The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Letting structure emerge: connectionist and dynamical systems approaches to cognition
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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
“This article briefly presents a pro-connectionist assessment of the early empirical research into neural networks.”
From The Ergonomics of Learning: A Proposed Update to Bloom’s Taxonomy (2025) · cites this paper as McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., & Smith, L. B. (2010)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ASPECTS OF THE THEORY OF SYNTAX
Chomsky, N. (1964) · doi:10.21236/ad0616323 · Cited by 13,587
McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256
Self-citationCatastrophic Interference in Connectionist Networks: The Sequential Learning Problem
McCloskey, M., & Cohen, N. J. (1989) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60536-8 · Cited by 3,770
Distributed representations, simple recurrent networks, and grammatical structure
Elman, J. L. (1991) · Machine Learning · doi:10.1007/bf00114844 · Cited by 1,354
Probabilistic models of cognition: exploring representations and inductive biases
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568
Knowing in the context of acting: the task dynamics of the A-not-B-error
Smith, L. B., Thelen, E., Titzer, R., & McLin, D. (1999) · Psychological Review · doi:10.1037/0033-295x.106.2.235 · Cited by 496
Self-citationLearning and applying contextual constraints in sentence comprehension
St. John, M. F., & McClelland, J. L. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90008-n · Cited by 322
Self-citationStructured statistical models of inductive reasoning
Kemp, C., & Tenenbaum, J. B. (2009) · Psychological Review · doi:10.1037/a0014282 · Cited by 259
Research works printed but not linked: 49
- Sternberg, D. and McClelland, J.L. (2009) When should we expect indirect effects in human contingency learning? In Proceedings of the 31st Annual Conference of the Cognitive Science Society (Taatgen, N.A. and van Rijn, H., eds), pp. 206-211, Cognitive Science Society
- Movellan, 1993, Learning continuous probability distributions with symmetric diffusion networks, Cogn. Sci., 17, 463, 10.1207/s15516709cog1704_1 · DOI
- Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 · DOI · Paper page
- Perfors, A. et al. (2006) Poverty of the stimulus? A rational approach. In Proceedings of the 28th Annual Conference of the Cognitive Science Society, pp. 663–668, Lawrence Erlbaum Associates
- Sobel, 2004, Children's causal inferences from indirect evidence: backwards blocking and Bayesian reasoning in preschoolers, Cogn. Sci., 28, 303
- Baillargeon, 1994, How do infants learn about the physical world?, Curr. Dir. Psychol. Sci., 3, 133, 10.1111/1467-8721.ep10770614 · DOI
- Thelen, 2001, The dynamics of embodiment: A field theory of infant perseverative reaching, Behav. Brain Sci., 24, 1, 10.1017/S0140525X01003910 · DOI
- Clearfield, 2009, Cue salience and infant perseverative reaching: Tests of the dynamic field theory, Dev. Sci., 12, 26, 10.1111/j.1467-7687.2008.00769.x · DOI
- Clearfield, 2006, Young infants reach correctly in A-not-B tasks: On the development of stability and perseveration, Infant Behav. Dev., 29, 435, 10.1016/j.infbeh.2006.03.001 · DOI
- Spencer, 2001, Tests of a dynamic systems account of the A-not-B error: The influence of prior experience on the spatial memory abilities of two-year-olds, Child Dev., 72, 1327, 10.1111/1467-8624.00351 · DOI
- Diedrich, 2000, Motor memory is a factor in infant perseverative errors, Dev. Sci., 3, 479, 10.1111/1467-7687.00140 · DOI
- Lew, 2007, Postural change effects on infants’ AB task performance: Visual, postural, or spatial?, J. Exp. Child Psychol., 97, 1, 10.1016/j.jecp.2006.12.009 · DOI
- Elman, 1990, Finding structure in time, Cogn. Sci., 14, 179, 10.1207/s15516709cog1402_1 · DOI · Paper page
- Rohde, 1999, Language acquisition in the absence of explicit negative evidence: How important is starting small?, Cognition, 72, 67, 10.1016/S0010-0277(99)00031-1 · DOI
- Keil, 1981, Constraints on knowledge and cognitive development, Psychol. Rev., 88, 197, 10.1037/0033-295X.88.3.197 · DOI
- Gelman, 1990, First principles organize attention to and learning about relevant data: Number and the animate/inanimate distinction as examples, Cogn. Sci., 14, 79, 10.1207/s15516709cog1401_5 · DOI
- Colunga, 2005, From the lexicon to expectations about kinds: a role for associative learning, Psychol. Rev., 112, 347, 10.1037/0033-295X.112.2.347 · DOI
- Gelman, 1986, Categories and induction in young children, Cognition, 23, 183, 10.1016/0010-0277(86)90034-X · DOI
- Rogers, 2004, The structure and deterioration of semantic memory: A neuropsychological and computational investigation, Psychol. Rev., 111, 205, 10.1037/0033-295X.111.1.205 · DOI · Paper page
- McClelland, J.L. et al. (2009) Semantic cognition: Its nature, its development, and its neural basis. In The cognitive neurosciences IV (Gazzaniga, M., ed.), pp. 1047–1066, MIT Press · DOI
- Rogers, 2008, A simple model from a powerful framework that spans levels of analysis, Behav. Brain Sci., 31, 729, 10.1017/S0140525X08006067 · DOI
- McClelland, 1998, Familiarity breeds differentiation: A subjective-likelihood approach to the effeccts of experience in recognition memory, Psychol. Rev., 105, 724, 10.1037/0033-295X.105.4.734-760 · DOI
- Ratcliff, 1990, Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions, Psychol. Rev., 97, 285, 10.1037/0033-295X.97.2.285 · DOI
- Feldman, 2009, The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference, Psychol. Rev., 116, 752, 10.1037/a0017196 · DOI
- Hay, 2001, Lexical frequency in morphology: Is everything relative?, Linguistics, 39, 1041, 10.1515/ling.2001.041 · DOI · Paper page
- Bybee, 2005, Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition, Ling. Rev., 22, 381, 10.1515/tlir.2005.22.2-4.381 · DOI
- Gonnerman, 2007, A distributed connectionist approach to morphology: Evidence from graded semantic and phonological effects in lexical priming, J. Exp. Psychol. Gen., 136, 323, 10.1037/0096-3445.136.2.323 · DOI
- Smolensky, P. and Legendre, G. (2006) The harmonic mind: From neural computation to Optimality-theoretic grammar, Vol. 1: Cognitive architecture, Vol. 2: Linguistic and philosophical Implications, MIT Press
- Rumelhart, D.E. et al. (1986) Schemata and sequential thought processes in PDP models. In Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2) (McClelland, J.L. et al., eds), pp. 7-57, MIT Press
- Plaut, 1996, Understanding normal and impaired word reading: Computational principles in quasi-regular domains, Psychol. Rev., 103, 56, 10.1037/0033-295X.103.1.56 · DOI · Paper page
- Plaut, 2000, Are non-semantic morphological effects incompatible with a distributed connectionist approach to lexical processing?, Lang. Cogn. Proc., 15, 445, 10.1080/01690960050119661 · DOI
- Pinker, 2002, The past and future of the past tense, Trends Cogn. Sci., 6, 456, 10.1016/S1364-6613(02)01990-3 · DOI
- Rumelhart, D.E. and McClelland, J.L. (1986) On learning past tenses of English verbs. In Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2) (McClelland, J.L. et al., eds), pp. 216–271, MIT press
- Joanisse, 1999, Impairments in verb morphology following brain injury: a connectionist model, Proc. Natl. Acad. Sci., 96, 7592, 10.1073/pnas.96.13.7592 · DOI
- Seidenberg, 1989, A distributed, developmental model of word recognition and naming, Psychol. Rev., 96, 523, 10.1037/0033-295X.96.4.523 · DOI · Paper page
- Servan-Schreiber, 1991, Graded state machines: The representation of temporal contingencies in simple recurrent networks, Mach. Learn., 7, 161, 10.1007/BF00114843 · DOI
- St. John, 1992, The story gestalt: A model of knowledge-intensive processes in text comprehension, Cogn. Sci., 16, 271, 10.1207/s15516709cog1602_5 · DOI
- Jansen, 2001, Evidence for the phase transition from Rule I to Rule II on the balance scale task, Dev. Rev., 21, 450, 10.1006/drev.2001.0530 · DOI
- Ferretti, 1986, Are childrens’ rule-assessment classifications invariant across instances of problem types?, Child Dev., 57, 1419, 10.2307/1130420 · DOI
- Schapiro, 2009, A connectionist model of a continuous developmental transition in the balance scale task, Cognition, 110, 395, 10.1016/j.cognition.2008.11.017 · DOI
- Farah, 1991, A computational model of semantic memory impairment: Modality-specificity and emergent category-specificity, J. Exp. Psychol. Gen., 120, 339, 10.1037/0096-3445.120.4.339 · DOI
- Schwartz, 1991, The quantitative description of action disorganization after brain damage: A case study, Cogn. Neuropsychol., 8, 381, 10.1080/02643299108253379 · DOI
- Botvinick, 2004, Doing without schema hierarchies: A recurrent connectionist approach to normal and impaired routine sequential action, Psychol. Rev., 111, 395, 10.1037/0033-295X.111.2.395 · DOI
- Botvinick, 2006, Short-term memory for serial order: A recurrent neural network model, Psychol. Rev., 113, 201, 10.1037/0033-295X.113.2.201 · DOI
- Hirsh-Pasek, K. et al. (2000) An emergentist coalition model for word learning: mapping words to objects is a product of the interaction of multiple cues. In Becoming a Word Learner: a Debate on Lexical Acquisition (Hirsh-Pasek, K. and Golinkoff, R.M., eds), pp. 136-164, Oxford Press · DOI
- Mayor, 2010, A neurocomputational account of taxonomic responding and fast mapping in early word learning, Psychol. Rev., 117, 1, 10.1037/a0018130 · DOI
- McClelland, 2002, Rules or connections in past-tense inflections: What does the evidence rule out?, Trends Cogn. Sci., 6, 465, 10.1016/S1364-6613(02)01993-9 · DOI
- Marchman, 1994, Continuity in lexical and morphological development: A test of the critical mass hypothesis, J. Child Lang., 21, 339, 10.1017/S0305000900009302 · DOI
- MacWhinney, 1998, Models of the emergence of language, Ann. Rev. Psychol., 49, 199, 10.1146/annurev.psych.49.1.199 · DOI
Cited by
Sorted by citations
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
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
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Hierarchical process memory: memory as an integral component of information processing
Hasson, U., Chen, J., & Honey, C. J. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.04.006 · Cited by 749
Bayesian just-so stories in psychology and neuroscience.
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436
Grossmann, I. (2017) · Perspectives on Psychological Science · doi:10.1177/1745691616672066 · Cited by 306
The power of possibility: causal learning, counterfactual reasoning, and pretend play
Buchsbaum, D., Bridgers, S., Skolnick Weisberg, D., & Gopnik, A. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0122 · Cited by 200
Indirection and symbol-like processing in the prefrontal cortex and basal ganglia
Kriete, T., Noelle, D. C., Cohen, J. D., & O’Reilly, R. C. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1303547110 · Cited by 181
Self-citationObserving the observer (I): meta-bayesian models of learning and decision-making
Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015554 · Cited by 167
Empiricism without magic: transformational abstraction in deep convolutional neural networks
Buckner, C. (2018) · Synthese · doi:10.1007/s11229-018-01949-1 · Cited by 149
Emergence in Cognitive Science
McClelland, J. L. (2010) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01116.x · Cited by 135
Self-citationToward the neural implementation of structure learning
Tervo, D. G. R., Tenenbaum, J. B., & Gershman, S. J. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.014 · Cited by 106
From simple innate biases to complex visual concepts
Ullman, S., Harari, D., & Dorfman, N. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207690109 · Cited by 77
Meta-learned models of cognition
Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23003266 · Cited by 38
Self-citationIs prediction nothing more than multi-scale pattern completion of the future?
Falandays, J. B., Nguyen, B., & Spivey, M. J. (2021) · Brain Research · doi:10.1016/j.brainres.2021.147578 · Cited by 36
Language and culture internalization for human-like autotelic AI
Colas, C., Karch, T., Moulin-Frier, C., & Oudeyer, P.-Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00591-4 · Cited by 34
Lampinen, A. (2024) · Computational Linguistics · doi:10.1162/coli_a_00525 · Cited by 32
Bayesian cognitive science, predictive brains, and the nativism debate
Colombo, M. (2018) · Synthese · doi:10.1007/s11229-017-1427-7 · Cited by 15
What is the “cognitive” in cognitive neuroscience?
Figdor, C. (2013) · Neuroethics · doi:10.1007/s12152-012-9157-5 · Cited by 10
When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education
Wang, P., Viberg, O., Law, E. L.-C., & Greiff, S. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10202-y · Cited by 3
The Ergonomics of Learning: A Proposed Update to Bloom’s Taxonomy
Curtis, A., & Johansson, A. (2025) · Intersection: A Journal at the Intersection of Assessment and Learning · doi:10.61669/001c.142595
Metadata
- Authors
- McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., & Smith, L. B.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2010-08
- Volume
- 14
- Issue
- 8
- Pages
- 348-356
- ISSN
- 1364-6613
- Cited by
- 305
- References
- 8
- Language
- en
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