Mapping representational mechanisms with deep neural networks
Authors
Publication Date
2022-05-05Journal Title
Synthese
ISSN
1111-4924
Publisher
Springer Netherlands
Volume
200
Issue
3
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Kieval, P. H. (2022). Mapping representational mechanisms with deep neural networks. Synthese, 200 (3) https://doi.org/10.1007/s11229-022-03694-y
Description
Funder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370
Abstract
Abstract: 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.
Keywords
Original Research, Machine learning, Cognitive neuroscience, Connectionism, RSA, Mechanistic explanation, Real patterns, Idealization
Identifiers
s11229-022-03694-y, 3694
External DOI: https://doi.org/10.1007/s11229-022-03694-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336874
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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