Weiskopf, Daniel (2022) The Predictive Turn in Neuroscience. [Preprint]

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Abstract

Neuroscientists have in recent years turned to building models that aim to generate predictions rather than explanations. This “predictive turn” has swept across domains including law, marketing, and neuropsychiatry. Yet the norms of prediction remain undertheorized relative to those of explanation. I examine two styles of predictive modeling and show how they exemplify the normative dynamics at work in prediction. I propose an account of how predictive models, conceived of as technological devices for aiding decision-making, can come to be adequate for purposes that are defined by both their guiding research questions and their larger social context of application.

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Item Type:	Preprint
Creators:	Creators Email ORCID Weiskopf, Daniel dweiskopf@gsu.edu 0000-0002-2788-2280
Keywords:	Prediction; modeling; neuroscience; biomarkers; values in science
Subjects:	General Issues > Models and Idealization Specific Sciences > Neuroscience General Issues > Technology General Issues > Values In Science
Depositing User:	Daniel Weiskopf
Date Deposited:	20 Mar 2022 03:37
Last Modified:	20 Mar 2022 03:37
Item ID:	20357
Subjects:	General Issues > Models and Idealization Specific Sciences > Neuroscience General Issues > Technology General Issues > Values In Science
Date:	March 2022
URI:	https://philsci-archive.pitt.edu/id/eprint/20357

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