Statistical Advances for Intensive Categorical Spatiotemporal Data from an Eye-Tracking Technique

该研究项目将开发分析眼动追踪数据的方法，从而更准确地了解人类行为的潜在认知过程。眼动往往会随着注意力而跟踪，而眼动跟踪是跟踪眼动的灵活工具。尽管眼动追踪方法的普遍性和实用性，目前的数据分析实践是有限的。有待开发的方法是通用的，可用于在收集可比数据的其他领域增进知识。研究人员将与心理语言学，教育和计算机科学的研究人员在跨学科的合作努力中应用新方法。研究生和本科生将接受这些分析技术的培训，该项目的结果将通过期刊文章，会议，研讨会和网络研讨会传播到专业和多学科社区。该项目的所有发现、数据、代码和软件都将公开提供。该研究项目将为密集的分类时间序列数据开发统计模型，这些数据具有复杂的时间，空间和依赖结构，这些结构由眼动跟踪数据表现出来。研究人员将开发一种称为动态广义线性混合效应模型或动态GLMM的通用模型。这些模型将捕获时间效应（序列依赖性和趋势效应）和空间效应（空间依赖性和距离效应），同时考虑数据模式之间的多种形式的变化，包括试验、项目、人员、项目组（或集群）和人员组。研究者将为二进制数据提供一个动态GLMM规范，该规范将指示参与者是否在每个时刻都在关注关键兴趣区域。研究人员还将使用IRTree方法为有序类别或部分有序类别数据提供动态GLMM规范。这种方法允许研究人员检查驱动眼睛注视的多个刺激中的选择的认知过程。为了改进数据分析实践，将按照数据分析程序的顺序开发使用动态GLMM的方法：数据描述、模型说明、模型估计、模型选择、该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

This research project will develop methods for analyzing eye-tracking data that will provide a more accurate view of the underlying cognitive processes of human behavior. Eye movements tend to track with attention, and eye-tracking is a flexible tool for tracking eye movements. Despite the ubiquity and utility of eye-tracking methodology, current data analytic practices are limited. The methods to be developed are general and may be used to advance knowledge in other fields where comparable data are collected. The investigators will apply the new methods in interdisciplinary collaborative efforts with researchers from psycholinguistics, education, and computer science. Graduate and undergraduate students will be trained in these analytic techniques, and the results of this project will be disseminated to both specialized and multidisciplinary communities through journal articles, conferences, workshops, and a webinar. All findings, data, code, and software from this project will be made publicly available. This research project will develop statistical models for intensive categorical time series data with the complex temporal, spatial, and dependence structures exhibited by eye-tracking data. The investigators will develop a general class of models called dynamic generalized linear mixed effect models or dynamic GLMM. These models will capture both temporal effects (serial dependence and trend effects) and spatial effects (spatial dependence and distance effects) while accounting for multiple forms of variation across data modes including trials, items, persons, item groups (or clusters), and person groups. The investigators will present a dynamic GLMM specification for binary data, which will indicate whether the participant is or is not fixating on a critical interest area at each moment in time. The investigators also will present a dynamic GLMM specification for ordered-category or partially ordered category data using an IRTree approach. This approach allows the researcher to examine cognitive processes which drive the choice among multiple stimuli for the eye to fixate on. To improve data analytic practices, methods using the dynamic GLMM will be developed following the sequence of the data analytic procedures: data description, model specification, model estimation, model selection, and model evaluation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.