MACLab

Zhu Jian-Qiao

I am an Assistant Professor at the University of Hong Kong where I direct the Machine-Augmented Cognition Lab. Previously, I was a postdoc in the Computational Cognitive Science Lab at Princeton University, USA, where I worked with Tom Griffiths. I completed my PhD in Psychology at the University of Warwick, UK.

I am interested in understanding the rational and computational foundations of human and machine cognition, with a particular focus on decision-making. I develop Bayesian and neural network models to investigate how people and AI systems form judgments and make decisions.

My lab aims to develop novel theories and tools that can predict, explain, and ultimately shape the behavior of both humans and AI systems.

Google Scholar / X / Email: zhujq@hku.hk

I'm recruiting RAs (remote or in-person), PhD students, and postdocs for 2026 fall intakes! Please reach out via email. Strong candidates are also encouraged to consider the following opportunities:

HKU Presidential PhD Scholar Programme (for PhD)

Hong Kong PhD Fellowship Scheme (for PhD)

PhD Program at SLAI affiliated with my lab (for PhD)

Preprints

Yan, H., Zhu, J. Q., & Sanborn, A. N. (submitted). Large Language Models Can Take False First Steps at Inference-time Planning. arXiv.
Lin, Y., Zhu, J. Q., Spicer, J., Li, Y-X., Chater, N., & Sanborn, A. N. (submitted). Integrating Heuristics and Mental Sampling in Belief Updating. PsyArXiv.
Zhang, X., Chen, Z., Zhu, J. Q., Shen, C., & Liu, Y. (submitted). Towards Digital Humans in the Era of Foundation Models.
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. (submitted). Levels of Analysis for Large Language Models. ArXiv.
Li, Y., Falben, J., Castillo, L., Spicer, J., Zhu, J. Q., Qian, S., Chater, N., & Sanborn, A. N. (submitted). Does Utility Bias Mental Simulations of Risky Events? PsyArXiv.

Technical reports

*Xie, H., & *Zhu, J. Q. (2025). Centaur May Have Learned a Shortcut that Explains Away Psychological Tasks. PsyArXiv.
Zhang, L., McCoy, R. T., Sumers, T., Zhu, J. Q., & Griffiths, T. L. (2023). Deep de Finetti: Recovering Topic Distributions from Large Language Models. arXiv.

Publications

2026

Zhu, J. Q., Yan, H., & Griffiths, T. L. (2026). Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints. Proceedings of the 48th Annual Conference of the Cognitive Science Society.
Liu, Q., Marjieh, R., Zhu, J. Q., Goldberg, A. E., Griffiths, T. L. (2026). Parallelograms Strike Back: LLMs Generate Better Analogies than People. Proceedings of the 48th Annual Conference of the Cognitive Science Society.
Yan, S., Zhu, L., & Zhu, J. Q. (2026). Eliciting Trustworthiness Priors of Large Language Models via Economic Games. Proceedings of the 48th Annual Conference of the Cognitive Science Society.
*Zhu, J. Q., *Yan, H., & Griffiths, T. L. (2026). Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations. Proceedings of the 48th Annual Conference of the Cognitive Science Society.
*Hu, X. & *Zhu, J. Q. (2026). Simulated Annealing Enhances Theory-of-Mind Reasoning in Autoregressive Language Models. Proceedings of the 48th Annual Conference of the Cognitive Science Society.
*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.

2025

Elga, A., Zhu, J. Q., & Griffiths, T. L. (2025). People Make Suboptimal Decisions about Existential Risks. Cognition.
Zhang, L., Li, M. Y., McCoy, R. T., Sumers, T., Zhu, J. Q., & Griffiths, T. L. (2025). What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. Transactions on Machine Learning Research. Featured Certification
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.
- Princeton AI lab blogpost: Machine Learning Helps Reveal What Makes Games Complex for People
- Phys.org coverage
Zhu, J. Q., & Griffiths, T. L. (2025). Computation-Limited Bayesian Updating: A Resource-Rational Analysis of Approximate Bayesian Inference. Psychological Review.
Zhu, J. Q. & Griffiths, T. L. (2025). Eliciting the Priors of Large Language Models using Iterated In-Context Learning. Proceedings of the 47th Annual Conference of the Cognitive Science Society.
Sanborn, A. N., Zhu, J. Q., Spicer, J., Leon-Villagra, P., Castillo, L., Falben, J. K., Li, Y- X, Tee, A., & Chater, N. (2025). Noise in Cognition: Bug or Feature? Perspectives on Psychological Science.
Zhu, J. Q., Yan, H., & Griffiths, T. L. (2025). Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. Proceedings of the 13th International Conference on Learning Representations.

2024

Griffiths, T. L., Zhu, J. Q., Grant, E., & McCoy, R. T. (2024). Bayes in the Age of Intelligent Machines. Current Directions in Psychological Science.
Spicer, J., Zhu, J. Q., Chater, N., & Sanborn, A. N. (2024). How Do People Predict a Random Walk? Lessons for Models of Cognition. Psychological Review.
Zhu, J. Q., Spicer, J., Sanborn, A. N., & Chater, N. (2024). The Statistics of Cognitive Variability: Explaining Common Patterns in Individuals, Groups and Financial Markets. Cognition.
Zhu, J. Q., & Griffiths, T. L. (2024). Incoherent Probability Judgments in Large Language Models. Proceedings of the 46th Annual Conference of the Cognitive Science Society. Oral
Zhu, J. Q., Yan, H., & Griffiths, T. L. (2024). Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo. Proceedings of the 46th Annual Conference of the Cognitive Science Society. Oral
Li, Y-X., Falben, J., Castillo, L., Spicer, J., Zhu, J. Q., Chater, N., & Sanborn, A. N. (2024). Probability, but not utility, influences repeated mental simulations of risky events. Proceedings of the 46th Annual Conference of the Cognitive Science Society.
Zhu, J. Q., Chater, N., Leon-Villagra, P., Spicer, J., Sundh, J., & Sanborn, A. N. (2024). An Introduction to Psychologically Plausible Sampling Schemes for Approximating Bayesian Inference. In K. Fielder, P. Juslin, & J. Denrell (Eds.), Sampling in Judgment and Decision Making, Cambridge University Press, UK.
Sundh, J., Sanborn, A. N., Zhu, J. Q., Spicer, J., Leon-Villagra, P., & Chater, N. (2024). Approximating Bayesian Inference through Internal Sampling. In K. Fielder, P. Juslin, & J. Denrell (Eds.), Sampling in Judgment and Decision Making, Cambridge University Press, UK.

2023

Zhu, J. Q., Sundh, J., Spicer, J., Chater, N., & Sanborn, A. N. (2023). The Autocorrelated Bayesian Sampler: A Rational Process for Probability Judgments, Estimates, Confidence Intervals, Choices, Confidence Judgments, and Response Times. Psychological Review.
Sundh, J., Zhu, J. Q., Chater, N., & Sanborn A. N. (2023). A Unified Explanation of Variability and Bias in Human Probability Judgments: How Computational Noise Explains the Mean-Variance Signature. Journal of Experimental Psychology: General.
Xia. F., Zhu, J. Q., & Griffiths, T. L. (2023). Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms. Proceedings of the 45th Annual Conference of the Cognitive Science Society.
Zhu, J. Q., Sanborn, A. N., Chater, N., & Griffiths, T. L. (2023). Computation-Limited Bayesian Updating. Proceedings of the 45th Annual Conference of the Cognitive Science Society.
Newall, P. W. S. & Zhu, J. Q. (2023). Skilled Poker Players Provide More Accurate Responses than Amateur Poker Players to the Gambling Fallacies Measure. Journal of Gambling Issues.

2022

Zhu, J. Q., Leon-Villagra, P., Chater, N., & Sanborn, A. N. (2022). Understanding the Structure of Cognitive Noise. PLoS Computational Biology.
Spicer, J., Zhu, J. Q., Chater, N., & Sanborn, A. N. (2022). Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion. Psychological Science.
Zhu, J. Q., Newall, P. W. S., Sundh, J., Chater, N., & Sanborn, A. N. (2022). Clarifying the Relationship between Accuracy and Coherence in Probability Judgments. Cognition.

2021

Sanborn, A. N., Zhu, J. Q., Spicer, J., Sundh, J., Leon-Villagra, P., & Chater, N. (2021). Sampling as the human approximation to probabilistic inference. In S. Muggleton & N. Chater, (Eds.), Human-like Machine Intelligence, Oxford University Press, UK.

2020

Chater, N., Zhu, J. Q., Spicer, J., Sundh, J., Leon-Villagra, P., & Sanborn, A. N. (2020). Probabilistic Biases Meet the Bayesian Brain. Current Directions in Psychological Science.
Zhu, J. Q., Sanborn, A. N., & Chater, N. (2020). The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments. Psychological Review.

2019

Sanborn, A. N., Zhu, J. Q., Spicer, J., & Chater, N. (2019). Sampling as a Resource-rational Constraints. Behavioural and Brain Science.
Cabrero, J. A., Zhu, J. Q., & Ludvig, E. A. (2019). Costly Curiosity: People Pay a Price to Resolve an Uncertain Gamble Early. Behavioral Processes.
Zhu, J. Q., Sanborn, A. N., & Chater, N. (2019). Why Decisions Bias Perception: An Amortised Sequential Sampling Account. Proceedings of the 41st Annual Conference of the Cognitive Science Society.

2018 and before

Zhu, J. Q., Sanborn, A. N., & Chater, N. (2018). Mental Sampling in Multimodal Representations. Advances in Neural Information Processing Systems 31.
Zhu, J. Q., Xiang, W., & Ludvig, E. A. (2017). Information Seeking as Chasing Anticipated Prediction Errors. Proceedings of the 39th Annual Conference of the Cognitive Science Society.

Last update: Mar 2026

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