Date:
Fri, 01/24/2025 - 10:30am - 12:00pm
Location:
CCRMA Seminar Room
Event Type:
Hearing Seminar
The recent renaissance in psychedelic medicine has highlighted the crucial role of "set and setting" in determining therapeutic outcomes, with music as a particularly important factor. This talk explores the intersection of psychedelics and music, beginning with a historical overview of classical psychedelics and their applications in both traditional and modern contexts. I will examine the psychological and neuroscientific foundations of psychedelic therapy, with a focus on how music may interact with the mechanisms of action of psychedelics to facilitate emotional breakthroughs and therapeutic change. I will also present a descriptive analyses of music information retrieval (MIR) features from established psychedelic therapy playlists, revealing patterns in the musical characteristics commonly employed in clinical settings. Despite growing interest in this field, empirical data on music's role in psychedelic experiences remains limited. I propose several strategies for systematic data collection in both clinical trials and naturalistic settings, aiming to build an evidence base for optimizing music selection in psychedelic treatments.
At the frontier of neuroscience and health technology innovation, Bob Dougherty is applying data-driven methods to advance how we understand and improve mental health. As Vice President of Digital Health Research at Compass Pathways, he's driving data science and machine learning research into psilocybin therapy to make psychedelic treatments safer and more accessible. Before joining Compass, Bob was VP of Research at Mindstrong Health, where he led developments in passive digital biomarkers to create a "digital fingerprint" of mental health through smartphone interactions. Prior to Mindstrong, Bob was Research Director of the Stanford Center for Neurobiological Imaging. Throughout his scientific career, Bob has published extensively in the fields of psychology, psychiatry, neuroscience, statistics, magnetic resonance technology, and machine learning.