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 |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=153650806&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 153650806 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Dec2021 vid: 199 iid: 1/2 pid: 237 pub: Springer Nature artinfo: ui: 153650806 10.1007/s11229-020-02713-0 ppf: 767 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 525KB tig: atl: Prediction versus understanding in computationally enhanced neuroscience. aug: au: Chirimuuta, M. affil: Department of History and Philosophy of Science, University of Pittsburgh, 1101 Cathedral of Learning, 4200 Fifth Avenue, 15260, Pittsburgh, PA, USA su: Artificial neural networks Philosophy of science Visual cortex Machine learning sug: subj: Artificial neural networks Philosophy of science Visual cortex Machine learning keyword: Artificial intelligence Computational modelling Explanation Philosophy of neuroscience Understanding ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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