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SUMMARY:Computational Mechanisms of Angry Face Processing in Depression: A
  Deep Neural Network Perturbation Approach - Professor Qiang Luo\, Fudan U
 niversity 
DTSTART:20251016T113000Z
DTEND:20251016T123000Z
UID:TALK235903@talks.cam.ac.uk
CONTACT:Oliver Knight
DESCRIPTION:According to the Theory of Constructed Emotion\, visual emotio
 n codes in the brain are regularized by abstract emotion concepts from mem
 ory. While neuroimaging studies show that similar concepts elicit similar 
 neural patterns—suggestive of predictive coding through error minimizati
 on—it remains unclear whether these codes are also shaped by development
 al experience. Such experience-dependent specialization could enable effic
 ient emotion recognition without constant memory system engagement\, yet t
 his mechanism is rarely investigated. Here\, we introduce a deep neural ne
 twork (DNN) framework to probe this process. First\, we trained a concept-
 regularized DNN to model the brain’s visual emotion codes. Next\, we per
 turbed 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 f
 indings illuminate how concept regularization underpins facial emotion rec
 ognition and its dysfunction in depression\, offering a novel DNN-based le
 ns for computational psychiatry.
LOCATION:Hybrid (in-person at the Herchel Smith Building and online via zo
 om)
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