Principal Investigator

Hang Zhang
perception and action, decision-making, computational modeling



Research Interests:

In Hang Zhang’s Computation and Decision Lab, we combine behavioral experiment, computational modeling and neuroimaging techniques to study a variety of decision problems in human perception and cognition, bridging psychology, cognitive neuroscience, statistical decision theory and economic decision-making. What concern us are the general computational principles behind human judgment and decision-making in different areas.


Many of our studies have one common keyword: uncertainty. In a world without uncertainty, most decision problems would have been trivial. In contrast, uncertainty is everywhere in the real world, which makes probabilistic computation an essential function of the brain. We ask: How does the brain, with its limited cognitive capacity, achieve efficient probabilistic computation? How does the brain model the uncertainty in its own perceptual and cognitive systems as well as the uncertainty in the environment? What are the prior beliefs or biases implicit in the brain’s probabilistic models? Any inspiration for artificial intelligence?



Selected Publications:

  • For a full list of publications, see here.

Ren, X, Luo H*, Zhang H* (2021) Automatic and fast encoding of representational uncertainty underlies the distortion of relative frequency. Journal of Neuroscience. [full text]

Zhang H*, Ren X, Maloney LT (2020) The bounded rationality of probability distortion. Proceedings of the National Academy of Sciences. 117(36), 22024-22034. [full text]
Wang M, Huang Y, Luo H, Zhang H* (2020) Sustained visual priming effects can emerge from attentional oscillation and temporal expectation. Journal of Neuroscience. 40(18):3657–3674. [full text]

Lu H, Yi L*, Zhang H* (2019) Autistic traits influence the strategic diversity of information sampling: insights from two-stage decision models. PLoS Computational Biology, 15(12): e1006964. [full text]
Sun J, Li J*, Zhang H* (2019) Human representation of multimodal distributions as clusters of samples. PLoS Computational Biology, 15(5): e1007047. [full text]

Zhang H*, Daw ND, Maloney LT (2015) Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions. Nature Neuroscience, 18, 1152-1158. [full text]

Zhang H*, Maloney LT. Ubiquitous log odds: a common representation of probability and frequency distortion in perception, action, and cognition. 2012, Frontiers in Neuroscience, 6(1). [full text]



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