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Dynamic facial expression recognition deficits in schizophrenia: A classification study.

Nan Chen1, Yuna She1, Jingkai Jiang1

  • 1Department of Psychology, Zhejiang Sci-Tech University, Hangzhou 310018, PR China.

Asian Journal of Psychiatry
|October 25, 2025
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Summary

Schizophrenia patients show significant facial expression recognition deficits, impacting social function. A machine learning model developed from these deficits achieved high accuracy, showing potential for early schizophrenia screening.

Keywords:
Error patternsFacial expression recognitionMachine learningSchizophreniaSensitivity

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Machine Learning

Background:

  • Schizophrenia is characterized by stable, multi-dimensional facial expression recognition deficits impacting social functioning.
  • Current early screening methods for schizophrenia are limited in scope and effectiveness.

Purpose of the Study:

  • To investigate facial expression recognition deficits in schizophrenia patients.
  • To develop a machine learning-based discrimination model for early schizophrenia screening and precise treatment.

Main Methods:

  • Fifty-one schizophrenia patients and 62 healthy controls performed an emotion recognition task involving morphing facial expressions.
  • Participants identified emotions; clinical symptoms were assessed using standardized scales (e.g., PANSS, SANS, SAPS, BPRS).

Main Results:

  • Schizophrenia patients exhibited lower accuracy and sensitivity in recognizing specific emotions (fear, disgust, anger, happiness, sadness).
  • Patients frequently confused negative emotions and happiness; symptom severity correlated negatively with recognition ability.
  • The screening model achieved 79% accuracy and an AUC of 0.84, with potential for 88% sensitivity and 89% specificity.

Conclusions:

  • Schizophrenia patients have significant, symptom severity-correlated deficits in facial expression recognition accuracy, error patterns, and sensitivity.
  • The developed screening model demonstrates high AUC and sensitivity, indicating its potential as a convenient auxiliary tool for early schizophrenia detection.