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2023

Abdool, M., Nam, A. J., & McClelland, J. L. (2023). Continual learning and out of distribution generalization in a systematic reasoning task. In MATH-AI: The 3rd Workshop on Mathematical Reasoning and AI at NeurIPS (Vol. 23). [PDF]

2022

Dasgupta, I., Lampinen, A. K., Chan, S. C., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2022). Language models show human-like content effects on reasoning. arXiv preprint arXiv:2207.07051. [PDF].

Lampinen, A. K., Roy, N., Dasgupta, I., Chan, S. C., Tam, A., Mcclelland, J., Yan, C., Santoro, A., Rabinowitz, N.C., Wang, J. & Hill, F. (2022). Tell me why! Explanations support learning relational and causal structure. International Conference on Machine Learning,, 11868-11890. [PDF].

Lampinen, A. K., Dasgupta, I., Chan, S. C., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., & Hill, F. (2022). Can language models learn from explanations in context?. arXiv preprint arXiv:2204.02329. [PDF].

Li, Y., & McClelland, J. L. (2022). Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks. arXiv preprint arXiv:2210.00400. [PDF].

Li, Y., & McClelland, J. L. (2022). A weighted constraint satisfaction approach to human goal-directed decision making. PLOS Computational Biology, 18(6), e1009553. [PDF].

McClelland, J. L. (2022). Capturing advanced human cognitive abilities with deep neural networks. Trends in Cognitive Sciences, 26(12), 1047-1050. [PDF].

Nam, A. J., Ren, M., Finn, C., & McClelland, J. L. (2022). Learning to Reason With Relational Abstractions. arXiv preprint arXiv:2210.02615. [PDF].

Nam, A. J., Abdool, M., Maxfield, T., & McClelland, J. L. (2022). Out-of-Distribution Generalization in Algorithmic Reasoning Through Curriculum Learning. arXiv preprint arXiv:2210.03275.[PDF].

2021

McClelland, J. L. (2021). Representational redescription: An appreciation of one of Annette Karmiloff-Smith's key contributions to developmental science. In Thomas, M. S. C., Mareschal, D. & Knowland, V. (Eds.), Taking Development Seriously: A Festschrift for Annette Karmiloff-Smith: Neuroconstructivism and the Multi-Disciplinary Approach to Understanding the Emergence of Mind, Chapter 9, 117-120. London: Taylor & Francis. [PDF]

Nam, A. J. & McClelland, J. L. (2021). What underlies rapid learning and systematic generalization in humans. [Arxiv.2107.06994]. [PDF].

Testolin, A., & McClelland, J. L. (2021). Do estimates of Numerosity really adhere to Webers law? A reexamination of two case studies. Psychonomic Bulletin & Review, 28(1), 158-168. doi: 10.3758/s13423-020-01801-z. [PDF]

2020

Hill, F., Lampinen, A., Schneider, R., Clark, S., Botvinick, M., McClelland, J. L., & Santoro, A. (2020). Environmental drivers of systematicity and generalization in a situated agent. In International Conference on Learning Representations, 2020. [PDF]

Lampinen, A., & McClelland, J. L. (2020). Transforming task representations to perform novel tasks. Proceedings of the National Academy of Sciences, 117(52), 32970-32981. DOI: 10.1073/pnas.2008852117. [PDF+SI]

McClelland, J. L. (2020). Exemplar models are useful and deep neural networks overcome their limitations: A commentary on Ambridge (2020). First Language. doi: 10.1177/0142723720905765. [PDF]

McClelland, J. L. & Botvinick, M. (2020). Deep learning: Implications for human learning and memory. To appear in The Oxford Handbook of Human Memory. PsyArXiv preprint. [PDF].

McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schuetze, H. (2020). Placing language in and integrated understanding system: Next steps toward human-level performance in neural language models. Proceedings of the National Academy of Sciences, 117(42), 25966-25974. DOI: 10.1073/pnas.1910416117. [ PDF]

McClelland, J. L., McNaughton, B. L., & Lampinen, A. (2020). Integration of new information in memory: New insights from a complementary learning systems perspective. Philosophical Transactions of the Royal Society B., 375: 20190637. http://dx.doi.org/10.1098/rstb.2019.0637. [PDF] [Supplement PDF]

Rabovsky, M., & McClelland, J. L. (2020). Quasi-compositional mapping from form to meaning: a neural-network-based approach to capturing neural responses during language comprehension. Philosophical Transactions of the Royal Society B. 375(1791), 20190313. doi: rstb.2019.0313. [PDF]

Sabathiel, S., McClelland, J. L. & Solstad, T. (2020). A computational model of learning to count in a multimodal, interactive environment. In S. Denison., M. Mack, Y. Xu, & B.C.Armstrong (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp.1425-1431). Cognitive Science Society. [PDF]

Sabathiel, S., McClelland, J. L. & Solstad, T. (2020). Emerging Representations for Counting in a Neural Network Agent Interacting with a Multimodal Environment. In Bongard, J., Lovato, J., Herbert-Dufresne, L., Dasari, R. & Soros, L. Proceedings of the 2020 Conference on Artificial Life. (pp. 736-743). Cambridge, MA. MIT Press. [PDF]

Singh, A. & McClelland, J. L. (2020). Human-like learning environment for frequency-skewed multi-level classification. In S. Denison., M. Mack, Y. Xu, & B.C. Armstrong (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp 2165-2171). Cognitive Science Society. [PDF]

Suri, G., Gross, J. J., & McClelland, J. L. (2020). Value-based decision making: An interactive activation perspective. Psychological Review, 127(2), 153-185. doi: 10.1037/rev0000164. [PDF] [Supplement PDF]

Testolin, A., Zou, W. Y., & McClelland, J. L. (2020). Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics. Developmental Science, e12940. doi: 10.1111/desc.12940. [PDF]

2019

Di Nuovo, A. & McClelland, J. L. (2019). Developing the knowledge of number digits in a child-like robot. Nature Machine Intelligence, 1,594-605. doi: 10.1038/s42256-019-0123-3. [PDF]

Hill, F., Lampinen, A., Schneider, R., Clark, S., Botvinick, M., McClelland, J. L., & Santoro, A. (2019). Emergent systematic generalization in a situated agent. arXiv:1910.00571.

Lampinen, A. K., & McClelland, J. L. Zero-Shot Task Adaptation by Homoiconic Meta-mapping (HoMM). arXiv:1905.09950.

McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J. & Shuetze, H. (2019). Extending Machine Language Models Toward Human-Level Language Understanding. arXiv:1912.05877.

Rostami, M., Kolouri, S., McClelland, J., & Pilly, P. (2019). Generative Continual Concept Learning. arXiv:1906.03744.

Saxe, A. M., McClelland, J. L., & Ganguli, S. (2019). A mathematical theory of semantic development in deep neural networks. Proceedings of the National Academy of Sciences, USA. 116(23), 11537-11546. pnas.182022611. [PDF] [Supplementary Information PDF]

2018

Chen, S., Zhou, Z., Fang, M., & McClelland, J. L. (2018). Can generic neural networks estimate numerosity like humans? In T.T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society, 202-207. Austin, TX: Cognitive Science Society. [PDF]

Fang, M., Zhou, Z., Chen, S., & McClelland, J. L. (2018). Can a recurrent neural network learn to count things? In T.T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society, 360-365. Austin, TX: Cognitive Science Society. [PDF]

Hoffman, P., McClelland, J. L., & Lambon Ralph, M. A., (2018). Concepts, control, and context: A connectionist account of normal and disordered semantic cognition. Psychological Review. 125(3), 293-328. [PDF]

Kanayet, F. J., Mattarella-Micke, A., Kohler, P. J., Norcia, A. M., McCandliss, B. D., & McClelland, J. L. (2018). Distinct representations of magnitude and spatial position within parietal cortex during number-space mapping. Journal of Cognitive Neuroscience, 30:2, 200-218. [PDF]

Lampinen, A. K. & McClelland, J. L. (2018). Different presentations of a mathematical concept can support learning in complementary ways. Journal of Educational Psychology, 110(5), 664-682. [PDF]

McClelland, J. L. (2018). A computational cognitive neuroscience perspective on word meaning in context. [PDF]

Rabovsky, M. Hansen, S. S., & McClelland, J. L. (2018). Modeling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behaviour, 2, 693-705. [PDF] [Supplementary Materials PDF]

2017

Kueffler, A., Kochenderfer, M. J., & McClelland, J. L., (2017). Geometric concept acquisition in a dueling deep Q-network. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), Procedings of the 39th Annual Conference of the Cognitive Science Society, pp. 2488-2493, Austin, TX: Cognitive Science Society. [PDF]

Lampinen, A., Hsu, S., & McClelland, J. L. (2017). Analogies emerge from learning dynamics in neural networks. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), Procedings of the 39th Annual Conference of the Cognitive Science Society, pp. 2512-2517, Austin, TX: Cognitive Science Society. [PDF]

Mickey, K. & McClelland, J. L. (2017). The unit circle as a grounded conceptual structure in pre-calculus trigonometry. In D. C. Geary, D. B. Berch, R. Ochsendorf and K. Mann Koepke (Eds.), Acquisition of Complex Arithmetic Skills and Higher-Order Mathematics Concepts. Elsevier/Academic Press. [PDF]

Turner, B. M., Gao, J., Koenig, S., Palfy, D., & McClelland, J. L. (2017). The dynamics of multimodal integration: The averaging diffusion model. Psychonomic Bulletin & Review, 24, 1819-1843. https://doi.org/10.3758/s13423-017-1255-2. [PDF]

2016

Kumaran, D., Hassabis, D., & McClelland, J. L. (2016). What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends in Cognitive Sciences, 20, 512-534. DOI: 10.1016/j.tics.2016.05.004. [PDF]

McClelland, J. L. (2016). Capturing gradience, continuous change, and quasi-regularity in sound, word, phrase, and meaning. In B. MacWhinney & W. O'Grady (Eds.), The Handbook of Language Emergence, Chapter 2, pp. 54-80. Hoboken, NJ: John Wiley & Sons. [PDF]

McClelland, J. L., Mickey, K., Hansen, S., Yuan, X., & Lu, Q. (2016). A Parallel-Distributed Processing Approach to Mathematical Cognition. Manuscript, Stanford University, February 18, 2016. [PDF]

McClelland, J. L., Sadeghi, Z. & Saxe, A. M. (2016). A Critique of pure hierarchy: Uncovering cross-cutting structure in a natural dataset. Neurocomputational Models of Cognitive Development and Processing, pp. 51-68. World Scientific. [PDF]

Turner, B. M., Sederberg, P. B. & McClelland, J. L. (2016). Bayesian analysis of simulation-based models. Journal of Mathematical Psychology, 72, 191-199. [PDF]

2015

Joanisse, M. F., & McClelland, J. L. (2015). Connectionist perspectives on language learning, representation, and processing. WIREs Cognitive Science. doi: 10.1002/wcs.1340 [PDF]

McClelland, J. L. (2015). Resilient properties of thought and experience. Language, Cognition and Neuroscience, 30(8), 917-918. [PDF]

McClelland, J.L. & Ralph, M.A.L., 2015. Cognitive Neuroscience. In: J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Vol 4. pp. 95-102. Oxford: Elsevier. [PDF]

Noorbaloochi, S., Sharon, D. & McClelland, J. L. (2015). Payoff information biases a fast guess process in perceptual decision making under deadline pressure: Evidence from behavior, evoked potentials, and quantitative model comparison. Journal of Neurscience, 35(31), 10989-11011. [PDF]

Sadeghi, Z., McClelland, J. L., & Hoffman, P. (2015). You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes. Neuropsychologia, 76, 52-61. [PDF]

2014

Flusberg, S. J. & McClelland, J. L. (2014). Connectionism and the emergence of mind. S. Chipman (Ed.), The Oxford Handbook of Cognitive Science. Forthcoming: published on-line, Nov 2014. [PDF]

Hansen, S. S., McKenzie, C., & McClelland, J. L. (2014). Two plus three is five: Discovering efficient addition strategies without metacognition. Proceedings of the 36th Annual Meeting of the Cognitive Science Society, pp 583-590. Cognitive Science Society: Austin, TX. [PDF].

Mickey, K. W., & McClelland, J. L. (2014). A neural network model of learning mathematical equivalence. Proceedings of the 36th Annual Meeting of the Cognitive Science Society, pp 1012-1017. Cognitive Science Society: Austin, TX. [PDF].

McClelland, J. L. (2014). Learning to discriminate English /r/ and /l/ in adulthood: Behavioral and modeling studies. Studies in Language Sciences: Journal of the Japanese Society for Language Sciences, 13, 32-52. Tokyo: Kaitakusha. [PDF]

McClelland, J. L., Mirman, D., Bolger, D. J., & Khaitan, P. (2014). Interactive activation and mutual constraint satisfaction in perception and cognition. Cognitive Science, 6, pp. 1139-1189. DOI: 10.1111/cogs.12146. [PDF]

Rogers, T. T. & McClelland, J. L. (2014). Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition. Cognitive Science, 6, pp. 1024-1077. DOI: 10.1111/cogs.12148. [PDF].

Note: The article above introduces a special issue of Cognitive Science (Volume 38, Issue 6), reflecting on the Parallel Distributed Processing approach over the 25+ years since the publication of the two PDP volumes. International Conference on Learning Representations. Banff, Canada. [ARXIV]

2013

Criss, A. H., Wheeler, M. E., & McClelland, J. L. (2013). A Differentiation Account of Recognition Memory: Evidence from fMRI. Journal of Cognitive Neuroscience, 25:3, 421-435. [PDF]

Kollias, P. & McClelland, J. L. (2013). Context, cortex, and associations: A connectionist developmental approach to verbal analogies. Frontiers in Psychology, 4, , 857. [PDF] [ DOI: doi: 10.3389/fpsyg.2013.00857]

McClelland, J. L. (2013). Cognitive neuroscience: Emergence of mind from brain. An introduction to the cognitive neuroscience series. In McClelland, J. L. and Lambon Ralph, M. A. (eds), Cognitive Neuroscience: Emergence of mind from brain, The Biomedical & Life Sciences Collection, Henry Stewart Talks Ltd, London. [Link to Talk]

McClelland, J. L. (2013). Integrating probabilistic models of perception and interactive neural networks: A historical and tutorial review. Frontiers in Psychology, 4, 503. [PDF]. [DOI: 10.3389/fpsyg.2013.00503].

McClelland, J. L. (2013). Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory. Journal of Experimental Psychology: General, 142(4), 1190-1210. doi: 10.1037/a0033812. [PDF]

Saxe, A. M., McClelland, J. L., & Ganguli, S. (2013). Learning hierarchical category structure in deep neural networks. In M. Knauff, M. Paulen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th annual meeting of the Cognitive Science Society. (pp. 1271-1276). Austin, TX: Cognitive Science Society. [PDF]

Schapiro, A. C., McClelland, J. L., Welbourne, S. R., Rogers, T. T., & Lambon Ralph, M. A. (2013). Why bilateral damage is worse than unilateral damage to the brain. Journal of Cognitive Neuroscience, 25(12), 2107-2123. doi:10.1162/jocn_a_00441. [PDF]

2012

Bogacz,R., Usher, M., Zhang, J. & McClelland, J. L. (2012). Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice. In A. K. Seth, T. J. Prescott and J. J. Bryson (Eds.), Modelling Natural Action Selection. pp 91-119. Cambridge, UK: Cambridge University Press. [PDF]

Ingvalson, E. M., Holt, L. L., & McClelland, J. L. (2012). Can native Japanese listeners learn to differentiate /r-l/ on the basis of F3 onset frequency? Bilingualism: Language and Cognition, 15(2), 255-274. [PDF]

Kumaran, D. & McClelland, J. L. (2012). Generalization through the recurrent interaction of episodic memories: A model of the hippocampal system. Psychological Review, 119, 573-616. [PDF]

Maia, T. V. & McClelland, J. L. (2012). A neurocomputational approach to obsessive-compulsive disorder (Spotlight Review Article). Trends in Cognitive Sciences, 16,, 14-15. [PDF]

McClelland, J. L. (2012). R. Duncan Luce (1925-2012) (Retrospective). Science, 337, 1619. [PDF]. DOI: 10.1126/science.1229851.

Sternberg, D. A. & McClelland, J. L. (2012). Two mechanisms of human contingency learning. Psychological Science, 23(1), 59-68. [PDF] [DOI].

Tsotsos, K., Gao, G., McClelland, J. L., & Usher, M. (2012). Using time-verying evidence to test models of decision dynamics: Bounded diffusion vs. the leaky competing accumulator model. Frontiers in Neuroscience, 6, 79, [PDF] [DOI].

2011

Gao, J., Tortell, R., & McClelland, J. L. (2011). Dynamic integration of reward and stimulus information in perceptual decision making. PLoS ONE 6(3): e16749. [PDF with Supplementary Information] [DOI].

Henderson, C. M. & McClelland, J. L. (2011). A PDP model of the simultaneous perception of multiple objects. Connection Science, 23, 161-172. [PDF] [DOI]

Ingvalson, E. M., McClelland, J. L., & Holt, L. L. (2011). Predicting native English-like performance by native Japanese speakers. Journal of Phonetics, 39, 571--584. doi:10.1016/j.wocn.2011.03.003. [PDF]

McClelland, J. L. (2011). Memory as a constructive process: The parallel-distributed processing approach. In S. Nalbantian, P. Matthews, and J. L. McClelland (Eds.), The Memory Process: Neuroscientific and Humanistic Perspectives. Cambridge, MA: MIT Press, pp. 129-151. [PDF]

Rogers, T. T. & McClelland, J. L. (2011). Semantics without categorization. In E. M. Pothos & A. J. Wills (Eds.), Formal approaches to categorization. Chapter 5. Cambridge, UK: Cambridge University Press, pp. 88-119. [PDF]

Tsotsos, K., Usher, M., & McClelland, J. L. (2011). Testing multi-alternative decision models with non-stationary evidence. Frontiers in Neuroscience, 5, 63, [PDF] [DOI.]

2010

Dilkina, K., McClelland, J. L. & Plaut, D. C. (2010). Are there mental lexicons? The role of semantics in lexical decision. Brain Research, 1365, 66-81. [PDF] [DOI]

McClelland, J. L. (2010). Emergence in cognitive science. Topics in Cognitive Science, 2, 751-770. [PDF] [DOI]

McClelland, J. L. (2010). Memory and its neural basis. In McClelland, J. L. and Lambon Ralph, M. A. (Eds), Cognitive Neuroscience: Emergence of mind from brain, The Biomedical & Life Sciences Collection, Henry Stewart Talks Ltd , London. [Link to Talk]

McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T.T., Seidenberg, M. S., & Smith, L. B. (2010). Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Understanding Cognition. Trends in Cognitive Sciences, 14, 348-356. [PDF] [DOI]

Plaut, D. C., & McClelland, J. L. (2010). Locating object knowledge in the brain: A critique of Bowers' (2009) attempt to revive the grandmother cell hypothesis. Psychological Review, 117, 284-288. [PDF] [DOI]

Rorie, A. E., Gao, J., McClelland, J. L., and Newsome, W. T. (2010). Integration of sensory and reward information during perceptual decision making in lateral intraparietal Cortex (LIP) of the macque monkey. PLoS ONE 5(2), e9308. [PDF] | [DOI]

2009

Lake, B. M., Vallabha, G. K. & McClelland, J. L. (2009). Modeling unsupervised perceptual category learning. IEEE Transactions on Autonomous Mental Development, 1, 35-43. [PDF]

McClelland, J. L. (2009). Is a machine realization of truly human-like intelligence achievable? Cognitive Computation, 1(1), 17-21. [PDF] | [DOI]

McClelland, J. L. (2009). Phonology and perception: A cognitive scientist's perspective. In Boersma, P. & Hamann, S. (Eds.). Phonology in perception. Berlin: Mouton De Gruyter. [PDF]

McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11-38. [PDF] | [DOI]

McClelland, J. L., & Cleeremans, A. (2009). Connectionist models. In T. Byrne, A. Cleeremans, & P. Wilken (Eds.), Oxford Companion to Consciousness. pp 177-181. New York: Oxford University Press. [PDF]

McClelland, J. L., Rogers, T. T., Patterson, K., Dilkina, K. N., & Lambon Ralph, M. R. (2009). Semantic Cognition: Its Nature, Its Development, and its Neural Basis. In M. Gazzaniga (Ed.), The Cognitive Neurosciences IV. Boston, MA: MIT Press. Chapter 72. [PDF]

McClelland, J. L. & Vallabha, G. (2009). Connectionist models of development: Mechanistic dynamical models with emergent dynamical properties. In J.P. Spencer, M. S. C. Thomas, & J. L. McClelland, (Eds). Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. 3-24. New York: Oxford. [PDF]

Schapiro, A. C. & McClelland, J. L. (2009). A connectionist model of a continuous developmental transition in the balance scale task. Cognition, 110(1), 395-411. [PDF] | [DOI]

Spencer, J. P., Thomas, M. S. C., & McClelland, J. L. (2009). Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. New York: Oxford. [Book Cover PDF]

Sternberg, D., & McClelland, J. L. (2009). When Should We Expect Indirect Effects in Human Contingency Learning? When Should We Expect Indirect Effects in Human Contingency Learning? In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 206-211). Austin, TX: Cognitive Science Society. [PDF]

Thibodeau, P., McClelland, J. L., & Boroditsky, L. (2009). When a bad metaphor may not be a victimless crime: The role of metaphor in social policy. In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 809-814). Austin, TX: Cognitive Science Society. [PDF]

Thomas, M. S. C., McClelland, J. L., Richardson, F. M., Schapiro, A. C. & Baughman, F. (2009). Dynamical and Connectionist Approaches to Development: Toward a Future of Mutually Beneficial Co-evolution. In J.P. Spencer, M. S. C. Thomas, & J. L. McClelland, (Eds). Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. pp 337-353. New York: Oxford. [PDF]

2008

Dilkina, K., McClelland, J. L. & Plaut, D. C. (2008). A single-system account of semantic and lexical deficits in five semantic demntia patients. Cognitive Neuropsychology, 25(2), 136-164. [PDF] | [DOI]

Mirman, D., McClelland, J. L., Holt, L. L., & Magnuson, J. S. (2008). Effects of attention on the strength of lexical influences on Speech Perception: Behavioral Experiments and Computational Mechanisms. Cognitive Science, 32, 398-417. [PDF] | [DOI]

Rogers, T. T. & McClelland, J. L. (2008). Precis of Semantic Cognition, a Parallel Distributed Processing Approach. Behavioral and Brain Sciences, 31, 689-749. Includes Open Peer Commentary and Response to Commentaries. [PDF] | [DOI]

Thomas, M. S. C. & McClelland, J. L. (2008). Connectionist models of cognition. In R. Sun (Ed). Cambridge handbook of computational psychology. Cambridge University Press. 23-58. [PDF]

Usher, M., Elhalal, A. & McClelland, J. L. (2008). The neurodynamics of choice, value-based decisions, and preference reversal. In Chater, N. & Oaksford, M. The Probabilistic Mind. New York: Oxford University Press. 278-300. [PDF]

2007

Bogacz,R., Usher, M., Zhang, J. & McClelland, J. L. (2007). Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice. Theme issue on Modeling Natural Action Selection. Philosophical Transactions of the Royal Society: B. Biological Sciences. 362. 1655-1670.[PDF]

Dilkina, K., McClelland, J. L. & Borodetsky, L. (2007). How language affects thought in a connectionist model. Proceedings of 29th Annual Meeting of the Cognitive Science Society. [PDF]

Lupyan, G., Rakison, D. H. & McClelland, J. L. (2007). Language is not just for talking: redundant labels facilitate learning of novel categories. Psychological Science, 18(12), 1077-1083. [PDF]

McClelland, J. L. & Bybee, J. (2007). Gradience of Gradience: A reply to Jackendoff. The Linguistic Review, 24, 437-455. [PDF]

McClelland, J. L. & Thompson, R. M. (2007). Using Domain-General Principles to Explain Children's Causal Reasoning Abilities. Developmental Science, 10(3), 333-356. [PDF]

Mechelli, A., Josephs, O., Lambon Ralph, M. A., McClelland, J. L., & Price, C. J. (2007). Dissociating stimulus-driven semantic and phonological effects during reading and naming. Human Brain Mapping, 28, 205-217. [PDF]

Vallabha, G. K. & McClelland, J. L. (2007). Success and failure of new speech category learning in adulthood: Consequences of learned Hebbian attractors in topographic maps. Cognitive, Affective and Behavioral Neuroscience, 7, 53-73.[PDF]

Vallabha, G. K., McClelland, J. L., Pons, F., Werker, J. & Amano, S. (2007). Unsupervised learning of vowel categories from infant-directed speech. Proceedings of the National Academy of Science, 104, 13273-13278. [PDF] [Supplementary Info. PDF]

2006

Criss, A. & McClelland, J. L. (2006). Differentiating the differentiation models: A comparison of the retrieving effectively from memory model (REM) and the subjective likelihood model (SLiM). Journal of Memory and Language, 55, 447-460. [PDF]

McClelland, J. L. (2006). How far can you go with Hebbian learning, and when does it lead you astray? In Munakata, Y. & Johnson, M. H. Processes of Change in Brain and Cognitive Development: Attention and Performance XXI. pp. 33-69. Oxford: Oxford University Press. [PDF]

McClelland, J. L. Mirman, D., & Holt, L. L. (2006). Are there interactive processes in speech perception? Trends in Cognitive Sciences, 10(8), pp. 363-369. [PDF] Reply by McQueen, Norris & Cutler [PDF] Response to reply by Mirman, McClelland & Holt [PDF]

McClelland, J. L. & Vander Wyk, Brent. (2006). Graded constraints in English word forms. Working manuscript, Department of Psychology, Carnegie Mellon University. [PDF]

Mirman, D., McClelland, J. L. & Holt, L. L. (2006). An interactive Hebbian account of lexically guided tuning of speech perception. Psychonomic Bulletin and Review, 13(6), 958-965. [PDF]

Moldakarimov, S. B., McClelland, J. L., & Ermentrout, G. B. (2006). A homeostatic rule for inhibitory synapses promotes temporal sharpening and cortical reorganization. Proceedings of the National Academy of Sciences, 103(44),16526-31. [PDF] [Supplementary Materials PDF]

Tricomi, E., Delgado, M. R., McCandliss, B. D., McClelland, J. L. & Fiez, J. A. (2006). Performance feedback drives caudate activation in a phonological learning task. Journal of Cognitive Neuroscience, 18, 1029-1043. [PDF]

2005

Bybee, J. & McClelland, J. L. (2005). Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition. The Linguistic Review, 22(2-4), 381-410. [PDF]

Lambon Ralph, M. A., Braber, N., McClelland, J. L. & Patterson, K. (2005). What underlies the neuropsychological pattern of irregular > regular past-tense verb production? Brain and Language. [PDF    Related article by Braber, Ellis, Lambon Ralph & Patterson [PDF]

Mechelli, A., Crinion, J. T., Long, S., Friston, K. J., Lambon Ralph, M. A., Patterson, K., McClelland, J. L., & Price, C. J. (2005). Dissociating reading processes on the basis of neuronal interactions. Journal of Cognitive Neuroscience, 17(11), 1753-1765. [PDF]

Mirman, D., McClelland, J. L. & Holt, L. L. (2005). Computational and behavioral investigations of lexically induced delays in phoneme recognition. Journal of Memory and Language, 52, 424-443. [PDF]

Rogers, T. T., & McClelland, J. L. (2005). A parallel distributed processing approach to semantic cognition: Applications to conceptual development. In L. Gershkoff-Stowe & D. Rakison (Eds), Building Object Categories in Developmental Time.. [PDF]

2004

Maia, T. V. & McClelland, J. L. (2004). A re-examination of the evidence for the somatic marker hypothesis: What participants know in the Iowa gambling task. Proceedings of the National Academy of Sciences, 101, 16075-16080. [PDF] Supplementary Materials [PDF] PNAS Commentary by Sanfey & Cohen [PDF] TiCS Research Focus Commentary by Bechara et al [PDF] Response to Bechara et al by Maia & McClelland [PDF]

Mirman, D., Holt, L. L. & McClelland, J. L. (2004). Categorization and discrimination of non-speech sounds: Differences between steady-state and rapidly-changing acoustic cues. Journal of the Acoustical Society of America, 116, 1198-1207. [PDF]

Rogers, T. T., Lambon Ralph, M. A., Garrard, P., Bozeat, S., McClelland, J. L., Hodges, J. R., & Patterson, K. (2004). The structure and deterioration of semantic memory: A neuropsychological and computational investigation. Psychological Review, 111, 205-235. [PDF]

Rogers, T. T. & McClelland, J. L. (2004).  Semantic Cognition:  A Parallel Distributed Processing Approach.  Cam­bridge, MA:  MIT Press. Available from Amazon.com. [Final Prepublication Manuscript PDF]. See also the BBS Multiple Book Review of Semantic Cognition (Includes Precis, Commentary and Author's Response) [PDF].

Rogers, T. T., Rakison, D. & McClelland, J. L. (2004). U-shaped curves in development: A PDP approach. Contribution to a special issue on U-shaped changes in behavior and their implications for cognitive development. Journal of Cognition and Development, 5, 137-145. [PDF]

Usher, M. & McCelland, J. L. (2004). Loss aversion and inhibition in dynamical models of multi-alternative choice. Psychological Review, 111, 757-769. [PDF]

2003

Bird, H., Lambon Ralph, M. A., Seidenberg, M.S., McClelland, J.L., & Patterson, K. (2003). Deficits in phonology and past-tense morphology: What's the connection? Journal of Memory and Language, 48, 502-526. [PDF]

Lupyan, G. & McClelland, J. L. (2003). Did, made, had, said: Capturing quasi-regularity in exceptions. Proceedings of the Annual Meeting of the Cognitive Science Society, 2003. [PDF]

McClelland, J. L. & Patterson, K. (2003).  Differentiation and integration in human language:  A reply to Marslen-Wilson and Tyler.  Trends in Cognitive Sciences, 7, 63-64. [PDF]

McClelland, J. L., Plaut, D. C., Gotts, S. J. & Maia, T. V. (2003). Developing a domain-general framework for cognition: What is the best approach? Commentary on a target article by Anderson & Lebiere. Behavioral and Brain Sciences, 22,, 611-614. [HTML.] [Target Article PDF with Commentaries.]

McClelland, J. L. & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4, 310-322. [PDF]

Munakata, Y. & McClelland, J. L. (2003). Connectionist models of development. Contribution to a special issue on Dynamical Systems and Connectionist Models. Developmental Science, 6:4, 413-429. [PDF]

2002

Davidson, R. J., Lewis, D. A., Alloy, L. B., Amaral, D. G., Bush, G., Cohen, J. D., Drevets, W. C., Farah, M. J., Kagan, J., McClelland, J. L., Nolen-Hoeksema, S., Peterson, B. S. (2002). Neural and behavioral substrates of mood and mood regulation. Biological Psychiatry, 52, 478-502.

McCandliss, B. D., Fiez, J. A., Protopapas, A., Conway, M., & McClelland, J. L. (2002). Success and failure in teaching the [r]-[l] contrast to Japanese adults: Predictions of a Hebbian model of plasticity and stabilization in spoken language perception. Cognitive, Affective and Behavioral Neuroscience. 2:2. 89-108. [PDF] [ Related Newspaper Story.]

McClelland, J. L., Fiez, J.A. & McCandliss, B. D. (2002). Teaching the /r/-/l/ discrimination to Japanese adults: behavioral and neural aspects. Physiology & Behavior, 77, 657-62. [PDF]

McClelland, J. L. & Lupyan, G. (2002). Double dissociations never license simple inferences about underlying brain organization, especially in developmental cases. Commentary on a target article by Thomas & Karmiloff-Smith. Behavioral & Brain Sciences, 25, 763-764. [HTML.] [Target Article PDF with Commentaries.]

McClelland, J. L. & Patterson, K. (2002). Rules or Connections in Past-Tense inflections: What does the evidence rule out? Trends in Cognitive Sciences. With McClelland, J. L. & Patterson, K. (2002). `Words Or Rules' cannot exploit the regularity in exceptions (Reply to Pinker & Ullman). [Preprint in PDF]

McClelland, J. L., Patterson, K., Pinker, S. & Ullman, M. (2002). The Past Tense Debate: Papers and replies by S. Pinker & M. Ullman and by J. McClelland & K. Patterson.Trends in Cognitive Sciences, 6,456-474. [ PDF] Follow-up comment by Marslen-Wilson and Tyler [ PDF] and response by McClelland and Patterson [ PDF].

Usher, M., Olami, Z., & McClelland, J. L. (2002). Hick's law in a stoachastic race model with speed- accuracy tradeoff. Mathematical Psychology, 46, 704-715. [PDF]

2001

Lambon Ralph, M. A., McClelland, J. L., Patterson, K., Galton, C. J., & Hodges, J. R. (2001). No right to speak? The relationship between object naming and semantic impairment: Neuropsychological evidence and a computational model. Cognitive Neuroscience. 13:3, 341-356. [PDF]

McClelland, J. L. (2001). Cognitive Neuroscience. In N. J. Smelser & Paul B. Baltes (Eds.), International Encyclopedia of the Social & Behavioral Sciences. Oxford: Pergamon, 2133-2139. [PDF]

McClelland, J. L. (2001). Failures to learn and their remediation: A Hebbian account. In J. L. McClelland & R. S. Siegler (Eds.) Mechanisms of Cognitive Development: Behavioral and Neural Approaches. Mahwah, NJ: Lawrence Erlbaum Associates, 97-211. [PDF]

McClelland, J. L. & Siegler, R. S. (Eds.), (2001). Mechanisms of cognitive development: Behavioral and neural perspec­tives.Mahwah, NJ: Erlbaum.

Movellan, J.R., & McClelland, J. L. (2001). The Morton-Massaro Law of Information Integration: Implications for Models of Perception. Psychological Review, 108, 113-148. [PDF]

Patterson, K., Lambon Ralph, M. A., Hodges, J. R., & McClelland, J. L. (2001). Deficits in irregular past-tense verb morphology associated with degraded semantic knowledge. Neuropsychologia, 39, 709-724. [PDF]

Usher, M., & McClelland, J. L. (2001). On the time course of perceptual choice: The leaky competing accumulator model. Psychological Review, 108, 550-592. [PDF]

Weng, J., McClelland, J. L., Pentland, A., Sporns, O., Stockman, I., Sur, M., Thelen, E. (2001).  Autonomous mental development by robots and animals.  Science, 291, 599-600. [PDF]

2000

Lambon Ralph, M. A., McClelland, J. L., Patterson, K., & Hodges, J. R. (2000).  The relationship between semantic memory and speech production:  Neuropsychology, neuroanatomy, and neural-net model.  Higher Brain Function Research (Shitsugosho Kenkyu), 20, 145-156.

McClelland, J. L. (2000). Connectionist models of memory. In E. Tulving & F. I. M. Craik (Eds.), The Oxford Handbook of Memory, 583-596.   New York, NY:  Oxford University Press. [PDF]

McClelland, J. L. (2000). The basis of hyperspecificity in autism: a preliminary suggestion based on properties of neural nets. Journal of Autism and Developmental Disorders, 30, 497-2002. [PDF]

McClelland, J. L. & Seidenberg, M. S. (2000). Why do kids say goed and brang? Science, 287, 47-48. [PDF]

Movellan, J. R. & McClelland, J. L. (2000). Information factorization in connectionist models of perception. In S. A. Solla, T. K. Leen, & K. R. Muller (Eds.), Advances in neural information processing systems,  45-51. Cam­bridge, MA: MIT Press. [PDF]

Plaut, D. C., & McClelland, J. L. (2000). Stipulating versus discovering representations Behavioral & Brain Sciences, 23. [PDF]

Stark, C. E. L. & McClelland, J. L. (2000). Repetition priming of words, pseudowords, and nonwords. Jour­nal of Experimental Psychology: Learning, Memory and Cognition, 26, 945-972. [PDF]

1999

Hasselmo, M. E. & McClelland, J. L. (1999). Neural models of memory. Current Opinion in Neurobiology, 9, 184-188.

McClelland, J. L. (1999). Cognitive modeling, connectionist. In R. A. Wilson & F. Keil (Eds)., The MIT Ency­clopedia of the Cognitive Sciences. Cambridge, MA: MIT Press, 137-139.

McClelland, J. L., & Plaut, D. C. (1999). Does generalization in infant learning implicate abstract algebra-like rules? [PDF]

McClelland, J., Thomas, A., McCandliss, B., & Fiez, J. (1999). Understanding Failures of Learning: Hebbian Learning, Competition for Representational Space, and Some Preliminary Experimental Data. In J. Reggia, E. Ruppin & D. Glanzman (Eds.), Progress in Brain Research. Volume 121. Disorders of Brain, Behavior and Cognition: The Neurocomputational Perspective, Amsterdam: Elsevier, 75-80. [PDF]

1998

Cohen, J. D., Usher, M., & McClelland, J. L. (1998). A PDP approach to set size effects within the stroop task. Psychological Review, 105, 188-194. [PDF]

McClelland, J. L. (1998). Complementary learning systems in the brain: A connectionist approach to explicit and implicit cognition and memory. In R. M. Bilder & F. F. LeFever (Eds.) Neuroscience of the Mind on the Centennial of Freud’s Project for a Scientific Psychology. Annals of the New York Academy of Sciences, 843, 153-169. [PDF]

McClelland, J. L. (1998). Connectionist models and Bayesian inference. In M. Oaksford & N. Chater (Eds.), Rational Models of Cognition. Oxford: Oxford University Press. 21-53. [PDF]

McClelland, J. L. (1998). Role of the hippocampus in learning and memory: A computational analysis. In. K. H. Pribram (Ed.) Brain and Values: Is a Biological Science of Values Possible. Mahwah, NJ: Erlbaum, 535-547. [PDF]

McClelland, J. L. & Chappell, M. (1998). Familiarity Breeds Differentiation: A Subjective-Likelihood Appoach to the Effects of Experience in Recognition Memory. Psychological Review, 105, 724-760. [PDF]

O’Reilly, R. C., Norman, K., & McClelland, J. L. (1998).  A hippocampal model of recognition memory.  In M. I. Jordan, M. J. Kearns, & S. A. Solla, (Eds.), Neural Information Processing Systems, 10, 73-79.  Cambridge, MA:  MIT Press. [PDF]

1997

McClelland, J. L. (1997). The neural basis of consciousness and explicit memory: Reflections on Kihlstrom, Mandler, & Rumelhart. In J. D. Cohen & J. W. Schooler (Eds.) Scientific approaches to consciousness. Mahwah, NJ: Erlbaum, 499-509. [PDF]

Munakata, Y., McClelland, J. L., Johnson, M. H. & Siegler, R. S. (1997). Rethinking Infant Knowledge: Toward an Adaptive Process Account of Successes and Failures in Object Permanence Tasks. Psychological Review, 104, 686-713. [PDF]

1996

Inui, T. & McClelland, J. L. (Eds.) (1996). Attention & Performance XVI: Information Integration in Perception and Communication. Cambridge, MA: MIT Press.

McClelland, J. L. (1996). Integration of information: Reflections on the theme of Attention and Performance XVI. In T. Inui & J. L. McClelland (Eds.), Attention & Performance XVI: Information Integration in Perception and Communication. Cambridge, MA: MIT Press, 633-656. [PDF]

McClelland, J. L. (1996). Neural mechanisms for the control and monitoring of memory: A parallel distrib­uted processing perspective. In L. Reder, Implicit memory and metacognition. Mahwah, NJ: Erlbaum, 275-286. [PDF]

McClelland, J. L. (1996). Role of the hippocampus in learning and memory: A computational analysis. In T. Ono, B. L. McNaughton, S. Molitchnikoff, E. T. Rolls & H. Nichijo (Eds.), Perception Memory, and Emotion: Frontier in Neuroscience. Oxford: Elsevier Science, Ltd. 601-613. [PDF]

McClelland, J. L. & Goddard, N. (1996). Considerations arising from a complementary learning systems perspective on hippocampus and neocortex. Hippocampus, 6, 654-665. [PDF]

Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56-115. [PDF]

1995

McClelland, J. L. (1995). A connectionist approach to knowledge and development. In T. J. Simon & G. S. Halford (Eds.), Developing cognitive competence: New approaches to process modeling. pp. 157-204. Mahwah, NJ: LEA, 157-204. [PDF]

McClelland, J. L. (1995). Constructive memory and memory distortions: A parallel-distributed processing approach. In D. L. Schacter, (Ed.), Memory Distortion. Cambridge, MA: Harvard, 69-90. [PDF]

McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457. [PDF] An interview with the authors of this highly cited paper is available [here].

McClelland, J. L. & Plunkett, K. (1995). Cognitive development. In Michael A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press, pp 193-197. [PDF]

Plaut, D. C., McClelland, J. L., & Seidenberg, M. S. (1995). Reading exception words and pseudowords: Are two routes really necessary? In J. P. Levy, D. Bairaktaris, J. Bullinaria, & P. Cairns (Eds.), Connectionist models of memory and language (pp. 145-159). London: UCL Press. [PDF]

Usher, M. & McClelland, J. L. (1995). On the time course of perceptual choice: A model based on principles of neural computation. Technical Report PDP.CNS.95.5, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA. [PDF]

1994

McClelland, J. L. (1994). The interaction of nature and nurture in development: A parallel distributed processing perspective. In P. Bertelson, P. Eelen, & G. d'Ydewalle (Eds.), International Perspectives on Psychological Science, Volume 1: Leading Themes. United Kingdom: Erlbaum. [PDF]

McClelland, J. L. (1994). Learning the general but not the specific. Current Biology, 4, 357-358. [PDF]

McClelland, J. L. (1994). Comment: Neural networks and cognitive science: Motivations and applications. (Cheng, B. & Titterington, D. M. Neural Networks: A review from a statistical perspective.) Statistical Science, 9, 1, 2-54. [PDF]

McClelland, J. L. (1994). The organization of memory: A Parallel Distributed Processing perspective. Revue Neurologique (Paris), 150, 8-9, 570-579. [PDF]

Movellan, J. R. & McClelland, J. L. (1994). Contrastive learning with graded random networks. In T. Petsche & M. Kearns (Eds.), Computational Learning Theory and Natural Learning Systems, Vol. 2. MIT Press: Cambridge, MA.

O'Reilly, R. C., & McClelland, J. L. (1994). Hippocampal conjunctive encoding, storage and recall: Avoiding a tradeoff. Hippocampus. [Journal Article PDF] [Preprint PDF]

Seidenberg, M. S., Plaut, D. C., Petersen, A. S., McClelland, J. L., & McRae, K. (1994). Nonword pronunciation and models of word recognition. Journal of Experimental Psychology: Human Perception & Performance, 20, 1177-1196. [PDF]

Stark, C. E. & McClelland, J. L. (1994). Tractable learning of probability distributions using the contrastive Hebbian algorithm. In Proceedings of the 16th Annual Meeting of the Cognitive Science Society. Hillsdale, NJ: Erlbaum, 818-823. [PDF].

1993

Hoeffner, J. H., & McClelland, J. L. (1993). Can a perceptual processing deficit explain the impairment of inflectional morphology in developmental dysphasia? A computational investigation. Proceedings of the 25th Annual Child Language Research Forum. [PDF; Related paper by Hoeffner [PDF]

McClelland, J. L. (1993). Toward a theory of information processing in graded, random, interactive networks. In D. E. Meyer & S. Kornblum (Eds.), Attention & Performance XIV: Synergies in experimental psychology, artificial intelligence and cognitive neuroscience, pp. 655-688. Cambridge, MA: MIT Press. [PDF]

McClelland, J. L., & Plaut, D. C. (1993). Computational approaches to cognition: Top-down approaches. [PDF]

Movellan, J. R. & McClelland, J. L. (1993). Learning continuous probability distributions with symmetric diffusion networks. Cognitive Science, 17, 463-496. [PDF]

Plaut, D. C., & McClelland, J. L. (1993). Generalization with componential attractors: Word and nonword reading in an attractor network Proceedings of the 15th Annual Conference of the Cognitive Science Society, 824-829. [PDF]

1992

Cohen, J. D., Servan-Schreiber, D., & McClelland, J. L. (1992). A parallel distributed processing approach to automaticity. American Journal of Psychology, 105, 239-269. [PDF]

Farah, M. J. McClelland, J. L. (1992). Neural network models and cognitive neuropsychology. Psychiatric Annals, 22, 148-153. [PDF]

McClelland, J. L. (1992). Can connectionist models discover the structure of natural language? In Morelli, R., Brown, W. M., Anselmi, D., Haberlandt, K., Lloyd, D. (Eds.) Minds, Brains & Computers, pp. 168-189. Ablex Publishing: Norwood, NJ. [PDF]

Nystrom, L. E. & McClelland, J. L. (1992). Trace synthesis in cued recall. Journal of Memory and Language, 31, 591-614. [PDF]

Servan-Schreiber, D., Cohen, J. D., & McClelland, J. L. (1992). A parallel distributed model of the mecha­nisms of processing in the Eriksen response-competition task: Relation to event-related potential studies. Psychophysiology (Suppl). 29, 6.

1991

Cleeremans, A. & McClelland, J. L. (1991). Learning the structure of event sequences. Journal of Experimental Psychology: General, 120,, 235-253. [PDF]

Farah, M. J., & McClelland, J. L. (1991). A computational model of semantic memory impairment: Modality- specificity and emergent category-specificity. Journal of Experimental Psychology: General, 120, 339-357. [PDF]

McClelland, J. L. (1991). Stochastic interactive processes and the effect of context on perception. Cognitive Psychology, 23, 1-44. [PDF]

McClelland, J. L., & Jenkins, E. (1991). Nature, nurture, and connections: Implications of connectionist models for cognitive development. In K. Van Lehn (Ed.), Architectures for Intelligence, pp. 41-73. Hillsdale, NJ: Erlbaum. [PDF]

Servan-Schreiber, D., Cleeremans, A. & McClelland, J. L. (1991). Graded state machines: The representation of temporal contingencies in simple recurrent networks. Machine Learning, 7, 161-193. [PDF]

1990

Butters, N., Grant, I., Haxby, J., Judd, L. L., Martin, A., McClelland, J., Pequegnat, W., Schacter, D., & Stover, E. (1990). Assessment of aids-related cognitive changes: Recommendations of the NIMH workshop on neuropsychological assessment approaches. Journal of Clinical and Experimental Neuropsychology, 12, 963-978. [PDF]

Cohen, J. D., Dunbar, K. & McClelland, J. L. (1990). On the Control of Automatic Processes: A Parallel-Distributed Processing Account of the Stroop Effect. Psychological Review, 97,, 332-361. [PDF]

McClelland, J. L., Cleeremans, A., & Servan-Schreiber, D. (1990). Parallel distributed processing: Bridging the gap between human and machine intelligence. Journal of the Japanese Society for Artificial Intelligence, 5, 2-14. [PDF]

St. John, M. F., & McClelland, J. L. (1990). Learning and applying contextual constraints in sentence comprehension. Artificial Intelligence, 46, 217-257. [PDF]

Seidenberg, M. S. & McClelland, J. L. (1990). More words but still no lexicon. Reply to Besner et al. (1990). Psychological Review, 97, 447-452. [PDF]

Taraban, R. & McClelland, J. L. (1990). Parsing and Comprehension. A multiple constraint view. In Rayner, K., Balota, M., & Flores D'Arcais, I. Comprehension processes in reading. Hillsdale, NJ: Erlbaum. [PDF]

1989

Cleeremans, A., Servan-Schreiber, D., & McClelland, J. L. (1989). Finite state automata and simple recurrent networks. Neural Computation, 1 (3), 372-381. [PDF]

McClelland, J. L. (1989). Parallel distributed processing and role assignment constraints. In Y. Wilks (Ed.), Theoretical issues in natural language processing (pp. 78-85). Hillsdale, NJ: Lawrence Erlbaum Associates.

McClelland, J. L. (1989). Parallel distributed processing: Implications for cognition and development. In Morris, R. (Ed)., Parallel distributed processing: Implications for psychology and neurobiology. (pp. 8-45). New York: Oxford University Press. [PDF]

McClelland, J. L., St. John. M., & Taraban, R. (1989). Sentence comprehension: A parallel distributed processing approach. Language and Cognitive Processes, 4, SI 287-335. [PDF]

Patterson, K., Seidenberg, M. S., & McClelland, J. L. (1989). Connections and disconnections: Acquired dys­lexia in a computational model of reading processes. In Morris, R. (Ed.), Parallel distributed processing: Implica­tions for psychology and neurobiology. New York: Oxford University Press. [PDF]

Seidenberg, M. S. & McClelland, J. L. (1989). A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review, 96, 523-568. [PDF]

Seidenberg, M. S. & McClelland, J. L. (1989). Visual word recognition and pronunciation: A computational model of acquisition, skilled performance, and dyslexia. In Galaburda, A. (Ed). From Neurons to Reading (pp. 255-305). Cambridge, MA: MIT Press. [PDF]

Servan-Schreiber, D., Cleeremans, A., & McClelland, J. L. (1989). Encoding sequential structure in simple recurrent networks. In D.Touretzky, (Ed.), Advances in neural information processing systems I. New York: Mor­gan Kaufman, 643-652. [PDF]

Ward, R. & McClelland, J. L. (1989). Conjunctive search for one and two identical targets. Journal of Experi­mental Psychology: Human Perception and Performance, 15, 664-672. [PDF]

1988

Elman, J. L., & McClelland, J. L. (1988). Cognitive penetration of the mechanisms of perception: Compensation for coarticulation of lexically restored phonemes. Journal of Memory and Language, 27, 143-165. [PDF]

Hinton, G. E., & McClelland, J. L. (1988). Learning representations by recirculation. In D. Z. Anderson, (Ed.), Neural information processing systems (pp. 358-366). New York: American Institute of Physics. [PDF]

McClelland, J. L. (1988). Connectionist models and psychological evidence. Journal of Memory and Language, 27, 107-123. [PDF]

McClelland, J. L. & Rumelhart, D. E. (1988). A simulation-based tutorial system for exploring parallel dis­tributed processing. Behavior Research Methods, Instruments & Computers, 2, 263-275.

McClelland, J. L. & Rumelhart, D. E. (1988). Explorations in parallel distributed processing: A handbook of models, programs, and exercises. Boston, MA: MIT Press. Macintosh edition, 1990. [Archive.]

Taraban, R. & McClelland, J. L. (1988). Constituent attachment and thematic role assignment in sentence processing: Influences of content-based expectations. Journal of Memory & Language, 27, 597-632. [PDF]

1987

McClelland, J. L. (1987). The case for interactionism in language processing.  In M. Coltheart (Ed.), Attention & performance XII: The psychology of reading (pp. 1-36). London: Erlbaum. [PDF]

Rumelhart, D. E. & McClelland, J. L. (1987). Learning the past tenses of english verbs: Implicit rules or par­allel distributed processing. In B. MacWhinney (Ed.), Mechanisms of Language Acquisition (pp. 194-248). Mah­wah, NJ: Erlbaum.

Taraban, R. & McClelland, J. L. (1987). Conspiracy effects in word pronunciation. Journal of Memory and Language, 26, 608-631. [PDF]

1986

McClelland, J. L., Feldman, J., Adelson, B., Bower, G., & McDermott, D. (1986). Connectionist models and cognitive science: Goals, directions, and implications. Report to the National Science Foundation.

Elman, J. L. & McClelland, J. L. (1986). An architecture for parallel processing in speech recognition: The TRACE model. In M. R. Schroeder (Ed.), Speech recognition. Basel: S. Krager AG.

Elman, J. L. & McClelland, J. L. (1986). Exploiting the lawful variability in the speech wave. In J. S. Perkell & D. H. Klatt (Eds.), Invariance and variability of speech processes. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. [PDF]

McClelland, J. L. & Elman, J. L. (1986). The TRACE Model of Speech Perception. Cognitive Psychology, 18, 1-86. [PDF]

McClelland, J. L. & Mozer, M. C. (1986). Perceptual interactions in two-word displays: Familiarity and simi­larity effects. Journal of Experimental Psychology: Human Perception and Performance, 12, 18-35. [PDF]

Rumelhart, D. E., McClelland, J. L., & the PDP research group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volume I. Cambridge, MA: MIT Press. [Archive.]

McClelland, J. L., Rumelhart, D. E., & the PDP research group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volume II. Cambridge, MA: MIT Press. [Archive.]

Chapters in above volumes:

• McClelland, J. L., Rumelhart, D. E., & Hinton, G. E. The Appeal of Parallel Distributed Processing. Vol I, Ch 1. [PDF]
• Rumelhart, D. E., Hinton, G. E., & McClelland, J. L. A framework for Parallel Distributed Processing. Vol I, Ch 2. [PDF]
• Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. Distributed Representations. Vol I, Ch 3. [PDF]
• Rumelhart, D. E. & McClelland, J. L. Discussion and Preview. Vol I, Ch 4.
• McClelland, J. L. Resource requirements of fixed and programmable nets.Vol I, Ch 12. [PDF]
• Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. Parallel Distributed Processing Models of Schemata and Sequential Thought Processes. Vol II, Ch 14. [PDF]
• McClelland, J. L. & Elman, J. E. Interactive processes in speech perception: The TRACE model. Vol II, Ch 15. [Adapted version of McClelland & Elman (1986) Cognitive Science, 18, 1-86.]
• McClelland, J. L. The Programmable blackboard model of reading.Vol II, Ch 16. [PDF]
• McClelland, J. L. & Rumelhart, D. E. A distributed model of memory. Vol II, Ch 17. [Adapted from McClelland & Rumelhart (1985), Journal of Experimental Psychology: General, 114, 159-188.]
• Rumelhart, D. E. & McClelland, J. L. On learning the past tenses of English verbs. Vol II, Ch 18. [PDF]
• McClelland, J. L. & Kawamoto, A. H. Mechanisms of sentence processing: Assigning roles to constituents of sentences. Vol II, Ch 19. [PDF]
• McClelland, J. L. & Rumelhart, D. E. Distributed memory and amnesia. Vol II, Ch 25. [PDF]

1980-1985

McClelland, J. L. (1985). Distributed models of cognitive processes. In D. Olton, E. Gamzu, & S. Corkin (Eds.), Memory Dysfunctions: An integration of animal and human research. New York: New York Academy of Sci­ences. [PDF]

McClelland, J. L. (1985). Putting knowledge in its place: A scheme for programming parallel processing structures on the fly. Cognitive Science, 9, 113-146. [PDF]

McClelland, J. L. & Rumelhart, D. E. (1985). Distributed memory and the representation of general and specific information. Journal of Experimental Psychology: General, 114, 159-197. [PDF]

Rumelhart, D. E. & McClelland, J. L. (1985). Levels indeed! A response to Broadbent. Journal of Experimental Psychology: General, 114, 193-197. [PDF]

Elman, J. L. & McClelland, J. L. (1984). Speech perception as a cognitive process: The interactive activation model. In Norman Lass (Ed.), Speech and Language, Vol. 10. New York: Academic Press. [PDF]

Elman, J. L. & McClelland, J. L. (1983). Speech perception as a cognitive process: The interactive activation model. ICS Report No. 8302, Institute for Cognitive Science, University of California, San Diego, La Jolla, CA 92093. [PDF]

Rumelhart, D. E. & McClelland, J. L. (1982). An interactive activation model of context effects in letter perception: Part 2. The context enhancement effect and some tests and extensions of the model. Psychological Review, 89, 60-94. [PDF]

Goodman, G. O., McClelland, J. L. & Gibbs, R. W. (1981). The role of syntactic context in visual word recognition. Memory and Cognition, 9, 580-586. [PDF]

Jackson, M. D. & McClelland, J. L. (1981). Exploring the nature of a basic visual processing component of reading ability. In O. Tzeng & H. Singer (Eds.), Perception of print: Reading research in experimental psychology. Hillsdale, NJ: Erlbaum. [PDF]

McClelland, J. L. & O'Regan, J. K. (1981). Expectations increase the benefit derived from parafoveal visual information in reading words aloud. Journal of Experimental Psychology: Human Perception and Performance, 7, 634- 644. [PDF]

McClelland, J. L. & O'Regan, J. K. (1981). On visual and contextual factors in reading: A reply to Rayner & Slowiaczek. Journal of Experimental Psychology: Human Perception and Performance, 7, 652-657. [PDF]

McClelland, J. L. (1981). Retrieving general and specific information from stored knowledge of specifics. Proceedings of the Third Annual Conference of the Cognitive Science Society 170-172. [PDF]

McClelland, J. L. & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88, 375-407. [PDF]

Rumelhart, D. E. & J. L. McClelland. (1981). Interactive processing through spreading activation. In C. Per­fetti & A. Lesgold (Eds.), Interactive processes in reading. Hillsdale NJ: Erlbaum. [PDF]

Johnston, J. C.and McClelland, J. L. (1980). Experimental tests of a hierarchical model of word identification. Journal of Verbal Learning and Verbal Behavior, 19, 503-524. [PDF]

Larochelle,S., McClelland, J. L., & Rodriguez, E. (1980). Context and the allocation of resources in word recognition. Journal of Experimental Psychology: Human Perception and Performance, 6, 686-694. [PDF]

1973-1979

Jackson, M. D. & McClelland, J. L. (1979). Processing determinants of reading speed. Journal of Experimental Psychology: General, 108, 151-181. [PDF]

McClelland, J. L. (1979). On the time relations of mental processes: An examination of systems of processes in cascade. Psychological Review, 86, 287-330. [PDF]

McClelland, J. L. & Miller, J. O. (1979). Structural factors in figure perception. Perception and Psychophysics, 26, 221-229. [PDF] McClelland, J. L. (1978). Cognitive psychology: The way we were. Contemporary Psychology, 23, 860-861.

McClelland, J. L. (1978). Perception and masking of wholes and parts. Journal of Experimental Psychology: Human Perception and Performance, 4, 210-223. [PDF]

McClelland, J. L. (1978). The phenomenology of perception. Science, 201, 899-900. [PDF]

McClelland, J. L. & Jackson, M. D. (1978). Studying individual differences in reading. In A. M. Lesgold, J.W. Pellegrino, S.Fokkema, & R. Glaser (Eds.), Cognitive psychology and instruction. New York: Plenum.

McClelland, J. L. (1977). Letter and configuration information in word identification. Journal of Verbal Learning and Verbal Behavior, 16, 137-150. [PDF]

McClelland, J. L. & Johnston, J. C. (1977). The role of familiar units in perception of words and nonwords. Perception and Psychophysics, 22, 249-261. [PDF]

McClelland, J. L. (1976). Preliminary letter identification in the perception of words and nonwords. Journal of Experimental Psychology: Human Perception and Performance, 2, 80-91. [PDF]

Jackson, M. D. & McClelland, J. L. (1975). Sensory and cognitive determinants of reading speed. Journal of Verbal Learning and Verbal Behavior, 14, 565-574. [PDF]

Johnston, J. C. & McClelland, J. L. (1974). Perception of letters in words: Seek not and ye shall find. Science, 184, 1192-1194. [PDF]

Johnston, J. C. & McClelland, J. L. (1973). Visual factors in word perception. Perception and Psychophysics, 14, 365-370. [PDF]

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