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首页> 外文期刊>Psychometrika >Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model
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Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model

机译：建模密集多种时间序列眼睛跟踪数据：一种基于动态的树木项目响应模型

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摘要

This paper presents a dynamic tree-based item response (IRTree) model as a novel extension of the autoregressive generalized linear mixed effect model (dynamic GLMM). We illustrate the unique utility of the dynamic IRTree model in its capability of modeling differentiated processes indicated by intensive polytomous time-series eye-tracking data. The dynamic IRTree was inspired by but is distinct from the dynamic GLMM which was previously presented by Cho, Brown-Schmidt, and Lee (Psychometrika 83(3):751-771, 2018). Unlike the dynamic IRTree, the dynamic GLMM is suitable for modeling intensive binary time-series eye-tracking data to identify visual attention to a single interest area over all other possible fixation locations. The dynamic IRTree model is a general modeling framework which can be used to model change processes (trend and autocorrelation) and which allows for decomposing data into various sources of heterogeneity. The dynamic IRTree model was illustrated using an experimental study that employed the visual-world eye-tracking technique. The results of a simulation study showed that parameter recovery of the model was satisfactory and that ignoring trend and autoregressive effects resulted in biased estimates of experimental condition effects in the same conditions found in the empirical study.
机译：本文介绍了一种基于动态的树木项目响应（IRTree）模型，作为自回归广义线性混合效果模型（动态GLMM）的新颖延伸。我们说明了动态IRTree模型在其建模差异化过程的能力中的独特效用，该过程由密集多种时间序列追踪数据指示的差异化过程。动态iRtree受到了激发的灵感，但与以前由Cho，Brown-Schmidt和Lee（Psycometrika 83（3）：751-771,2018）呈现的动态GLMM。与动态IRTree不同，动态GLMM适用于建模密集型二进制时间序列的眼睛跟踪数据，以识别对所有其他可能的固定位置的单个兴趣区域的视觉注意。动态IRTree模型是一般建模框架，可用于建模改变过程（趋势和自相关），并且允许将数据分解成各种异质性源。使用采用视觉世界的眼睛跟踪技术的实验研究说明了动态IRTree模型。模拟研究的结果表明，该模型的参数恢复令人满意，忽略趋势和自回归效应导致了实证研究中相同条件下的实验条件效应的偏差估计。

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