Data Mining the Brain to Decode the Mind

In Neural Mechanisms: New Challenges in the Philosophy of Neuroscience (forthcoming)
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Abstract

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience.

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Daniel Weiskopf
Georgia State University

References found in this work

Explanation: a mechanist alternative.William Bechtel & Adele Abrahamsen - 2005 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):421-441.
Can cognitive processes be inferred from neuroimaging data?Russell A. Poldrack - 2006 - Trends in Cognitive Sciences 10 (2):59-63.
In Defense of Reverse Inference.Edouard Machery - 2014 - British Journal for the Philosophy of Science 65 (2):251-267.

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