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Mapping representational mechanisms with deep neural networks.

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This...

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Published in:Synthese Vol. 200; no. 3; pp. 1 - 26
Main Author: Kieval, Phillip Hintikka
Format: Article
Published: Springer Nature Jun2022
Online Access:View this record in EBSCOhost
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        atl: Mapping representational mechanisms with deep neural networks.
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        au: Kieval, Phillip Hintikka
        affil: Department of History and Philosophy of Science, University of Cambridge, Cambridge, UK
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      keyword:
        Cognitive neuroscience
        Connectionism
        Idealization
        Machine learning
        Mechanistic explanation
        Real patterns
        RSA
      ab: The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (MVPA) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis (RSA). I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain.
      pubtype: Academic Journal
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      src: R
    language: English
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