Please see the copyright notice at the bottom of this page. I encourage sharing access to these materials for educational and scientific purposes by pointing others to this page or to the urls of specific items listed.

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]

Copyright Notice: The documents accessible through this page have been provided to facilitate dissemination of scholarly and technical work on a noncommercial basis. Copyright is maintained by the authors or other copyright holders, and copyright law requires respect for the terms of each document's copyright.