Computational Cognitive Science Lab

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CI Causal Induction
CD Cognitive Development
CEIL Cultural Evolution and Iterated Learning
DMRL Decision Making and Reinforcement Learning
E Education
F Foundations
IB Inductive Biases
NBM Nonparametric Bayesian Models
P Perception
PR Probabilistic Reasoning
RPM Rational Process Models
S&C Similarity and Categorization
SC Social Cognition
SML Statistical Models of Language

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By McCoy, R.
IB	Bencomo, G., Gupta, M., Marinescu, I., McCoy, R. T., & Griffiths, T. L. (2025). Teasing apart architecture and initial weights as sources of inductive bias in neural networks. (preprint)
IB	Gupta, M., Rane, S., McCoy, R. T., & Griffiths, T. L. (2025). Convolutional neural networks can (meta-) learn the same-different relation. Proceeding of the Annual Meeting of the Cognitive Science Society, 47.(pdf)
F	Ku, A., Campbell, D., Bai, X., Geng, J., Liu, R., Marjieh, R., McCoy, R. T., Nam, A., Sucholutsky, I., Veselovsky, V., Zhang, L., Zhu, J. Q., & Griffiths, T. L. (2025). Using the tools of cognitive science to understand large language models at different levels of analysis. (preprint)
IB S&C	Marinescu, I., McCoy, R. T., & Griffiths, T. L. (2025). Neural networks can capture human concept learning without assuming symbolic representations. (preprint)
IB SML	McCoy, R. T., & Griffiths, T. L. (2025). Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. Nature Communications, 16(1) (pdf)
SML	Yamakoshi, T., Griffiths, T. L., McCoy, R. T., & Hawkins, R. D. (2025). Evaluating distillation methods for data-efficient syntax learning. Findings of the Association for Computational Linguistics: EMNLP 2025. (pdf)
IB SML	Zhang, L., Veselovsky, V., McCoy, R. T., & Griffiths, T. L. (2025). Identifying and mitigating the influence of the prior distribution in large language models. (preprint)
F PR	Griffiths, T. L., Zhu, J. Q., Grant, E., & McCoy, R. T. (2024). Bayes in the age of intelligent machines. Current Directions in Psychological Science, 33(5), 283-291. (pdf)
PR S&C	Marinescu, I. R., Thomas McCoy, R. T., & Griffiths, T. (2024). Distilling symbolic priors for concept learning into neural networks. 46th Annual Meeting of the Cognitive Science Society. (pdf)
F SML	McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024). Embers of autoregression show how large language models are shaped by the problem they are trained to solve. Proceedings of the National Academy of Sciences, 121(41), e2322420121. (pdf)
F SML	McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024). When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. (preprint)
PR SML	Prabhakar, A., Griffiths, T. L., & McCoy, R. T. (2024). Deciphering the factors influencing the efficacy of chain-of-thought: Probability, memorization, and noisy reasoning. Proceedings of the 19th Conference on Empirical Methods in Natural Language Processing. (pdf)
F IB	Griffiths, T. L., Kumar, S., & McCoy, R. T. (2023). On the hazards of relating representations and inductive biases. Behavioral and Brain Sciences, 46, e275. (pdf)
IB SML	McCoy, R. T., Grant, E., Smolensky, P., Griffiths, T. L., & Linzen, T. (2020). Universal linguistic inductive biases via meta-learning. Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pdf)