The Electric Kool-Aid Turing Test: How Psychedelic Research Challenges Data Positivism

Authors: Emma Stamm*, Virginia Tech
Topics: Qualitative Methods, Cyberinfrastructure, Qualitative Research
Keywords: data, psychedelic research, philosophy of technology, qualitative methods
Session Type: Paper
Day: 4/10/2018
Start / End Time: 4:40 PM / 6:20 PM
Room: Iberville, Marriott, River Tower Elevators, 4th Floor
Presentation File: No File Uploaded

In this paper, I argue that qualitative research on the medicinal use of psychedelic drugs problematizes the development of data models. The paper draws from scholarship that uses qualitative methods to interpret research on psychedelics as psychotherapeutic tools. I combine this with an assessment of the positivist stance that underscores big data as a social phenomenon to explore the problems of generalizing psychedelic research — which includes accounts from those undergoing “ineffable” and difficult-to-predict experiences— for computational modeling. In doing so, I demonstrate that the use of qualitative methods in psychedelic research offers a useful intervention into the epistemological foundations of the worldview that names data, and particularly big data, as the highest sources of truth. I begin with an overview of the “psychedelic renaissance,” the recent resurgence of interest in the medicinal use of psychedelics. I then explore recent scholarship that uses qualitative approaches to supplement quantia in psychedelic research. This emerging paradigm of psychedelic research recognizes the need for qualia to make sense of deeply subjective narrative accounts. From there, I explore axioms of big data that emphasize the ways in which generalization and inductive reasoning are used to build programs designed to precisely standardize, codify and replicate human body-mind processes. This function exemplifies data positivism in practice, and can be meaningfully problematized by the qualitative turn in psychedelic research. Merging insights from new psychedelic research and critical analyses of the epistemology of data, I assert that the former is a challenge to the latter.

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