14 June 2019
(Georgia State University)
Data Mining the Brain to Decode the Mind
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 technological applications (e.g., brain-computer interfaces and early diagnosis of neuropathology), these techniques 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.