Trajectories of sentiment in 11,816 psychoactive narratives.

Objective: Can machine learning (ML) enable data-driven discovery of how changes in sentiment correlate with different psychoactive experiences? We investigate by training models directly on text testimonials from a diverse 52-drug pharmacopeia.

Methods: Using large language models (i.e.

Trips and neurotransmitters: Discovering principled patterns across 6850 hallucinogenic experiences.

Psychedelics probably alter states of consciousness by disrupting how the higher association cortex governs bottom-up sensory signals. Individual hallucinogenic drugs are usually studied in participants in controlled laboratory settings. Here, we have explored word usage in 6850 free-form testimonials about 27 drugs through the prism of 40 neurotransmitter receptor subtypes, which were then mapped to three-dimensional coordinates in the brain via their gene transcription levels from invasive tissue probes.