Low doses of psilocybin (microdoses) can be detected in natural speech. In a double-blind, placebo-controlled experiment, participants given 0.5 g of psilocybin mushrooms showed significant differences in verbosity and sentiment scores compared to placebo, though semantic variability did not differ. Machine learning classifiers using these speech metrics distinguished between the psilocybin and placebo conditions with high accuracy (AUC≈0.8). These findings suggest that unconstrained natural language may serve as a practical, low-cost tool for monitoring microdosing effects, addressing limitations of existing questionnaires designed for larger psychedelic doses.
Natural speech can reveal whether someone has taken a microdose of psilocybin. In a double-blind, placebo-controlled experiment, 34 healthy adults provided speech samples after consuming either 0.5 grams of psilocybin mushrooms or a placebo. Machine learning classifiers distinguished between the two conditions with high accuracy (AUC ~0.8), based on features such as verbosity and sentiment scores, though semantic variability did not differ significantly. This suggests that low doses of serotonergic psychedelics leave detectable signatures in unconstrained speech, offering a potential low-cost, non-invasive method for monitoring microdosing regimens.
Gamma-band neural synchrony is thought to be important for cortical computation and consciousness, but prior evidence has been inconclusive due to methodological issues. By using intracranial EEG in three participants with implanted electrodes, researchers compared high-gamma connectivity (90-120 Hz) during passive wakefulness versus non-REM sleep. Connectivity and graph-theory measures were higher during wakefulness and distinguished the two states better than other frequency bands. This is the first intracranial EEG report of wake-sleep differences in high-gamma activity at both local and distant brain sites, highlighting the technique's value for studying neural correlates of consciousness.