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.
Website:
www.yujiapeng.com
www.ypeng.org
List of Publications
(* Equal contribution, # Corresponding author)
Ju, Q., Chen, Z., Xu, Z., Fan, J., Zhang, H., Peng, Y. (2025). Screening Social Anxiety with the Social Artificial Intelligence Picture System. Journal of Anxiety Disorders, 109, 102955.
Liu, F., Wang, P., Hu, J., Shen, S., Wang, H., Shi, C., Peng, Y., & Zhou, A. (2025). A psychologically interpretable artificial intelligence framework for the screening of loneliness, depression, and anxiety. Applied Psychology: Health and Well‐Being, 17(1), e12639.
彭玉佳, 王愉茜, 鞠芊芊, 刘峰, 徐佳. (2025). 贝叶斯框架下社交焦虑的社会认知特性. 心理科学进展, 33(8), 1267-1274.
Liu, F., Ju, Q. Zheng, Q., Peng, Y. (2024). AI in Mental Health: Innovations brought by AI Techniques in Stress Detection and Interventions of Building Resilience. Current Opinion in Behavioral Sciences, 60, 101452. https://doi.org/10.1016/j.cobeha.2024.101452
Peng, Y., Gong, X., Lu, H., & Fang, F. (2024). Human Visual Pathways for Action Recognition versus Deep Convolutional Neural Networks: Representation Correspondence in Late but Not Early Layers. Journal of Cognitive Neuroscience, 36(11), 2458-2480. https://doi.org/10.1162/jocn_a_02233
Cushing, C. A. , Peng, Y., Anderson, Z., Young, K. S., Bookheimer, S. Y., Zinbarg, R. E., Nusslock, R., & Craske, M. G. (2024). Broadening the scope: Multiple functional connectivity networks underlying threat conditioning and extinction. Imaging Neuroscience. 2: 1–15. https://doi.org/10.1162/imag_a_00213
王愉茜, 臧寅垠, & 彭玉佳. (2024). 成人社交焦虑问卷中文版的效度和信度评价. 中国心理卫生杂志, 38(08), 730–736. DOI: 10.3969/j.issn.1000-6729.2024.08.015
Peng, Y., Burling J., Todorova G., Pollick F., & Lu, H. (2024). Patterns of Saliency and Semantic Features Distinguish Gaze of Expert and Novice Viewers of Surveillance Footage. Psychonomic Bulletin & Review. 31, 1745-1758. https://doi.org/10.3758/s13423-024-02454-y
Xu, J., Wang, Y., Peng, Y. (2024) Psychodynamic Profiles of Major Depressive Disorder and Generalized Anxiety Disorder in China. Frontiers in Psychiatry. 15:1312980. doi: 10.3389/fpsyt.2024.1312980
Peng, Y., Han J., Zhang Z., Fan L., Liu T., Qi S., Feng X., Ma Y., Wang Y., Zhu. S.C.,（2024）The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social Interactions. Engineering.34(3), 12-22. https://doi.org/10.1016/j.eng.2023.07.006
彭玉佳, 王愉茜, 路迪. (2023). 基于生物运动的社交焦虑者情绪加工与社会意图理解负向偏差机制.心理科学进展,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., 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.