Prediction versus understanding in computationally enhanced neuroscience.
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 neu...
| Publicado en: | Synthese Vol. 199; no. 1/2; pp. 767 - 791 |
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| Formato: | Artículo |
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Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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 (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. |
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