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Mapping representational mechanisms with deep neural networks

Published version
Peer-reviewed

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Authors

Kieval, Phillip Hintikka  ORCID logo  https://orcid.org/0000-0001-5369-0322

Abstract

jats:titleAbstract</jats:title>jats:pThe 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.</jats:p>

Description

Funder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370

Keywords

Original Research, Machine learning, Cognitive neuroscience, Connectionism, RSA, Mechanistic explanation, Real patterns, Idealization

Journal Title

Synthese

Conference Name

Journal ISSN

1111-4924
1573-0964

Volume Title

200

Publisher

Springer Science and Business Media LLC