Research Interest:
Ru-Yuan Zhang’s group is currently focusing on:
1. Computational Visual Neuroscience
Human vision accounts for over 80% of sensory input to the brain. The neural and computational mechanisms underlying visual perception have long been a central topic in cognitive neuroscience. My research questions in this area include: (1) the computation and representation of uncertainty in perceptual decision-making; (2) the influence of top-down modulation (e.g., attention and learning) on population codes in the human brain; and (3) the neural implementation of Bayesian inference.
2. Deep Learning and Applications in Neuroscience
In recent years, there has been a surge in research comparing artificial neural networks with the human brain. We envision a promising future in which machine learning and cognitive science mutually inform and advance one another, fostering the development of general intelligence. My research in this direction includes: (1) neural alignment in vision–language models; and (2) the neural mechanisms underlying perceptual and cognitive learning.
3. Cognitive Learning and Decision-Making
The biological brain, as the most powerful intelligent agent, acquires knowledge through thought, experience, and sensory input. Decision-making, in contrast, is a cognitive process that involves choosing among alternatives to achieve desired outcomes. Cognitive learning and decision-making are fundamental to understanding how humans think, learn, and behave. Our research aims to uncover how individuals perceive the world, learn to solve problems, and make decisions. Current research in this area includes: (1) mechanisms of continual learning in humans and machines; (2) structure and parameter learning; and (3) complex problem-solving in dynamic environments.
4. Computational Psychiatry
Computational psychiatry is an interdisciplinary field that bridges basic computational neuroscience and translational psychiatry. Research in this field emphasizes the use of computational models developed in healthy populations or basic neuroscience to characterize the mechanisms underlying abnormal cognitive behavior in psychiatric disorders. I am currently working on the following topics: (1) computational mechanisms of visual working memory deficits in psychiatric disorders; (2) abnormal reinforcement learning in psychiatric disorders; and (3) LLM-based simulation of psychiatric diseases.
Selected Publications：
1. Yang, L, Xie, X., Zheng, W., Fang, F.*, Zhang, R.Y.*. Neural prediction errors as a unified cue of abstract visual reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), doi: 10.1109/TPAMI.2025.3623461.
2. Cheng, Y.A., Sanayei, M., Chen, X., Jia, K., Li, S., Fang, F., Watanabe, T., Thiele, A., Zhang, R.Y.* (2025). A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning. Nature Human Behaviour, 9, 1023-1040.
3. Pan, W., Geng, H., Zhang, L., Fengler, A., Frank, M.J., Zhang, R.Y.*, Hu, C.P.*. (2025). dockerHDDM: A user-friendly environment for Bayesian Hierarchical Drift-Diffusion Modeling. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/25152459241298700.
4. Teng, X.*, Zhang, R.Y.*. (2025). Sequential temporal anticipation characterized by neural power modulation and in recurrent neural networks. eLife, 13:RP99383.
5. Fang, Z., Zhao, M., Xu, T., Li, Y., Xie, H., Quan, P., Geng, H., Zhang, R.Y.*. (2024) Individuals with anxiety and depression use atypical decision strategies in an uncertain world. eLife, 13:RP93887.
6. Cheng, Z.J.#, Yang, L.#, Zhang, W.H., Zhang, R.Y.*. (2023). Representational geometries reveal differential effects of response correlations on population codes in neurophysiology and functional magnetic resonance imaging. Journal of Neuroscience, 43(24), 4498-4512.
7. Yang, L., You, H., Zhen, Z., Wang, D-H., Wan, X., Xie, X., Zhang, R.Y.*. (2023). Neural prediction errors enable abstract analogical reasoning in human standard intelligence tests. In International Conference on Machine Learning (ICML). PMLR.
8. Xu, Y.#, Yang, L.#, You, H., Zhen, Z., Wang, D-H., Wan, X., Xie, X., Zhang, R.Y.*. (2023). RuleMatch: matching abstract rules for semi-supervised learning of human standard intelligence tests. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI).
9. Geng, H., Chen, J.*, Hu, C.P., Jin, J, Raymond C. K. Chan, Li, Y., Hu, X., Zhang, R.Y.*, Zhang, L. (2022). Promoting computational psychiatry in China. Nature Human Behaviour, 6, 615-617.
10. Bejjanki, V. R.#, Zhang, R. Y.#, Li, R., Pouget, A., Green, C. S., Lu, Z. L., & Bavelier, D. (2014). Action video game facilitates development of better perceptual templates. Proceedings of the National Academy of Sciences, 111(47), 16961-16966.
11. Zhang, R. Y.#, Kwon, O. S.#, & Tadin, D. (2013). Illusory Movement of stationary stimuli in the visual periphery: evidence for a strong centrifugal prior in motion processing. Journal of Neuroscience, 33(10), 4415-4423.
Lab Website：
https://ruyuanzhang.github.io/