Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic regression model.
Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic regression model.
复制标题
使用广义加性逻辑回归模型对密集二进制时间序列眼动追踪数据进行时空建模。
DOI：
10.1037/met0000444
复制
发表时间：
2022
影响因子：
7
通讯作者：
Naveiras, Matthew
中科院分区：
文献类型：
--
作者：
Cho, Sun-Joo;Brown-Schmidt, Sarah;De Boeck, Paul;Naveiras, Matthew
眼球跟踪已经成为一种流行的方法，用于跨多个实质性研究领域的认知过程的实证研究。眼睛跟踪系统能够以高时间分辨率自动生成随时间推移的注视位置数据。通常，研究人员获得在每个时间点参与者是否专注于现实世界或计算机显示器中的关键感兴趣区域或对象的二进制测量。眼睛跟踪数据的特点是时空相关性和随机变异性，由小时间间隔(例如，每10毫秒)获取的多个细粒度观测驱动。忽略这些数据的复杂性会导致对感兴趣的协变量(如实验条件效应)的偏颇推断。本文提出了一种新的广义加性Logistic回归模型应用于密集的二进制时间序列眼球跟踪数据，该数据来自受试者之间和受试者内的实验设计。该模型被描述为广义可加混合模型(GAMM)，并在MGCVR程序包中实现。广义加性Logistic回归模型被用一个旨在理解口语处理中区域口音适应的经验数据集来说明。通过一项模拟研究，在与我们的经验数据集相似的条件下，显示了参数估计的准确性和对时空相关性进行建模在检测实验条件影响方面的重要性。
Eye-tracking has emerged as a popular method for empirical studies of cognitive processes across multiple substantive research areas. Eye-tracking systems are capable of automatically generating fixation-location data over time at high temporal resolution. Often, the researcher obtains a binary measure of whether or not, at each point in time, the participant is fixating on a critical interest area or object in the real world or in a computerized display. Eye-tracking data are characterized by spatial-temporal correlations and random variability, driven by multiple fine-grained observations taken over small time intervals (eg, every 10 ms). Ignoring these data complexities leads to biased inferences for the covariates of interest such as experimental condition effects. This article presents a novel application of a generalized additive logistic regression model for intensive binary time series eye-tracking data from a between-and within-subjects experimental design. The model is formulated as a generalized additive mixed model (GAMM) and implemented in the mgcv R package. The generalized additive logistic regression model was illustrated using an empirical data set aimed at understanding the accommodation of regional accents in spoken language processing. Accuracy of parameter estimates and the importance of modeling the spatial-temporal correlations in detecting the experimental condition effects were shown in conditions similar to our empirical data set via a simulation study.(PsycInfo Database Record (c) 2022 APA, all rights reserved)
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影响因子：
24.8
作者：
Kutas M;Federmeier KD
通讯作者：
Federmeier KD
影响因子：
4.3
作者：
Barr, Dale J.
通讯作者：
Barr, Dale J.
影响因子：
5.8
作者：
Aaron M. Christ
通讯作者：
Aaron M. Christ
使用广义加性（混合）模型来分析单一案例设计。
影响因子：
5
作者：
W. Shadish;A. Zuur;K. J. Sullivan
通讯作者：
K. J. Sullivan
感知不确定性的时间动态：来自粤语片段和声调感知的眼动证据
DOI：
--
发表时间：
2016
期刊：
影响因子：
--
作者：
Jessie S. Nixon;Jacolien van Rij;P. Mok;R. Baayen;Yiya Chen
通讯作者：
Yiya Chen