Information Seeking as Chasing Anticipated Prediction Errors. - Jian-Qiao Zhu; Wendi Xiang; Elliot A. Ludvig (CogSci, 2017)

Mental Sampling in Multimodal Representations. - Jian-Qiao Zhu; Adam Sanborn; Nick Chater (NeurIPS, 2018)

Why Decisions Bias Perception: An Amortised Sequential Sampling Account. - Jian-Qiao Zhu; Adam Sanborn; Nick Chater (CogSci, 2019)

Bayesian Inference Causes Incoherence in Human Probability Judgments. - Jian-Qiao Zhu; Adam Sanborn; Nicholas Chater (CogSci, 2019)

How many instances come to mind when making probability estimates? - Joakim Sundh; Jian-Qiao Zhu; Nicholas Chater; Adam Sanborn (CogSci, 2020)

Sampling as the Human Approximation to Probabilistic Inference. - Adam Sanborn; Jian-Qiao Zhu; Jake Spicer; Joakim Sundh; Pablo León-Villagrá; Nick Chater (Human-Like Machine Intelligence, 2022)

Understanding the structure of cognitive noise. - Jian-Qiao Zhu; Pablo León-Villagrá; Nick Chater; Adam N. Sanborn (PLoS Comput. Biol., 2022)

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms. - Feng Xia; Jian-Qiao Zhu; Tom Griffiths 001 (CogSci, 2023)

Computation-Limited Bayesian Updating. - Jian-Qiao Zhu; Adam Sanborn; Nick Chater; Tom Griffiths 001 (CogSci, 2023)

Bayes in the age of intelligent machines. - Thomas L. Griffiths 001; Jian-Qiao Zhu; Erin Grant; R. Thomas McCoy (CoRR, 2023)

Deep de Finetti: Recovering Topic Distributions from Large Language Models. - Liyi Zhang; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (CoRR, 2023)

Incoherent Probability Judgments in Large Language Models. - Jian-Qiao Zhu; Thomas L. Griffiths 001 (CoRR, 2024)

Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (CoRR, 2024)

Eliciting the Priors of Large Language Models using Iterated In-Context Learning. - Jian-Qiao Zhu; Thomas L. Griffiths 001 (CoRR, 2024)

Capturing the Complexity of Human Strategic Decision-Making with Machine Learning. - Jian-Qiao Zhu; Joshua C. Peterson; Benjamin Enke; Thomas L. Griffiths 001 (CoRR, 2024)

Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (ICLR, 2025)

What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. - Liyi Zhang; Michael Y. Li; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (Trans. Mach. Learn. Res., 2025)

Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis. - Alexander Ku; Declan Campbell; Xuechunzi Bai; Jiayi Geng; Ryan Liu 001; Raja Marjieh; R. Thomas McCoy; Andrew Nam; Ilia Sucholutsky; Veniamin Veselovsky; Liyi Zhang; Jian-Qiao Zhu; et al. (CoRR, 2025)

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (CoRR, 2025)

Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions. - Jian-Qiao Zhu; Hanbo Xie; Dilip Arumugam; Robert C. Wilson; Thomas L. Griffiths 001 (CoRR, 2025)

Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations. - Jian-Qiao Zhu; Haijiang Yan; Thomas L. Griffiths 001 (CoRR, 2025)