The Algebraic Mind: Integrating Connectionism and Cognitive Science

Toward better mutual understanding

Jackendoff, R. S. (2003) · Behavioral and Brain Sciences · doi:10.1017/s0140525x03510159 · Cited by 25

Syntactic systematicity arising from semantic predictions in a Hebbian-competitive network

Hadley, R. F., Rotaru-Varga, Á., Arnold, D. V., et al. (2001) · Connection Science · doi:10.1080/09540090110052996 · Cited by 25

A review of computational models of basic rule learning: The neural-symbolic debate and beyond

Alhama, R. G., & Zuidema, W. H. (2019) · Psychonomic Bulletin & Review · doi:10.3758/s13423-019-01602-z · Cited by 27

Plasticity and Nativism: Towards a Resolution of an Apparent Paradox

Marcus, G. F. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44597-8_27 · Cited by 24

The problem of rapid variable creation

Hadley, R. F. (2009) · Neural Computation · doi:10.1162/neco.2008.07-07-572 · Cited by 23

ROSE: A Universal Neural Grammar

Murphy, E. (2025) · Cognitive Neuroscience · doi:10.1080/17588928.2025.2523875 · Cited by 29

The Binding Problem 2.0: Beyond Perceptual Features

Yu, X., & Lau, E. F. (2023) · Cognitive Science · doi:10.1111/cogs.13244 · Cited by 23

The reality of a universal language faculty

Pinker, S. A., & Jackendoff, R. S. (2009) · Behavioral and Brain Sciences · doi:10.1017/s0140525x09990720 · Cited by 23

Neural networks discover a near-identity relation to distinguish simple syntactic forms

Shultz, T. R., & Bale, A. C. (2006) · Minds and Machines · doi:10.1007/s11023-006-9029-z · Cited by 23

Elicitation of the passé composé in French preschoolers with and without specific language impairment

Royle, P., & Thordardottir, E. T. (2008) · Applied Psycholinguistics · doi:10.1017/s0142716408080168 · Cited by 23

Sequence Learning in 4-Month-Old Infants: Do Infants Represent Ordinal Information?

Lewkowicz, D. J., & Berent, I. (2009) · Child Development · doi:10.1111/j.1467-8624.2009.01369.x · Cited by 22

Empiricism in the foundations of cognition

Childers, T., Hvorecký, J., & Majer, O. (2021) · AI & Society · doi:10.1007/s00146-021-01287-w · Cited by 27

The Building Blocks of Thought

Laurence, S., & Margolis, E. (2024) · doi:10.1093/9780191925375.001.0001 · Cited by 26

Interpreting dissociations between regular and irregular past-tense morphology: Evidence from event-related potentials

Justus, T. C., Larsen, J., Davies, P., et al. (2008) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.8.2.178 · Cited by 21

Dynamic searching in the brain

Mizraji, E., Pomi, A., & Valle-Lisboa, J. C. (2009) · Cognitive Neurodynamics · doi:10.1007/s11571-009-9084-2 · Cited by 21

On The Proper Treatment of Semantic Systematicity

Hadley, R. F. (2004) · Minds and Machines · doi:10.1023/b:mind.0000021693.67203.46 · Cited by 21

Information Processing, Computation, and CognitionPreprint

Piccinini, G., & Scarantino, A. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1635411 · Cited by 21

The Acquisition of Anaphora by Simple Recurrent Networks

Frank, R., Mathis, D. W., & Badecker, W. (2013) · Language Acquisition · doi:10.1080/10489223.2013.796950 · Cited by 19

THE PHASE TRANSITION IN HUMAN COGNITION

Spivey, M. J., ANDERSON, S. E., & Dale, R. A. C. (2009) · New Mathematics and Natural Computation · doi:10.1142/s1793005709001234 · Cited by 20

On the generality and limits of abstraction in rats and humans

Urcelay, G. P., & Miller, R. R. (2009) · Animal Cognition · doi:10.1007/s10071-009-0295-z · Cited by 19

Toward Integrative Dynamic Models for Adaptive Perspective Taking

Duran, N. D., Dale, R. A. C., & Galati, A. (2016) · Topics in Cognitive Science · doi:10.1111/tops.12219 · Cited by 19

Abstract Neural Representations of Category Membership beyond Information Coding Stimulus or Response

Mok, R. M., & Love, B. C. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01651 · Cited by 20

Universal and particular in morphological processing: Evidence from Hebrew

Farhy, Y., Veríssimo, J., & Clahsen, H. (2017) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2017.1310917 · Cited by 18

Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

Kühn, S., Beyn, W., & Cruse, H. (2007) · Biological Cybernetics · doi:10.1007/s00422-006-0137-x · Cited by 17

Connectionist Models of Cognition

Thomas, M. S. C., & McClelland, J. L. (2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.005 · Cited by 17

The Nature of Symbols in the Language of Thought

Schneider, S. M. (2009) · Mind & Language · doi:10.1111/j.1468-0017.2009.01373.x · Cited by 16

Perceptual Learning

Kellman, P. J. (2002) · Wiley eBooks · doi:10.1002/0471214426.pas0307 · Cited by 35

Le système interactionnel : connexions sémantiques et contextique relationnelle

Wagener, A. (2012) · Nouvelles perspectives en sciences sociales · doi:10.7202/1013055ar · Cited by 15

Infants differentially extract rules from language

Berent, I., de la Cruz‐Pavía, I., Brentari, D., & Gervain, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-99539-8 · Cited by 15

Insight, Working Through, and Practice: The Role of Procedural Knowledge

Rosenblatt, A. D. (2004) · Journal of the American Psychoanalytic Association · doi:10.1177/00030651040520011901 · Cited by 14

Understanding the Computational Demands Underlying Visual Reasoning

Vaishnav, M., Cadène, R., Alamia, A., Linsley, D. A., VanRullen, R., & Serre, T. (2022) · Neural Computation · doi:10.1162/neco_a_01485 · Cited by 18

A Discrete-Event Simulation of Claude Lévi-Strauss’ Structural Analysis of Myths Based on Symmetry and Double Twist Transformations

Santucci, J.-F., Doja, A., & Capocchi, L. (2020) · Symmetry · doi:10.3390/sym12101706 · Cited by 14

Languages of thought need to be distinguished from learning mechanisms, and nothing yet rules out multiple distinctively human learning systems

Tetzlaff, M., & Carruthers, P. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x08003749 · Cited by 13

Abstract and associatively based representations in human sequence learning

Spiegel, R., & McLaren, I. P. L. (2003) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2003.1311 · Cited by 14

Hierarchy in language interpretation: evidence from behavioural experiments and computational modelling

Coopmans, C. W., de Hoop, H., Kaushik, K., Peter Hagoort, A., & Martin, A. E. (2021) · Language Cognition and Neuroscience · doi:10.1080/23273798.2021.1980595 · Cited by 14

Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning

Zhang, C., Xie, S., Jia, B., Wu, Y. N., Zhu, S. C., & Zhu, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_40 · Cited by 12

Creating New Informational Primitives in Minds and Machines

Cariani, P. (2012) · Springer eBooks · doi:10.1007/978-3-642-31727-9_15 · Cited by 12

Cognitive Science and Cognitive Development

Keil, F. C. (2007) · Wiley eBooks · doi:10.1002/9780470147658.chpsy0214 · Cited by 12

How Many Mechanisms Are Needed to Analyze Speech? A Connectionist Simulation of Structural Rule Learning in Artificial Language Acquisition

Laakso, A., & Calvo, P. (2011) · Cognitive Sciences · doi:10.1111/j.1551-6709.2011.01191.x · Cited by 13

Can we get human nature right?

Berent, I. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2108274118 · Cited by 12

Characterizing linguistic structure with mutual information

Pothos, E. M., & Juola, P. (2006) · British Journal of Psychology · doi:10.1348/000712606x122760 · Cited by 11

What the <0.70, 1.17, 0.99, 1.07> is a Symbol?

Berkeley, I. S. N. (2007) · Minds and Machines · doi:10.1007/s11023-007-9086-y · Cited by 11

Cellular computation and cognition

Fitch, W. T. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1107876 · Cited by 10

A Phone in a Basket Looks Like a Knife in a Cup: Role-Filler Independence in Visual Processing

Hafri, A., Bonner, M., Landau, B., & Firestone, C. (2024) · Open Mind · doi:10.1162/opmi_a_00146 · Cited by 16

A simple model from a powerful framework that spans levels of analysis

Rogers, T. T., & McClelland, J. L. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x08006067 · Cited by 10

Structured models of semantic cognition

Kemp, C., & Tenenbaum, J. B. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x08005931 · Cited by 10

Acquisition of adjectives in Quebec French as revealed by elicitation data

Royle, P., & Valois, D. (2010) · Journal of French Language Studies · doi:10.1017/s0959269510000013 · Cited by 10

Ritual ecology

McGraw, J. J., & Krátký, J. (2017) · Journal of Material Culture · doi:10.1177/1359183517704881 · Cited by 13

Two Apparent ‘Counterexamples’ To Marcus: A Closer Look

Vilcu, M., & Hadley, R. F. (2005) · Minds and Machines · doi:10.1007/s11023-005-9000-4 · Cited by 10

Nonadjacent Dependency Learning in Cantonese-Speaking Children With and Without a History of Specific Language Impairment

Iao, L., Ng, L. Y., Wong, A. M. ‐., et al. (2017) · Journal of Speech Language and Hearing Research · doi:10.1044/2016_jslhr-l-15-0232 · Cited by 10

Language Development in Childhood

Hoff, E. (2003) · Handbook of Psychology · doi:10.1002/0471264385.wei0607 · Cited by 10

Plus or Minus 30 Years in the Language Sciences

Newport, E. L. (2010) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01102.x · Cited by 9

What Is Statistical Learning, and What Statistical Learning Is Not

Saffran, J. R. (2009) · doi:10.1093/acprof:oso/9780195331059.003.0009 · Cited by 9

Preventing perseveration in language production

Stemberger, J. P. (2009) · Language and Cognitive Processes · doi:10.1080/01690960902836624 · Cited by 10

The mind doesn't work that way: The scope and limits of computational psychology. By Jerry Fodor. Cambridge, MA: MIT Press, 2000. Pp. 126.

Jackendoff, R. S. (2002) · Language · doi:10.1353/lan.2002.0024 · Cited by 9

Abstract neural representations of category membership beyond information coding stimulus or responsePreprint

Mok, R. M., & Love, B. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.13.947341 · Cited by 8

Configural relations in humans and deep convolutional neural networks

Baker, N., Garrigan, P., Phillips, A., & Kellman, P. J. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.961595 · Cited by 7

Young minds’ quest for regularity: Evidence from the Turkish causative

Nakipoğlu, M., Uzundağ, B. A., & SARIGÜL, Ö. (2021) · Journal of Child Language · doi:10.1017/s0305000921000672 · Cited by 7

Time-Based Binding as a Solution to and a Limitation for Flexible Cognition

Senoussi, M., Verbeke, P., & Verguts, T. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.798061 · Cited by 10

Dual-process theories, cognitive architectures, and hybrid neural-symbolic models

Sun, R. (2024) · Neurosymbolic Artificial Intelligence · doi:10.3233/nai-240720 · Cited by 7

Amodal phonology

Berent, I., Bat‐El, O., Brentari, D., Andan, Q., & Vaknin‐Nusbaum, V. (2020) · Journal of Linguistics · doi:10.1017/s0022226720000298 · Cited by 7

Space—The Primal Frontier? Spatial Cognition and the Origins of Concepts

Keil, F. C. (2008) · Philosophical Psychology · doi:10.1080/09515080801980203 · Cited by 7

On the Origins of Phonology

Berent, I. (2017) · Current Directions in Psychological Science · doi:10.1177/0963721416688891 · Cited by 7

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

Velichko, A., Борисков, П. П., Belyaev, M. A., & Putrolaynen, V. V. (2023) · Sensors · doi:10.3390/s23167137 · Cited by 6

LOT, CTM, and the Elephant in the Room

Schneider, S. M. (2009) · Synthese · doi:10.1007/s11229-009-9581-1 · Cited by 6

From Reinforcement Learning Towards Artificial General Intelligence

Rocha, F. M., Costa, V. S., & Reis, L. P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-45691-7_37 · Cited by 6

Solving Bongard Problems With a Visual Language and Pragmatic Constraints

Depeweg, S., Rothkopf, C. A., & Jäkel, F. (2024) · Cognitive Science · doi:10.1111/cogs.13432 · Cited by 35

An emergent approach to analogical inference

Thibodeau, P. H., Flusberg, S. J., Glick, J. J., & Sternberg, D. A. (2013) · Connection Science · doi:10.1080/09540091.2013.821458 · Cited by 6

Linguistics and the explanatory economy

Dupré, G. (2019) · Synthese · doi:10.1007/s11229-019-02290-x · Cited by 6

The systematicity challenge to anti-representational dynamicism

Verdejo, V. M. (2014) · Synthese · doi:10.1007/s11229-014-0597-9 · Cited by 6

How thalamic relays might orchestrate supervised deep training and symbolic computation in the brainPreprint

Hayworth, K. J., & Marblestone, A. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/304980 · Cited by 4

Abstract representations in temporal cortex support generative linguistic processing

Gow, D. W., Avcu, E., Schoenhaut, A. M., Sorensen, D. O., & Ahlfors, S. P. (2022) · Language Cognition and Neuroscience · doi:10.1080/23273798.2022.2157029 · Cited by 6

Cognitive Semiotics: An Overview

Azcárate, A. L. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.101848 · Cited by 6

Modelling language development and evolution with the benefit of hindsight

van der Velde, F. (2005) · Connection Science · doi:10.1080/09540090500177497 · Cited by 5

A defence of functional modularity

Hadley, R. F. (2003) · Connection Science · doi:10.1080/09540090310001597566 · Cited by 5

Behavioural evidence for implicative paradigmatic relations

Copot, M., & Bonami, O. (2023) · The Mental Lexicon · doi:10.1075/ml.22020.cop · Cited by 6

What is the role of the sensori-motor system in semantics? Evidence from a brain-constrained neural network model of action verbs

Carriere, M., Tomasello, R., & Friedemann Pulvermüller, A. (2025) · Language Cognition and Neuroscience · doi:10.1080/23273798.2025.2596802 · Cited by 5

Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks

Brugiapaglia, S., Liu, M., & Tupper, P. (2022) · Neural Computation · doi:10.1162/neco_a_01510 · Cited by 7

Cognitive Systems, Predictive Processing, and the Self

Rupert, R. D. (2021) · Review of Philosophy and Psychology · doi:10.1007/s13164-021-00577-y · Cited by 4

Using Natural Language Processing to Improve Document Categorization with Associative Networks

Bloom, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31178-9_18 · Cited by 4

CONSCIOUSNESS AS A PROCESS OF QUERIES AND ANSWERS IN ARCHITECTURES BASED ON IN SITU REPRESENTATIONS

van der Velde, F. (2013) · International Journal of Machine Consciousness · doi:10.1142/s1793843013400039 · Cited by 4

Bilingualism, Language Development and Processing across the Lifespan

Herschensohn, J. (2022) · Bilingual processing and acquisition · doi:10.1075/bpa.15 · Cited by 5

Systematic Human Learning and Generalization From a Brief Tutorial With Explanatory Feedback

Nam, A., & McClelland, J. L. (2024) · Open Mind · doi:10.1162/opmi_a_00123 · Cited by 12

Requirements Statements Are Transfer Functions: An Insight from Model‐Based Systems Engineering

Schindel, W. D. (2024) · Insight · doi:10.1002/inst.12506 · Cited by 4

Deep Learning of Inflection and the Cell-Filling Problem

Cardillo, F. A., Ferro, M., Marzi, C., & Pirrelli, V. (2018) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.540 · Cited by 5

Pluralist neurophenomenology: a reply to Lopes

Yoshimi, J. (2023) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-023-09892-4 · Cited by 4

Semantic Effects in Morphological Priming: The Case of Hebrew Stems

Farhy, Y., & Veríssimo, J. (2018) · Language and Speech · doi:10.1177/0023830918811863 · Cited by 4

Mixing the Old with the New and the New with the Old: Combining Prior and Current Knowledge in Conceptual Change

Mareschal, D., & Westermann, G. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195331059.003.0011 · Cited by 4

The relational processing limits of classic and contemporary neural network models of language processing

Puebla, G., Martin, A. E., & Doumas, L. A. A. (2019) · Language Cognition and Neuroscience · doi:10.1080/23273798.2020.1821906 · Cited by 17

Overcoming classic challenges for artificial neural networks by providing incentives and practice

Irie, K., & Lake, B. M. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01121-8 · Cited by 10

Semi-supervised learning from demonstration through program synthesis: An inspection robot case study

Smith, S. C., & Ramamoorthy, S. (2020) · Edinburgh Napier Research Repository (Edinburgh Napier University) · Cited by 3

An information theoretic score for learning hierarchical concepts

Madani, O. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1082502 · Cited by 3

Can Deep CNNs Avoid Infinite Regress/Circularity in Content Constitution?

Lopes, J. (2023) · Minds and Machines · doi:10.1007/s11023-023-09642-0 · Cited by 3

Mindfulness, Authentic Connection, and Making “Right” Decisions: Using Neuroscience to Build a Bridge with the Person-Centered Approach

Ryback, D. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7141-7_8 · Cited by 3

The “Strong Programme”, Normativity, and Social Causes

Calvert-Minor, C. (2008) · Journal for the Theory of Social Behaviour · doi:10.1111/j.1468-5914.2008.00355.x · Cited by 3

From Parallel Mathematical Description of Action to Unparalleled Outcome of Abstraction: A Comparative Analysis

Nemati, F. (2015) · Archives of Neuroscience · doi:10.5812/archneurosci.22573 · Cited by 3

A Dual Simple Recurrent Network Model for Chunking and Abstract Processes in Sequence Learning

Wang, L., Feng, Y., Fu, Q., Wang, J., Sun, X., Fu, X., Zhang, L., & Zhang, Y. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.587405 · Cited by 3

Harnessing the Symmetry of Convolutions for Systematic Generalisation

Mitchell, J., & Bowers, J. S. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207183 · Cited by 4

Can connectionist models of phonology assembly account for phonology?

Berent, I. (2001) · Psychonomic Bulletin & Review · doi:10.3758/bf03196202 · Cited by 3

Morphological Processing

Baayen, R. H., & Schreuder, R. (2005) · Encyclopedia of Cognitive Science · doi:10.1002/0470018860.s00254 · Cited by 3

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ

Атабекова, Б. Б. (2024) · Habaršy. Pedagogika ġylymdary seriâsy · doi:10.51889/2959-5762.2023.80.4.019

Prim�rzerlegung in Steinschen Algebren

Förster, O. (1964) · Mathematische Annalen · doi:10.1007/bf01362567 · Cited by 32

�ber unirationale Scharen auf algebraischen Mannigfaltigkeiten

Reich, L. (1966) · Mathematische Annalen · doi:10.1007/bf01364537 · Cited by 4