目標(biāo)和宗旨

彭玉佳研究員領(lǐng)導(dǎo)的抑郁與焦慮障礙計(jì)算神經(jīng)實(shí)驗(yàn)室（Depression & Anxiety Computational Neuroscience Lab，DACN）主要聚焦于臨床心理學(xué)的基礎(chǔ)研究，并結(jié)合認(rèn)知神經(jīng)和人工智能的交叉研究，致力于探究抑郁和焦慮障礙的心理與神經(jīng)機(jī)制以及治療方法。

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  • Ph.D.博士同學(xué)：我們是一個(gè)高度學(xué)科交叉的實(shí)驗(yàn)室，涉及臨床心理學(xué)、計(jì)算精神病學(xué)、認(rèn)知神經(jīng)科學(xué)、人工智能等多學(xué)科研究?jī)?nèi)容，歡迎不同背景的同學(xué)加入我們。
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研究方法

  1. 問(wèn)卷量表和訪談，線上調(diào)查和實(shí)驗(yàn)
  2. 行為學(xué)手段，包括心理物理學(xué)，眼動(dòng)技術(shù)
  3. 腦成像，包括腦電，核磁共振成像，腦磁圖和近紅外成像
  4. 計(jì)算建模，機(jī)器學(xué)習(xí)

研究?jī)?nèi)容

  1. 計(jì)算精神病學(xué)及社交焦慮的先驗(yàn)偏差。通過(guò)計(jì)算模型和數(shù)據(jù)挖掘，來(lái)建模臨床病人的認(rèn)知特點(diǎn)，比較病人與常人的認(rèn)知行為差異，理解精神疾病背后的機(jī)制，預(yù)期實(shí)現(xiàn)基于多維度數(shù)據(jù)的診斷和分類，以及發(fā)病的早期預(yù)測(cè)。社交恐懼癥處于多種疾病交叉的中心，具有復(fù)雜的認(rèn)知、情緒和行為的個(gè)體差異。然而，對(duì)于社交恐懼的理解還存在很多未知，并忽視了同樣重要的且包含大量社會(huì)信息的身體運(yùn)動(dòng)和社交運(yùn)動(dòng)。實(shí)驗(yàn)室結(jié)合心理物理學(xué)、眼動(dòng)捕捉、腦成像以及生理信號(hào)記錄，從縱向的時(shí)間維度和橫向的多數(shù)據(jù)維度，研究社交恐懼病人對(duì)于運(yùn)動(dòng)中社會(huì)信息的加工特異性。
  2. 社交媒體對(duì)于心理健康的影響。社交媒體的廣泛使用對(duì)心理健康的影響備受關(guān)注。研究表明，過(guò)度使用社交媒體可能導(dǎo)致焦慮、抑郁和孤獨(dú)感，尤其是青少年群體。負(fù)面效應(yīng)常源于社交比較、網(wǎng)絡(luò)欺凌和睡眠干擾。然而，社交媒體也能提供社會(huì)支持、增強(qiáng)人際聯(lián)系，對(duì)心理健康有積極影響。個(gè)體差異、使用動(dòng)機(jī)和內(nèi)容類型是關(guān)鍵調(diào)節(jié)因素。實(shí)驗(yàn)室采用橫斷面和縱向追蹤設(shè)計(jì)，探討社交媒體使用與心理健康的關(guān)系。橫斷面調(diào)查將測(cè)量被試的社交媒體使用頻率、內(nèi)容偏好及心理健康指標(biāo)；縱向追蹤通過(guò)多次數(shù)據(jù)收集，分析使用模式的變化對(duì)心理健康的影響。
  3. 抑郁與焦慮障礙的認(rèn)知神經(jīng)機(jī)制。當(dāng)前科學(xué)界對(duì)于抑郁癥和焦慮癥的發(fā)展機(jī)制還存在很多未知，難以實(shí)現(xiàn)精神疾病的早期診斷和預(yù)測(cè)。社交恐懼癥是焦慮癥中一個(gè)重要的分支，體現(xiàn)為對(duì)于社交行為和場(chǎng)合的極度焦慮和回避，嚴(yán)重影響了病人的正常工作和生活，且為病人就醫(yī)和尋求治療帶來(lái)了極大的阻礙，從而形成一個(gè)惡性循環(huán)。從青春期至成年的過(guò)渡時(shí)期，該人生階段伴隨著前所未有的挑戰(zhàn)、生活壓力及人際關(guān)系，同時(shí)處于情緒和焦慮障礙發(fā)病的高峰時(shí)期。實(shí)驗(yàn)室主要關(guān)注大腦活動(dòng)和情緒障礙癥狀維度隨時(shí)間變化的關(guān)系。注重時(shí)間維度上的縱向追蹤，探究從青春期至成年期的發(fā)病誘因和神經(jīng)發(fā)展機(jī)制，以及環(huán)境和家庭因素對(duì)于情緒、認(rèn)知和神經(jīng)網(wǎng)絡(luò)的調(diào)節(jié)。
  4. 精神疾病的創(chuàng)新治療方法。實(shí)驗(yàn)室采用計(jì)算建模、數(shù)據(jù)挖掘和解碼神經(jīng)反饋等前沿技術(shù)，深入探索精神疾病的發(fā)病機(jī)制。通過(guò)構(gòu)建臨床病人的認(rèn)知計(jì)算模型，系統(tǒng)比較其與健康人群在認(rèn)知行為、神經(jīng)活動(dòng)等多維度的差異，揭示精神疾病的核心特征。在此基礎(chǔ)上，我們開(kāi)發(fā)基于多模態(tài)數(shù)據(jù)的智能診斷和分類系統(tǒng)，以實(shí)現(xiàn)更精準(zhǔn)的早期預(yù)測(cè)。進(jìn)一步，結(jié)合解碼神經(jīng)反饋等創(chuàng)新神經(jīng)調(diào)控技術(shù)，實(shí)現(xiàn)對(duì)異常神經(jīng)活動(dòng)的靶向干預(yù)，為精神疾病提供個(gè)性化、非藥物的治療新途徑。該研究有望推動(dòng)精神疾病診療的智能化發(fā)展。

公眾號(hào)

科研隊(duì)伍

負(fù)責(zé)人

彭玉佳 ([email protected])

博士生

王愉茜 ([email protected])

路迪（[email protected]）

德吉央拉（[email protected]）

賈仁和（[email protected]）

傅雨秋（[email protected]）

科研助理

程真嚳（[email protected]）

碩士生

麥燁婧，肖嘉茵，段海容，單婷，楊煦

Alumni

博士后：鞠芊芊

碩士生：江欣、彭旱雨、李自立、李婉心

代表性論文

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, 劉峰, 徐佳. (2025). 貝葉斯框架下社交焦慮的社會(huì)認(rèn)知特性. 心理科學(xué)進(jìn)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). 成人社交焦慮問(wèn)卷中文版的效度和信度評(píng)價(jià). 中國(guó)心理衛(wèi)生雜志, 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). 基于生物運(yùn)動(dòng)的社交焦慮者情緒加工與社會(huì)意圖理解負(fù)向偏差機(jī)制.心理科學(xué)進(jìn)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.