Natural language signatures of psilocybin microdosing.
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| Title: | Natural language signatures of psilocybin microdosing. |
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| Authors: | Sanz, Camila (AUTHOR), Cavanna, Federico (AUTHOR), Muller, Stephanie (AUTHOR), de la Fuente, Laura (AUTHOR), Zamberlan, Federico (AUTHOR), Palmucci, Matías (AUTHOR), Janeckova, Lucie (AUTHOR), Kuchar, Martin (AUTHOR), Carrillo, Facundo (AUTHOR), García, Adolfo M. (AUTHOR), Pallavicini, Carla (AUTHOR), Tagliazucchi, Enzo (AUTHOR) |
| Source: | Psychopharmacology. Sep2022, Vol. 239 Issue 9, p2841-2852. 12p. 4 Graphs. |
| Subjects: | Psilocybin, Mental health services, Natural languages, Mental illness, Edible mushrooms, Machine learning |
| Abstract: | Rationale: 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. Objectives: Determine whether unconstrained speech contains signatures capable of identifying the acute effects of psilocybin microdoses. Methods: 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. Results: 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 ≈ 0.8). Conclusions: 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Rationale: 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. Objectives: Determine whether unconstrained speech contains signatures capable of identifying the acute effects of psilocybin microdoses. Methods: 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. Results: 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 ≈ 0.8). Conclusions: 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00333158 |
| DOI: | 10.1007/s00213-022-06170-0 |
