在密集的二进制时间序列眼睛跟踪数据中建模时空模式,使用通用添加混合模型
Sarah Brown-Schmidt1, Sun-Joo Cho1, Kimberly M Fenn2
1Vanderbilt University, Department of Psychology & Human Development, United States.
Brain research
|February 20, 2025
概括
一般化添加混合模型 (GAMM) 分析密集的二进制时间序列眼睛跟踪数据. 这种方法揭示了语音感知动态和空间关系如何影响随时间推移的固定概率.
科学领域:
- 心理语言学 心理语言学
- 认知科学 认知科学
- 计算语言学 计算语言学
背景情况:
- 眼睛跟踪研究经常产生密集的二进制时间序列数据.
- 分析这些数据需要能够处理自回归模式和复杂的时间依赖性的方法.
- 现有的方法可能无法完全捕捉语言处理过程中空间信息和时间之间的动态相互作用.
研究的目的:
- 介绍和演示通用添加混合模型 (GAMM) 用于分析密集的二进制时间序列眼睛跟踪数据.
- 为了说明时空GAMM如何在语音感知中显示时间变化的效果.
- 展示一种用于模拟视觉世界眼球跟踪中复杂的时空关系的新技术.
主要方法:
- 空间-时间通用添加混合模型 (GAMM) 的应用.
- 在语音感知过程中对密集的二进制时间序列眼睛跟踪数据进行分析.
- 交叉随机效应 (按人与物品) 和自回归模式的建模.
主要成果:
- 确定固定条件效应和时间偶然性在语音感知过程中随着时间的推移而变化.
- 证明固定点和参考点之间的空间关系调节目标固定概率.
- 表明空间关系对固定感的影响随着语言的展开而动态变化.
结论:
- GAMM为分析复杂的眼睛跟踪数据提供了一个强大的框架.
- 这种技术允许在语言处理中建模动态的,时间变化的效应.
- 这种方法使得关于空间,时间和语言理解的相互作用的新研究问题成为可能.
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