自闭症早期的多式联动行为线索:在自然主义的亲子互动过程中对行为,手势和言语的微分析性探索
M Mastrogiuseppe1, F I Famà1, R Bruschetta1
1National Research Council of Italy, Institute for Biomedical Research and Innovation (CNR-IRIB), Messina, Italy.
International journal of clinical and health psychology : IJCHP
|February 3, 2026
概括
在沟通和游戏中早期的自闭症症状可以通过对运动和声乐行为的微分析来确定. 机器学习可以根据特定的运动和手势模式,准确地区分自闭症 (ASC) 和神经类型 (NT) 儿童.
科学领域:
- 发展心理学 发展心理学
- 神经发育障碍 神经发育障碍
- 机器学习在行为科学中的应用
背景情况:
- 早期发现自闭症谱系障碍 (ASD) 对于及时干预至关重要.
- 自然主义的亲子游戏为观察早期行为标记提供了一个生态有效的背景.
- 现有的研究往往缺乏在自然主义互动期间对多式联络行为的细粒度分析.
研究的目的:
- 为了调查明显的运动,手势和声音行为,区分自闭症 (ASC) 和神经类型 (NT) 儿童.
- 应用机器学习来识别早期自闭症神经分歧的关键行为预测因素.
- 为早期自闭症检测验证一种生态和发育相关的方法.
主要方法:
- 微分析编码58名儿童 (13-40个月) 在自然主义的亲子游戏中录制的视频.
- 详细捕捉行动,手势,言语,以及它们的多式联络整合.
- 机器学习 (ANOVA F测试,逻辑回归) 用于识别区分行为特征.
主要成果:
- 在NT和ASC儿童之间观察到的手势类型,手势-目光协调,物体游戏和发音方面存在显著差异.
- 确定了关键预测因素:交替的目光 (与NT相关),伸展和仪器手势 (与ASC相关).
- 一种后勤回归模型在将儿童分类为NT或ASC时取得了超过85%的准确性.
结论:
- 在生态上有效的微分析方法揭示了自闭症的早期行为标志物.
- 特定的运动和手势模式 (交替的目光,伸手,工具手势) 是神经型的高度预测.
- 这些发现支持更早地识别自闭症,并开发个性化,基于优势的干预措施.
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