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David E. Presti

3 papers in the library · publishing 2012-2023

Papers

MDMA

Psychedelics as Psychiatric Medications March 1, 2023 Michael C. Mithoefer, David E. Presti

MDMA was first synthesized by Merck in 1914 but not studied in humans until the 1970s–80s, when it was reported to reduce anxiety and increase emotional openness, making it a possible catalyst for psychotherapy. In 1985, after recreational use in dance scenes attracted media attention, the US government placed MDMA in Schedule 1, banning it for medical use. MDMA's pharmacological effects include releasing serotonin and other monoamines and raising oxytocin levels. Research on its effects is evolving, and links between its physiology and user experiences remain speculative. Controlled clinical trials of MDMA-assisted psychotherapy began in 2004, focusing on PTSD.

Putting Mind Back into Nature: A Tribute to Henry P. Stapp

arXiv Preprint Archive April 16, 2019 David E. Presti

Henry Stapp has argued for 60 years that the structure of quantum mechanics implies a central and irreducible role for mind—an experiential aspect of nature distinct from physical matter and energy. The paper describes this thesis and calls for interdisciplinary exploration, especially connections with neuroscience and empirical psychology, to investigate mind's role in quantum ontology.

Quantitative Analysis of Narrative Reports of Psychedelic Drugs

arXiv Preprint Archive June 1, 2012 Jeremy R. Coyle, David E. Presti, Matthew J. Baggott

Machine learning applied to 1000 written reports of 10 different drugs from the Erowid website identified distinct patterns in how people describe their experiences with each drug. A random-forest classifier using just 110 key words achieved 51.1% accuracy in identifying which drug a report described, far above the 10% expected by chance. Reports of MDMA were most distinctive (86.9% accuracy), while those for DPT were hardest to classify (20.1%). Hierarchical clustering revealed similarities between certain drugs, such as DMT and Salvia divinorum. The findings suggest that automated text analysis can uncover consistent, drug-specific features in subjective experience reports, potentially aiding hypothesis generation about new or poorly understood compounds.