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
Items 201–300 of 341
Evidence from formal logical reasoning reveals that the language of thought is not natural language
Kean, H., Fung, A., Jaggers, P., Chen, J., Rule, J. S., Benn, Y., Tenenbaum, J. B., Piantadosi, S. T., Varley, R. A., & Fedorenko, E. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2520095123 · Cited by 2
Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence
Queloz, M. (2025) · Minds and Machines · doi:10.1007/s11023-025-09738-9 · Cited by 3
The detection of algebraic auditory structures emerges with self-supervised learning
Orhan, P., Boubenec, Y., & King, J.-R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013271 · Cited by 5
Leivada, E., Marcus, G., Günther, F., & Murphy, E. (2026) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2025.0008 · Cited by 28
Self-citationA Neurocomputational Approach to Decision Making and Aging
Mata, R. (2005) · Lecture notes in computer science · doi:10.1007/11550907_65 · Cited by 2
Optimism for the future of unified theories
Anderson, J. R., & Lebiere, C. J. (2003) · Behavioral and Brain Sciences · doi:10.1017/s0140525x03460135 · Cited by 2
Gedächtnis: Informationsverarbeitung in der kognitiven Architektur
Tobinski, D. A. (2017) · Springer-Lehrbuch · doi:10.1007/978-3-662-53948-4_3 · Cited by 2
Two types of motifs enhance human recall and generalization of long sequences
Wu, S.-C., Thalmann, M., & Schulz, E. (2025) · Communications Psychology · doi:10.1038/s44271-024-00180-8 · Cited by 6
A Neural Network Model for a Hierarchical Spatio-temporal Memory
Ramanathan, K., Shi, L., Li, J., Lim, K. G., Li, M., Ang, Z. P., & Chong, T. C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02490-0_53 · Cited by 2
Design for a Darwinian Brain: Part 1. Philosophy and Neuroscience
Fernando, C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39802-5_7 · Cited by 2
Psychologiczne konsekwencje poczucia zagrożenia doświadczanego w sytuacji pracy
Mamcarz, P. J., Mamcarz, I., & Suchocka, L. (2012) · Acta Universitatis Lodziensis Folia Psychologica · doi:10.18778/1427-969x.16.02 · Cited by 2
Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers
İnce, O. B., Zeraati, T., Yagcioglu, S., Yaghoobzadeh, Y., Erdem, E., & Erdem, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.867 · Cited by 2
Assessing the Representation of Phonological Rules by a Production Study of Non-Words in Coratino
Bucci, J., Lorusso, P., Gerber, S., et al. (2019) · Phonetica · doi:10.1159/000504452 · Cited by 2
Training neural networks to encode symbols enables combinatorial generalization
Vankov, I., & Bowers, J. S. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0309 · Cited by 12
The modules behind the learning
Marcus, G. F. (2002) · Developmental Science · doi:10.1111/1467-7687.00218_2 · Cited by 2
Self-citationFrequently Asked Questions for: The Atoms of Neural ComputationPreprint
Marcus, G., Marblestone, A. H., & Dean, T. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/010983 · Cited by 2
Self-citationThe unbounded productivity of (sign) language
Berent, I., & Dupuis, A. (2017) · The Mental Lexicon · doi:10.1075/ml.17016.ber · Cited by 2
Analogy is indispensable but rule is a must: Insights from Turkish
NAKIPOĞLU, M., Uzundağ, B. A., & KETREZ, F. N. (2022) · Journal of Child Language · doi:10.1017/s0305000921000921 · Cited by 1
Time Slows Down in the Future: Aging and the Brain Rhythms of Language
Murphy, E. (2025) · Topics in Cognitive Science · doi:10.1111/tops.70019 · Cited by 1
Pay Attention to the Foreground in Object-Centric Learning
Tian, P., Yang, S., Yu, H., et al. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02819 · Cited by 1
Climbing towards the NLU of the universal reading of shei ‘who’
Chung, M.-H., & Chou, C. T. (2025) · Concentric Studies in Linguistics · doi:10.1075/consl.24041.chu · Cited by 1
Three arguments for abstraction in phonology
Berent, I. (2026) · Glossa a journal of general linguistics · doi:10.16995/glossa.27277 · Cited by 1
Hernández-Espinosa, A., Ozelim, L., Abrahão, F. S., & Zenil, H. (2026) · Nature Communications · doi:10.1038/s41467-026-73289-5 · Cited by 1
Design for a Darwinian Brain: Part 2. Cognitive Architecture
Fernando, C., Vasas, V., & Churchill, A. W. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39802-5_8 · Cited by 1
Languages as evolving organisms –Thesolution to the logical problem of language evolution?
Behme, C. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x08005025 · Cited by 1
A depictive neural model for the representation of motion verbs
Rao, S., & Aleksander, I. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0400-5 · Cited by 1
A NEURAL MECHANISM FOR FUNCTION APPLICATION IN VISUAL AWARENESS MODELS
Rao, S., & Aleksander, I. (2011) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026811003094 · Cited by 1
Fluid Construction Grammar in the Brain
Fernando, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34120-5_13 · Cited by 1
Rondal, J. A. (2015) · Cognitive Linguistic Studies · doi:10.1075/cogls.2.2.01ron · Cited by 2
Group Minds in Historical Perspective
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.012 · Cited by 1
Distinguishing Representations as Origin and Representations as Input: Roles for Individual Neurons
Edwards, J. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01537 · Cited by 1
Kurtz, K. J. (2025) · Cognitive Science · doi:10.1111/cogs.70033 · Cited by 1
The need for ethical guidelines in mathematical research in the time of generative AI
Pantsar, M. (2025) · AI and Ethics · doi:10.1007/s43681-025-00660-5 · Cited by 1
Language and the algebraic mind
Qin, J., & Opfer, J. E. (2025) · Memory & Cognition · doi:10.3758/s13421-025-01738-5 · Cited by 1
Preferring Rules to Similarity: Coherence, goals, and commitment
Pothos, E. M. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05470016 · Cited by 1
Smith, S. C., & Ramamoorthy, S. (2020) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.319.7 · Cited by 1
Machery, É. (2010) · doi:10.1002/9781444315578.ch11 · Cited by 1
Proxytypes and Linguistic Nativism
Collins, J. M. (2006) · Synthese · doi:10.1007/s11229-005-4063-6 · Cited by 1
Li, P., & Zhao, X. (2012) · Oxford University Press eBooks · doi:10.1093/obo/9780199772810-0010 · Cited by 1
The Broad Foundations of Adaptationist-Computational Evolutionary Psychology
Dahlgrün, M. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1387-9_2
Metaphysics, Mind, and Science: Two Views of Realization
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.006
Language, Connectionist and Symbolic Representations of
Steedman, M. J. (2005) · Encyclopedia of Cognitive Science · doi:10.1002/0470018860.s00292
Driscoll, C. (2013) · Philosophical Psychology · doi:10.1080/09515089.2013.785068
MODELLING NEURONAL GRAMMAR CIRCUITS
Hoya, T. (2008) · ENGLISH LINGUISTICS · doi:10.9793/elsj1984.25.475
Intra dynamic scenario relations and a dynamic decision making algorithm
Liang, Y., Wang, Y., & Jarmasz, J. (2011) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2011.6019890
The Group Mind Hypothesis in Contemporary Biology and Social Science
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.013
Computational Modeling of Various Cognitive Functionalities and Domains
Sun, R. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.010
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.004
Applications of model theory to the study of ‘soft’ universals in language
Gilkey, P., Καρούσου, Α., & Ornat, S. L. (2016) · Mathematical Methods in the Applied Sciences · doi:10.1002/mma.3778
Intentionality and Phenomenology
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.011
The Embedded Mind and Cognition
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.009
Individualism: Philosophical Foundations
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.005
Verdejo, V. M. (2016) · Dialogue · doi:10.1017/s0012217316000408
Individuals, Psychology, and the Mind
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.003
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.014
Representation of Neuro-Information and Knowledge
van der Velde, F. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-018-1.ch002
From Streams of Observations to Knowledge-Level Productive Predictions
Wernsdorfer, M., & Schmid, U. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3682-8.ch013
Context-Sensitive Realizations
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.007
Representation, Computation, and Cognitive Science
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.008
Modal Ω-Logic: Automata, Neo-Logicism, and Set-Theoretic Realism
Elohim, D. (2019) · Philosophical studies series · doi:10.1007/978-3-030-01800-9_4
A representational asymmetry for composition in the human left-middle temporal gyrusPreprint
Frankland, S., & Greene, J. D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/844183 · Cited by 1
A Note on Densities of the L2 Mental Lexicon Network
Higuchi, Y., Takahashi, R., & Tanaka, M. N. (2019) · Open Journal of Modern Linguistics · doi:10.4236/ojml.2019.96037
Spike-based symbolic computations on bit strings and numbersPreprint
Kraišniković, C., Maass, W., & Legenstein, R. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.14.452347
Probabilistic Feature Attention as an Alternative to Variables in Phonotactic Learning
Prickett, B. (2021) · Linguistic Inquiry · doi:10.1162/ling_a_00440
Jackendoff, R. S., & Audring, J. (2021) · John Benjamins Publishing Company eBooks · doi:10.1075/z.238.07jac
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.010
(2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.001
Wilson, R. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511606847.015
Clark, A., & Lappin, S. (2011) · doi:10.1002/9781444390568.refs
Learning and Memory, Transfer and Interference
Norman, K. L. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316212554.008
(2018) · De Boeck Supérieur eBooks · doi:10.3917/dbu.rossi.2018.01.0237
Sun, R. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.004
Chen, P.-H. P., Osei-Tutu, K. O. A., & Taherkhani, N. (2023) · Studies in Language · doi:10.1075/sl.22014.che
A Derived Functional Framework for Cognitive Computational Learning System
Feng, J., Zhang, & Luo (2026) · Cognitive Computation · doi:10.1007/s12559-026-10575-w
(2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.004
Understanding Quasiregularity and Continua in Language: Beyond “Words and Rules”
Caldwell‐Harris, C. L. (2023) · Springer eBooks · doi:10.1007/978-981-19-5276-0_10
The Evolution of Fodor’s Case against Concept Learning
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0024
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0022
Fodor’s Biological Account of Concept Acquisition—and the Importance of Cultural Learning
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0026
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0023
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0028
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0025
The Argument from Early Development (2)
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0009
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0007
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0019
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0004
The Argument from Universality
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0011
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.002.0005
The Argument from Neural Wiring
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0013
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0010
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0017
The Argument from Cognitive and Behavioural Quirks
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0015
Why the Rationalism-Empiricism Debate Isn’t the Nature-Nurture Debate
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0003
The Argument from Early Development (1)
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0008
Abstraction and the Allure of Illusory Explanation
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0005
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0018
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0001
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.002.0007
Laurence, S., & Margolis, E. (2024) · Oxford University Press eBooks · doi:10.1093/9780191925375.003.0021
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
