The DOI matches its Crossref record.
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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
In the citing paper
“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
Citations
Cited by
Sorted by citations
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Hybrid computing using a neural network with dynamic external memory
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature · doi:10.1038/nature20101 · Cited by 1,412
Darwin's mistake: Explaining the discontinuity between human and nonhuman minds
Penn, D. C., Holyoak, K. J., & Povinelli, D. J. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x08003543 · Cited by 1,174
Active perception: sensorimotor circuits as a cortical basis for language
Friedemann Pulvermüller, A., & Fadiga, L. (2010) · Nature reviews. Neuroscience · doi:10.1038/nrn2811 · Cited by 1,105
Chang, F., Dell, G. S., & Bock, K. (2006) · Psychological Review · doi:10.1037/0033-295x.113.2.234 · Cited by 984
Metadata
- 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 › Arts and Humanities › Philosophy › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review