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Updated: Sep 10, 2025
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使用人工智能驱动的情感计算,预测早期儿童与临床医生的前性同步
Giulio Bertamini1,2,3, Silvia Perzolli4, Arianna Bentenuto4
1Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière University Hospital, Sorbonne University, 47-83 Bd de l'Hôpital, Paris, 75013, Île-de-France, France. giulio.bertamini@unitn.it.
Scientific reports
|August 24, 2025
概括
自闭症学龄前儿童和治疗师之间的早期声学同步预测了治疗的成功. 这种自动化方法量化了人际关系,突出了情感参与
科学领域:
- 心理学
- 计算机科学
- 发展科学
背景情况:
- 在心理治疗中,患者与治疗师之间的关系至关重要, 但很难量化, 尤其是在自闭症治疗中.
- 这一领域的行为研究往往耗时,限制了其临床应用.
- 人际交互是自闭症发育干预的有效组成部分.
研究的目的:
- 开发一个自动化系统来测量患者-治疗师在自闭症干预中的声学同步.
- 调查早期同步模式是否预测自闭症学龄前儿童的长期临床结果.
- 探索同步动态,情感参与和发展变化之间的关系.
主要方法:
- 通过深度学习和情感计算开发了一个完全自动化的管道.
- 分析了25名自闭症学龄前儿童和17名治疗师的全会录音.
- 来自复杂系统理论的非线性度量被用来建模儿童和临床医生的声学同步.
主要成果:
- 早期声学同步动态的变化预测了对干预的临床反应.
- 改善的结果与同步模式相关,显示出增加的可变性和可预测性.
- 与情感参与相关的前性特征与更好的发育结果有关.
结论:
- 开发的系统为客观测量治疗动态提供了可扩展的非侵入性方法.
- 早期的人际同步和情感参与是自闭症发展变化的关键驱动因素.
- 这种方法提供了更丰富的,生态有效的测量人际同步治疗课程.
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