Fodor and Pylyshyn’s Critique of Connectionism and the Brain as Basis of the Mind
Bibliographic Data
| ID | 21252013 |
|---|---|
| Authors | Christoph von der Malsburg (0000-0003-2978-6900, SIB Swiss Institute of Bioinformatics, corresponding author) |
| Year | 2025 |
| Volume | 8 |
| Issue | 3 |
| Pages | 824-838 |
| Publication date | 2025-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Human Arenas (JOURNAL) |
| Journal identifiers | ISSN: 2522-5790 • E-ISSN: 2522-5804 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s42087-024-00452-z |
| OpenAlex | W4404085490 |
| Language | EN |
| Citations received | 1 |
| References cited | 44 |
To this day there is no satisfactory answer to the question how mental patterns correspond to physical states of our brain. For more than six decades, progress has been held up by the logjam between two traditions, one inspired by neuroscience, the other by digital computing. This logjam is well illuminated by Fodor and Pylyshyn’s article of 1988, which is mainly devoted to a critique of what they call Connectionism, but also lays bare weaknesses of the Classical approach which they defend. As recent machine learning breakthroughs may be expected to shed new light on the issue, it seems time to arrive at a synthesis of the connectionist neural approach and the classical stance based on symbol processing. I will present and discuss an attempt at such synthesis in the form of structured, self-organized neural networks
Artificial neural network · Cognitive science · Connectionism · Epistemology · Strengths and weaknesses · Cognitive Science and Education Research · Computer Science · Mathematics · Neural dynamics and brain function · Philosophy · Plant and Biological Electrophysiology Studies · Psychology · Artificial Intelligence
Vision
Connectionism and cognitive architecture
AI models collapse when trained on recursively generated data
Global workspace theory of consciousness
Untersuchungen zur Lehre von der Gestalt. II
ImageNet classification with deep convolutional neural networks
Neural networks and physical systems with emergent collective computational abilities.
Am I Thinking Assemblies?
Remembering
Distributional Structure
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |