Yujia Peng is currently a principal investigator at the School of Psychology and Cognitive Sciences at Peking University. She received her bachelor’s degree at Peking University and her Ph.D. at the University of California, Los Angeles (UCLA). She did her post-doctoral research at UCLA with Michelle Craske and Hakwan Lau. Yujia Peng joined Peking University in September 2021.

The Depression and Anxiety Computational Neuroscience (DACN) lab is affiliated with the Department of Psychological and Cognitive Sciences, Peking University, Beijing. We closely collaborate with the Institute for Artificial Intelligence, Peking University, and the Beijing Institute for General Artificial Intelligence (BIGAI). DACN lab overall focuses on the multi-dimensional mechanistic investigations of mood and anxiety disorders, with a special focus on computational psychiatry and neuroimaging. We use a combination of behavioral experiments, fMRI, EEG, MEG, computational modeling, and machine learning, to promote our understanding of mechanisms underlying mental disorders.

Cognition, emotion, and social processes closely intertwine, and dysregulated functioning of corresponding neural networks associates with mental disorders such as depression and anxiety. My research examines the order, disorder, and interconnections of cognitive, emotional, and social processing throughout the lifespan from childhood to older adulthood in healthy and diseased brains, to develop improved diagnostic and prognostic tests to be used in community mental health settings. My current works aim to (1) understand negative cognitive bias in social anxiety, (2) decode individual differences in emotion and social perception based on multi-dimensional data, and (3) develop personalized neurofeedback treatments combined with classic psychological interventions to anxiety through computational neuroimaging methods.


List of Publications

(* Equal contribution, # Corresponding author)

Peng, Y., Han, J., Zhang, Z., Fan, L., Liu, T., Qi, S., ... & Zhu, S. C. (2023). The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social Interactions. Engineering. https://doi.org/10.1016/j.eng.2023.07.006

Peng, Y.#, Wang, Y., & Lu, D., (2023). The mechanism of emotion processing and intention inference in social anxiety disorder based on biological motion. Advances in Psychological Science, 31(6), 905-914. https://doi.org/10.3724/SP.J.1042.2023.00905.

Peng, Y. *, Knotts, J. D. *, Young, K. S., Bookheimer, S. Y., Nusslock, R., Zinbarg, R. E., ... & Craske, M. G.# (2023). Threat neurocircuitry predicts the development of anxiety and depression symptoms in a longitudinal study. Biological psychiatry: cognitive neuroscience and neuroimaging. 8(1): 102-110. https://doi.org/10.1016/j.bpsc.2021.12.013

Peng, Y., Gong, X., Lu, H., & Fang, F. (2023). Mapping between the Human Visual System and Two-stream DCNNs in Action Representation. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45). Austin, TX: Cognitive Science Society.

Jiang, G., Xu, M., Xin, S., Liang, W., Peng, Y., Zhang, C., & Zhu, Y. (2023). MEWL: Few-shot multimodal word learning with referential uncertainty. Proceedings of the 40th International Conference on Machine Learning (PMLR) 202:15144-15169.

Peng, Y.*, Knotts, J.D.*#, Taylor, C.T., Craske, M.G., Stein, M.B., Bookheimer, S., Young, K.S., Simmons, A.N., Yeh, H., Ruiz, J., Paulus, P.M. (2021). Failure to identify robust latent variables of positive or negative valence processing across units of analysis. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 6(5), 518-526.

Shu, T.#, Peng, Y., Zhu, S., & Lu, H. (2021). A unified psychological space for human perception of physical and social events. Cognitive Psychology. 128. 101398.

Peng, Y. #, Lu, H., & Johnson, S. P. (2021). Infant perception of causal motion produced by humans and inanimate objects. Infant Behavior and Development, 64, 101615.

Peng, Y. #, Lee, H., Shu, T., & Lu, H. (2020). Exploring biological motion perception in two-stream convolutional neural networks. Vision Research, 178, 28-40.

Chiang J.N. #, Peng, Y., Lu, H., Holyoak, K.J., & Monti, M.M. (2020). Distributed code for semantic relations predicts neural activity during analogical reasoning. Journal of Cognitive Neuroscience, 1-13.

Peng, Y. #, Ichien, N., & Lu, H. (2020). Causal actions enhance perception of continuous body movements. Cognition, 194, 104060,

Ogren, M.#, Kaplan, B., Peng, Y., Johnson, K. L., & Johnson, S. P. (2019). Motion or emotion: Infants discriminate emotional biological motion based on low-level visual information. Infant Behavior and Development, 57, 101324.

Tsang, T., Ogren, M., Peng, Y., Nguyen, B., Johnson, K.L. & Johnson S.P. # (2018). Infant perception of sex differences in biological motion displays. Journal of Experimental Child Psychology, 173, 338–350.

Keane, B. P.*, Peng, Y.*, Demmin, D., Silverstein, S. M., & Lu, L. (2018). Intact perception of coherent motion, dynamic rigid form, and biological motion in chronic schizophrenia. Psychiatry Research, 268, 53-59.

Shu, T.*#, Peng, Y.*, Fan, L., Zhu, S., & Lu, H. (2017). Perception of human interaction based on motion trajectories: from aerial videos to decontextualized animations. Topics in Cognitive Science, 10(1), 225-241.

Peng, Y. #, Thurman, S., & Lu, H. (2017). Causal action: A fundamental constraint on perception and inference about body movements. Psychological Science, 28(6), 798-807.

van Boxtel, J. #, Peng, Y., Su, J., & Lu, H. (2016). Individual differences in high-level biological motion tasks correlate with autistic traits. Vision Research, 141, 136-144.

Chen, J., Yu, Q., Zhu, Z., Peng, Y., & Fang, F.# (2016). Spatial summation revealed in the earliest visual evoked component C1 and the effect of attention on its linearity. Journal of Neurophysiology, 115(1), 500-509.

Chen, J., He, Y., Zhu, Z., Zhou, T., Peng, Y., Zhang, X., & Fang, F.# (2014). Attention-dependent early cortical suppression contributes to crowding. The Journal of Neuroscience, 34(32), 10465-10474.

Lu, J. *, & Peng, Y.*# (2014). Brain-computer interface for cyberpsychology: components, methods, and applications. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 4(1), 1-14.