The use of machine learning instead of traditional models in neuroscience raises sig- nificant 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 bench- marks for the interpretability of artificial neural networks (ANN’s) 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.
|Early online date||28 May 2020|
|Publication status||E-pub ahead of print - 28 May 2020|
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- School of Philosophy, Psychology and Language Sciences - Senior Lecturer In Philosophy
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