Effects of psilocybin microdosing on awe and aesthetic experiences: a preregistered field and lab-based study Participants in a microdosing workshop 2021 Preregistered combined field- and lab-based crossover study
Microevidence for microdosing with psilocybin mushrooms: a double-blind placebo-controlled study of subjective effects, behavior, creativity, perception, cognition, and brain activity Individuals planning to microdose with psilocybin mushrooms 2021 Randomized controlled trial n = 34
Microdosing psychedelics and its effect on creativity: Lessons learned from three double-blind placebo controlled longitudinal trials Participants in semi-naturalistic microdosing trials 2021 Double-blind placebo-controlled longitudinal trial (mega-analysis of three trials) n = 175

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