Hang Zhang
Email: hang.zhang(at)pku(dot)edu(dot)cn；
Research Area:
Decision making, computational modeling, computational psychiatry, creative problem solving
The focus of Hang Zhang’s Computation and Decision Lab is to understand how the brain, despite its limited capacity, achieves efficient probabilistic computations for decision-making. We have used behavioral experiments, computational modeling and neuroimaging techniques to study a wide range of decision problems in human cognition, searching for general computational principles underlying different tasks. We also apply the decision-theoretic framework to provide a unifying explanation for classic phenomena in human cognition, and to understand individual differences associated with mental disorders and aging. Our recent research interests also include creative problem solving, one of the few cognitive domains where humans still outperform artificial intelligence.
Much of our past research has centered on uncertainty. Without uncertainty, most real-world decision problems would be trivially simple. Yet uncertainty is pervasive, making probabilistic computation a fundamental function of the brain. How does the brain build probabilistic models of itself and the world to represent these uncertainties, and how does it achieve efficient probabilistic computation under limited cognitive resources? What cognitive principles underlie the seemingly irrational biases that pervade human decision-making?
Our ongoing work extends to more complex learning processes and sequential decision-making tasks, including hypothesis generation, inductive reasoning, abductive reasoning, and creative problem solving. These higher-order cognitive processes are essential for human survival in an open-ended world and lie at the heart of our ability to do science and continually push the frontiers of knowledge. How does the brain generate meaningful hypotheses within a vast space of possibilities? How does it induce general rules from limited and noisy observations, arrive at the most plausible explanations for what it observes, and even break out of existing frameworks to solve novel problems in creative ways? Do these seemingly serendipitous “aha” moments rest on neural computational principles that can likewise be characterized?
We test both human participants and artificial neural networks (e.g., large language models). In silico testing offers a new frame of reference for understanding individual differences in humans, and opens up the possibility of probing the causal mechanisms underlying the emergence of cognitive abilities at a scale beyond the reach of biological experiments.
Selected Publications:
1.Lu Y-L, Lu Y-F, Ren X, Zhang H* (2025) Exploring the bounded rationality in human decision anomalies through an assemblable computational framework. Cognitive Psychology. https://doi.org/10.1016/j.cogpsych.2025.101713
2. Lu Y-L#, Ge Y#, Li M, Liang S, Zhang X, Sui Y, Yang L, Li X, Zhang Y, Yue W, Zhang H*, Yan H* (2025) Cognitive Phenotype Shifts in Risk-Taking: Interplay of Non-Suicidal Self-Injury Behaviors and Intensified Depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 10(5), 504–512. https://doi.org/10.1016/j.bpsc.2024.05.011
3. Wu X#, Ren X#, Liu C*, Zhang H* (2024) The motive cocktail in altruistic behaviors. Nature Computational Science, 4(9), 659-676. https://doi.org/10.1038/s43588-024-00685-6
4. Teng T#, Wenliang LK#*, Zhang H* (2023) Bounded rationality in structured density estimation. Advances in neural information processing systems (NeurIPS). https://openreview.net/pdf?id=VnfeOjR73Q
5. Zhang H*, Ren X, Maloney LT (2020) The bounded rationality of probability distortion. Proceedings of the National Academy of Sciences, 117(36), 22024-22034. https://doi.org/10.1073/pnas.1922401117
6. Zhang H*, Daw ND, Maloney LT (2015) Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions. Nature Neuroscience, 18, 1152-1158. https://doi.org/10.1038/nn.4055
