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M. Sigman

The entropic tongue: Disorganization of natural language under LSD

January 21, 2022 / LSD, Papers, Psychology / Leave a Comment / By / , , , , , , , , , ,

Abstract

Serotonergic psychedelics have been suggested to mirror certain aspects of psychosis, and, more generally, elicit a state of consciousness underpinned by increased entropy of on-going neural activity. We investigated the hypothesis that language produced under the effects of lysergic acid diethylamide (LSD) should exhibit increased entropy and reduced semantic coherence. Computational analysis of interviews conducted at two different time points after 75 μg of intravenous LSD verified this prediction. Non-semantic analysis of speech organization revealed increased verbosity and a reduced lexicon, changes that are more similar to those observed during manic psychoses than in schizophrenia, which was confirmed by direct comparison with reference samples. Importantly, features related to language organization allowed machine learning classifiers to identify speech under LSD with accuracy comparable to that obtained by examining semantic content. These results constitute a quantitative and objective characterization of disorganized natural speech as a landmark feature of the psychedelic state.

Sanz, C., Pallavicini, C., Carrillo, F., Zamberlan, F., Sigman, M., Mota, N., Copelli, M., Ribeiro, S., Nutt, D., Carhart-Harris, R., & Tagliazucchi, E. (2021). The entropic tongue: Disorganization of natural language under LSD. Consciousness and cognition, 87, 103070. https://doi.org/10.1016/j.concog.2020.103070

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Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression

March 30, 2018 / Depressive Disorders, Mushrooms / Psilocybin, Papers, Psychology, Publications / By / , , , , , ,

Abstract

BACKGROUND:
Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not.
METHODS:
A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response.
RESULTS:
Speech analytics and machine learning successfully differentiated depressed patients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision).
CONCLUSIONS:
Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity.
LIMITATIONS:
The sample size was small and replication is required to strengthen inferences on these results.
Carrillo, F., Sigman, M., Slezak, D. F., Ashton, P., Fitzgerald, L., Stroud, J., … & Carhart-Harris, R. L. (2018). Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. Journal of affective disorders, 230, 84-86. 10.1016/j.jad.2018.01.006
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A window into the intoxicated mind? Speech as an index of psychoactive drug effects.

April 3, 2014 / MDMA, Papers, Psychology, Publications / By / , , , , ,

Abstract

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique ‘window’ into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; ‘ecstasy’) and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness.

Bedi, G., Cecchi, G. A., Slezak, D. F., Carrillo, F., Sigman, M., & de Wit, H. (2014). A window into the intoxicated mind? speech as an index of psychoactive drug effects. Neuropsychopharmacology. https://dx.doi.org/10.1038/npp.2014.80

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