Mapping representational mechanisms with deep neural networks
Bibliographic Data
| ID | 10814760 |
|---|---|
| Authors | Phillip Hintikka Kieval (0000-0001-5369-0322, University of Cambridge, corresponding author) |
| Year | 2022 |
| Volume | 200 |
| Issue | 3 |
| Publication date | 2022-05-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Synthese (JOURNAL) |
| Journal identifiers | ISSN: 0039-7857 • E-ISSN: 1573-0964 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11229-022-03694-y |
| OpenAlex | W4229053789 |
| Language | EN |
| Citations received | 4 |
| References cited | 62 |
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
Abstraction · Artificial neural network · Cognition · Cognitive science · Connectionism · Epistemology · Idealization · Metaphysics · Philosophy of biology · Philosophy of language · Philosophy of mind · Philosophy of science · Set (abstract data type) · Action Observation and Synchronization · Artificial Intelligence · Cognitive Science and Education Research · Computer Science · Neuroscience · Philosophy and History of Science · Psychology
Language, Thought, and Other Biological Categories
Simulation and Similarity
How the Laws of Physics Lie
Making Things Happen
Models as Mediators
Explaining Behavior
The Mind Doesn't Work That Way
Parallel Distributed Processing
Idealization and the Aims of Science
True Enough
Real Patterns
"How Does It Work?" versus "What Are the Laws?"
Mastering the game of Go with deep neural networks and tree search
Three Kinds of Idealization
Deep learning
All at Sea in Semantic Space
Explanation
Representational similarity analysis in neuroimaging
The physics of representation
Empiricism without magic
Multivariate pattern analysis and the search for neural representations
Hypothetical Pattern Idealization and Explanatory Models
The Explanatory Force of Dynamical and Mathematical Models in Neuroscience
From Implausible Artificial Neurons to Idealized Cognitive Models
Thinking about Mechanisms
The Nature of Dynamical Explanation
Decoding the Brain
| Unique citing works | 4 |
|---|---|
| Citations per year | 1,33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |