Quantitative Analysis of Narrative Reports of Psychedelic Drugs - Matthew Baggott


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Quantitative Analysis of Narrative Reports of Psychedelic Drugs

Matthew Baggott, PhD

Abstract: Matthew Baggott and colleagues have used machine learning techniques to classify written descriptions of psychedelics and to analyze the speech of volunteers in studies. This reveals consistencies in how we experience and remember altered states, and provides a promising way to study novel psychedelics.

Matthew Baggott is a neuroscientist who has been studying the perceptual and emotional effects of drugs like MDMA in healthy human volunteers for over 13 years. He was part of the first team to receive federal funding to administer MDMA to healthy people and he co-authored the first successful application to administer MDMA to people with PTSD. He earned a PhD in neuroscience from UC Berkeley and is currently a postdoctoral fellow at University of Chicago.

More videos available at http://psychedelicscience.org

At Psychedelic Science 2013, over 100 of the world's leading researchers and more than 1,900 international attendees gathered to share recent findings on the benefits and risks of LSD, psilocybin, MDMA, ayahuasca, ibogaine, 2C-B, ketamine, DMT, marijuana, and more, over three days of conference presentations, and two days of pre- and post-conference workshops.
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