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Nature Communications|May 20, 2025
Modeling rapid language learning by distilling Bayesian priors into artificial neural networksR Thomas McCoy, Thomas L GriffithsThe Behavioral and Brain Sciences|September 23, 2024
Meta-learning as a bridge between neural networks and symbolic Bayesian modelsR Thomas McCoy, Thomas L GriffithsThe Behavioral and Brain Sciences|September 28, 2023
On the hazards of relating representations and inductive biasesThomas L Griffiths, Sreejan Kumar, R Thomas McCoyProceedings of the National Academy of Sciences of the United States of America|October 4, 2024
Embers of autoregression show how large language models are shaped by the problem they are trained to solveR Thomas McCoy, Shunyu Yao, Dan Friedman, et al.Trends in Cognitive Sciences|April 16, 2026
Whither symbols in the era of advanced neural networks?Thomas L Griffiths, Brenden M Lake, R Thomas McCoy, et al.The Behavioral and Brain Sciences|June 30, 2026
Not-so-strange love: Language models and generative linguistic theories are more compatible than they appearR Thomas McCoyTrends in Cognitive Sciences|October 12, 2020
Understanding Human Intelligence through Human LimitationsThomas L GriffithsCognition|December 16, 2014
Manifesto for a new (computational) cognitive revolutionThomas L GriffithsProceedings. Biological Sciences|March 15, 2021
Human biases limit cumulative innovationBill Thompson, Thomas L GriffithsPageof 21