The Algebraic Mind: Integrating Connectionism and Cognitive Science
Abstract
In The Algebraic Mind, Gary Marcus attempts to integrate two theories about how the mind works, one that says that the mind is a computer-like manipulator of symbols, and another that says that the mind is a large network of neurons working together in parallel. Resisting the conventional wisdom that says that if the mind is a large neural network it cannot simultaneously be a manipulator of symbols, Marcus outlines a variety of ways in which neural systems could be organized so as to manipulate symbols, and he shows why such systems are more likely to provide an adequate substrate for language and cognition than neural systems that are inconsistent with the manipulation of symbols. Concluding with a discussion of how a neurally realized system of symbol-manipulation could have evolved and how such a system could unfold developmentally within the womb, Marcus helps to set the future agenda of cognitive neuroscience. Bradford Books imprint
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Language and reasoning appear to present a problem for neural networks: we seem to be able to apply common grammatical rules to sentences regardless of the content of those sentences, and regardless of whether we have ever seen even remotely similar sentences in the training data.
“Language and reasoning appear to present a problem for neural networks (Minsky, 1991; Marcus, 2001; Hadley, 2009): we seem to be able to apply common grammatical rules to sentences regardless of the content of those sentences, and regardless of whether we have ever seen even remotely similar sentences in the training data.”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Marcus, 2001
Many top-down frameworks for understanding neural computation boil down to assuming that the brain simply optimizes a single, given cost function for a single computational architecture.
“Interestingly, many of the “top down” frameworks boil down to assuming that the brain simply optimizes a single, given cost function for a single computational architecture.”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Marcus, 2001
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- Authors
- Marcus, G. F.
- Corresponding author
- Marcus, Gary F.
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2001-04-13
- OpenAlex
- W4235344441
- Cited by
- 341
- References
- 0
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 38 · 2023: 17 · 2022: 25 · 2021: 11 · 2020: 12 · 2019: 10 · 2018: 10 · 2017: 7 · 2016: 8 · 2015: 10 · 2014: 7 · 2013: 13 · 2012: 11
- FWCI
- 14.93
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- The MIT Press eBooks (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- symbolic computation, artificial neural networks, parallel computing, computational theory of mind, cognitive architecture, language, cognition, cognitive evolution, cognitive development
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