James L. McClelland
Affiliations: | Stanford University, Palo Alto, CA |
Area:
Cognitive Science and Cognitive NeuroscienceWebsite:
http://psychology.stanford.edu/~jlm/Google:
"James McClelland"Mean distance: 12.49 (cluster 23) | S | N | B | C | P |
Cross-listing: LinguisTree
Children
Sign in to add traineeCollaborators
Sign in to add collaboratorMark S. Seidenberg | collaborator | UW Madison | ||
David E. Rumelhart | collaborator | 1976-1984 | UCSD | |
(JLM learned mathematical modeling from DER then they developed PDP models together) | ||||
Kevin N. Dunbar | collaborator | 1985-1988 | University of Toronto | |
Konstantinos Tsetsos | collaborator | 2009-2012 |
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Publications
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McClelland JL, Hill F, Rudolph M, et al. (2020) Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models. Proceedings of the National Academy of Sciences of the United States of America |
McClelland JL, McNaughton BL, Lampinen AK. (2020) Integration of new information in memory: new insights from a complementary learning systems perspective. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 375: 20190637 |
Henderson CM, McClelland JL. (2020) Intrusions into the shadow of attention: A new take on illusory conjunctions. Attention, Perception & Psychophysics |
Testolin A, Zou Y, McClelland JL. (2020) Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics. Developmental Science |
Rabovsky M, McClelland JL. (2020) Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 375: 20190313 |
McClelland JL. (2020) Exemplar models are useful and deep neural networks overcome their limitations: A commentary on Ambridge (2020) First Language. 40: 612-615 |
Suri G, Gross JJ, McClelland JL. (2019) Value-based decision making: An interactive activation perspective. Psychological Review |
Saxe AM, McClelland JL, Ganguli S. (2019) A mathematical theory of semantic development in deep neural networks. Proceedings of the National Academy of Sciences of the United States of America |
Di Nuovo A, McClelland JL. (2019) Developing the knowledge of number digits in a child-like robot Nature Machine Intelligence. 1: 594-605 |
Rabovsky M, Hansen SS, McClelland JL. (2018) Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behaviour. 2: 693-705 |