了解回忆及其在年轻人日常对话中的负面功能:一种机器学习方法
Andrea Ferrario1,2, Burcu Demiray3,4
1ETH Zurich, Zurich, Switzerland.
Heliyon
|January 16, 2024
概括
年轻人回忆以与他人联系,打击无聊或处理困难的记忆. 这项研究表明,自然语言处理可以在对话中自动检测这些回忆功能,帮助数字福祉干预.
科学领域:
- 心理学 心理学 心理学
- 计算语言学 计算语言学
- 数字健康数字健康
背景情况:
- 回忆,回忆个人过去的经历,对于跨年龄组的福祉至关重要.
- 虽然在老年人中进行了广泛的研究,但年轻人的回忆力研究不足.
- 了解年轻人的记忆功能对于有针对性的干预至关重要.
研究的目的:
- 通过自然主义观察,分析年轻人记忆的产生和功能.
- 开发和验证自然语言处理 (NLP) 和机器学习 (ML) 模型,用于自动检测回忆及其在年轻人对话中的功能.
- 用Shapley解释来解释ML模型的发现,以获得更深入的见解.
主要方法:
- 对年轻成年人的日常对话进行自然主义的观察.
- 应用NLP和ML技术用于自动化回忆检测.
- 对于模型可解释性的沙普利值分析.
主要成果:
- 年轻人主要是为了社会联系,刺激补偿和处理困难的记忆而回忆.
- 开发的ML模型比现有的自动回忆检测基准实现了更好的性能.
- 沙普利的解释为驱动回忆检测的特征提供了洞察力.
结论:
- 在年轻人中,自动检测回忆及其功能是可行的,使用NLP和ML.
- 结果可以为数字健康干预措施的发展提供信息,以支持年轻人的福祉.
- 这项研究弥合了理解年轻人的回忆力及其计算分析的差距.
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