在密集的二进制纵向眼睛跟踪数据中,使用GAMM的副变量平滑函数来比较组间的功能趋势和学习
Sun-Joo Cho1, Sarah Brown-Schmidt1, Sharice Clough2,3
1Vanderbilt University.
Psychometrika
|April 14, 2025
概括
这项研究介绍了一种分析眼睛跟踪数据的统计模型,以了解脑损伤和没有脑损伤的个体的语言理解差异和随时间的学习. 该模型有效地在纵向数据中捕捉功能趋势和学习效应.
科学领域:
- 神经科学是一个神经科学.
- 心理语言学 心理语言学
- 统计建模 统计建模
背景情况:
- 密集的纵向眼睛跟踪数据提供了对实时认知过程的见解.
- 了解脑损伤和没有脑损伤的个体之间的语言理解和学习差异至关重要.
研究的目的:
- 介绍一个通用的添加混合模型,用于分析功能趋势和随时间学习的群体差异.
- 将该模型应用于用于大脑损伤研究的密集二元纵向眼睛跟踪数据.
主要方法:
- 使用了通用添加混合模型 (GAMM) 框架.
- 使用变量流函数来建模功能趋势和学习效应.
- 利用R包"mgcv"进行模型实现.
主要成果:
- 模拟研究表明,模型参数的恢复很好.
- 通过可变的平滑函数得到了充分的预测.
- 该模型成功应用于眼睛跟踪数据,比较有和没有脑损伤的个体.
结论:
- 拟议的GAMM对于分析密集的纵向眼睛跟踪数据是有效的.
- 该模型可以区分不同组的功能趋势和学习效应.
- 这种方法为研究脑损伤人群的认知过程提供了一个强大的方法.
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