在边界性人格障碍中的网络球范式期间自动检测面部表情:试点研究
Iván Arango-de-Montis1, Adriana Reyes-Soto1, Alejandra Rosales-Lagarde1,2
1Dirección de Servicios Clínicos, Instituto Nacional de Psiquiatría "Ramón de la Fuente Muñiz", Mexico City, Mexico.
Frontiers in psychiatry
|June 19, 2024
概括
边界性人格障碍 (BPD) 患者在社会排斥期间表现出改变的情绪反应. 人工智能分析揭示了惊喜的掩饰和厌恶/蔑视和情绪调节困难之间的反向相关性.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 精神病学是一个精神病学.
背景情况:
- 边界性人格障碍 (BPD) 的特点是愤怒控制问题和情绪失调.
- 网络球模式模拟了社会排斥,以研究情绪反应.
- 以前的研究还没有详尽地追踪在BPD中社会排斥期间的基本情绪.
研究的目的:
- 通过使用Cyberball范式,调查BPD患者在社会排斥期间表达七种基本情绪的表达.
- 探索AI在BPD中检测情绪混合和掩饰方面的潜力.
- 为了将特定的情绪表达与BPD症状和情绪调节困难相关联.
主要方法:
- 使用FaceReader软件对14名BPD患者玩网络球的视频进行面部情绪分析.
- 社交排斥与包容条件期间情绪表达的比较.
- 情绪表达和临床措施之间的相关性分析.
主要成果:
- 观察到愤怒的相互作用:它在纳入过程中增加,在排除过程中减少.
- 惊的掩饰 (在惊时表现出快乐) 在排斥期间明显高.
- 厌恶和蔑视分别与情绪调节困难和BPD症状相反相关.
结论:
- 在模拟社会排斥期间自动检测面部情绪表情可以帮助诊断BPD.
- 在排斥期间,在BPD中,情绪掩饰和改变的愤怒反应是显著的.
- 研究结果表明,BPD诊断和治疗监测的潜在生物标志物.
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