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James L. McClelland

Stanford University, Palo Alto, CA 
Cognitive Science and Cognitive Neuroscience
"James McClelland"
Mean distance: 12.49 (cluster 23)
Cross-listing: LinguisTree


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Paul Rozin grad student 1975 Penn


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Eric S. De Vos research assistant 1976-1976 UCSD
Devika Narain research assistant 2007-2007 Stanford
Ritwik K. Niyogi research assistant 2007-2007 Stanford
Anna C. Schapiro research assistant 2006-2009 Princeton
Qihong Lu research assistant 2015-2016 Stanford
Zhenglong Zhou research assistant 2017-2019
Katia Dilkina grad student Stanford
Jeremy Glick grad student Stanford
Yuko Munakata grad student Carnegie Mellon
Leigh Erik Nystrom grad student Carnegie Mellon
Randall C. O'Reilly grad student Carnegie Mellon
Andrew Saxe grad student
Roman Taraban grad student (PsychTree)
Adam G Thomas grad student University of Pittsburgh
Jonathan D. Cohen grad student 1990 Carnegie Mellon
Axel Cleeremans grad student 1988-1991 Carnegie Mellon
Craig E. L. Stark grad student 1992-1997 Carnegie Mellon
Timothy T. Rogers grad student 2000 Carnegie Mellon
Daniel Mirman grad student 2000-2005 Carnegie Mellon
Gary Lupyan grad student 2003-2007 Carnegie Mellon
Erin M. Ingvalson grad student 2002-2008 Carnegie Mellon / Univ. of Pittsburgh
Daniel A. Sternberg grad student 2006-2011 Stanford
Pavlos Kollias grad student 2010-2012 Stanford
Amy H. Criss post-doc Carnegie Mellon
Juan Joanne Gao post-doc
Alan Kawamoto post-doc Carnegie Mellon
Samat Moldakarimov post-doc Carnegie Mellon / Univ. of Pittsburgh
David C. Plaut post-doc Carnegie Mellon
David A. Medler post-doc 1998-2001
Michael A. Erickson post-doc 1999-2002 Carnegie Mellon
Dahlia Sharon research scientist Stanford


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Mark 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
BETA: Related 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
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