一种混合方法用于识别和解释与年龄相关的虚假记忆中的模式
Noorbakhsh Amiri Golilarz1, Elias Hossain2, Shahram Rahimi1
1Department of Computer Science, The University of Alabama, Tuscaloosa, AL, United States.
Frontiers in psychology
|August 7, 2025
概括
由于语义上的相似性,老年人更容易受到虚假记忆的影响,与从阅读时间中受益的年轻人不同. 这项研究使用机器学习来识别衰老中的记忆模式.
科学领域:
- 认知神经科学 认知神经科学
- 计算心理学 计算心理学
- 老年学是一门学科.
背景情况:
- 老龄化会损害诸如记忆等认知功能,影响生活质量.
- 与年龄相关的虚假记忆形成的机制尚不清楚.
- 目前的方法在研究认知衰老方面存在局限性.
研究的目的:
- 开发一种混合计算方法,以区分年轻和老年人的真实和虚假记忆.
- 确定与年龄相关记忆力下降相关的行为模式.
- 使用语言模型提高发现的可解释性.
主要方法:
- 一种混合方法,结合机器学习 (LightGBM) 和语言模型.
- 识别九个关键特征,使用顺序的重要性.
- 检查特征与年龄的相互作用,以获得认知衰退的见解.
主要成果:
- 年轻的成年人利用阅读时间来拒绝误导性信息 (诱惑).
- 老年人对从语义相似性推断的脆弱性增加.
- 经过修改的LightGBM模型实现了F1得分0.82和召回0.88.8的F1得分.
结论:
- 混合计算方法在发现记忆衰退模式方面是有效的.
- 研究结果提供了对衰老中的虚假记忆机制的见解.
- 开发的模型表现优于深度学习和基于变压器的方法.
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