Prediction versus understanding in computationally enhanced neuroscience

Synthese 199 (1-2):767-790 (2020)
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The use of machine learning instead of traditional models in neuroscience raises significant questions about the epistemic benefits of the newer methods. I draw on the literature on model intelligibility in the philosophy of science to offer some benchmarks for the interpretability of artificial neural networks used as a predictive tool in neuroscience. Following two case studies on the use of ANN’s to model motor cortex and the visual system, I argue that the benefit of providing the scientist with understanding of the brain trades off against the predictive accuracy of the models. This trade-off between prediction and understanding is better explained by a non-factivist account of scientific understanding.



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Mazviita Chirimuuta
University of Pittsburgh

References found in this work

How the laws of physics lie.Nancy Cartwright - 1983 - New York: Oxford University Press.
True Enough.Catherine Z. Elgin - 2017 - Cambridge: MIT Press.
Idealization and the Aims of Science.Angela Potochnik - 2017 - Chicago: University of Chicago Press.

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