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 Zhu, J.
SML	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. (2026). Levels of analysis for large language models. Philosophical Transactions of the Royal Society A, 384(2320). (pdf)
S&C SML	Liu, Q. E., Marjieh, R., Zhu, J. Q., Goldberg, A. E., & Griffiths, T. L. (2026). Large language models provide support for the parallelogram theory of analogy. (preprint)
PR	Zhu, J. Q., & Griffiths, T. L. (2026). Computation-limited bayesian updating: A resource-rational analysis of approximate bayesian inference. Psychological Review, 133(3), 619-635. (pdf)
DMRL SML	Zhu, J.Q., Xie, H., Arumugam, D., Wilson, R. C., & Griffiths, T. L. (2026). Using reinforcement learning to train large language models to explain human decisions. Proceedings of the 14th International Conference on Learning Representations (ICLR). (pdf)
PR SML	Zhu, J. Q., Yan, H., & Griffiths, T. L. (2026). Recovering event probabilities from large language model embeddings via axiomatic constraints. Proceedings of the Annual Meeting of the Cognitive Science Society, 48. (pdf)
DMRL	Elga, A., Zhu, J. Q., & Griffiths, T. L. (2025). People make suboptimal decision about existential risks. Cognition, 265, 106216. (pdf)
DMRL	Zhu, J. Q., Peterson, J. C., Enke, B., & Griffiths, T. L. (2025). Capturing the complexity of human strategic decision-making with machine learning. Nature Human Behaviour. (pdf)
SML	Zhu, J. Q., Yan, H., & Griffths, T. L. (2025). Steering risk preferences in large language models by aligning behavioral and neural representations. (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 SML	Zhu, J. Q., & Griffiths, T. L. (2024). Incoherent probability judgments in large language models. 46th Annual Meeting of the Cognitive Science Society. (pdf)
PR SML	Zhu, J. Q., & Griffiths, T. L. (2024). Eliciting the priors of large language models using iterated in-context learning. (preprint)
IB S&C	Zhu, J. Q., Yan, H., & Griffiths, T. (2024). Recovering mental representations from large language models with Markov chain Monte Carlo. 46th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SML	Zhu, J. Q., Yan, H., & Griffiths, T. L. (2024). Language models trained to do arithmetic predict human risky and intertemporal choice. (preprint)
DMRL	Xia, F., Zhu, J., & Griffiths, T. (2023). Comparing human predictions from expert advice to on-line optimization algorithms. 45th Annual Meeting of the Cognitive Science Society. (pdf)
RPM	Zhu, J. Q., Sanborn, A., Chater, N., & Griffiths, T. (2023). Computation-limited Bayesian updating. 45th Annual Meeting of the Cognitive Science Society. (pdf)