Computational Mechanisms of Angry Face Processing in Depression: A Deep Neural Network Perturbation Approach

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• Professor Qiang Luo, Fudan University
• Thursday 16 October 2025, 12:30-13:30
• Hybrid (in-person at the Herchel Smith Building and online via zoom).

If you have a question about this talk, please contact Oliver Knight.

According to the Theory of Constructed Emotion, visual emotion codes in the brain are regularized by abstract emotion concepts from memory. While neuroimaging studies show that similar concepts elicit similar neural patterns—suggestive of predictive coding through error minimization—it remains unclear whether these codes are also shaped by developmental experience. Such experience-dependent specialization could enable efficient emotion recognition without constant memory system engagement, yet this mechanism is rarely investigated. Here, we introduce a deep neural network (DNN) framework to probe this process. First, we trained a concept-regularized DNN to model the brain’s visual emotion codes. Next, we perturbed its regularization strength to simulate impaired emotion processing in depression. From this mechanism, we derived a computational phenotype and validated its utility in population-based and clinical cohorts. Our findings illuminate how concept regularization underpins facial emotion recognition and its dysfunction in depression, offering a novel DNN -based lens for computational psychiatry.

This talk is part of the Department of Psychiatry & CPFT Thursday Lunchtime Seminar Series series.

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