Mapping representational mechanisms with deep neural networks

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.

Exploring the role of structuralist methodology in the neuroscience of consciousness: a defense and analysis

“…It is not necessary for downstream systems to read out whole neural spaces to consider neural pattern-similarity structures functional. We might understand state space structures as structural descriptions that provide a way to capture the functional similarities of a neural system’s underlying processes, while abstracting away from the specific properties of the mechanism that generates them ( Kieval 2022 ).…”