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)
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.