I describe and compare procedures for binary eye-tracking (ET) data. These procedures are applied to both raw and compressed data. The basic GLMM model is a logistic mixed model combined with random effects for persons and items. Additional models address autocorrelation eye-tracking serial observations. In particular, two novel approaches are illustrated that address serial without the use of an observed lag-1 predictor: a first-order autoregressive model obtained with generalized estimating equations, and a recurrent two-state survival model. Altogether, the results of four different analyses point to unresolved issues in the analysis of eye-tracking data and new directions for analytic development.

翻译：本文描述并比较了针对二元眼动追踪（ET）数据的处理方法。这些方法同时应用于原始数据与压缩数据。基础GLMM模型为结合了被试与项目随机效应的逻辑混合模型。额外模型用于处理眼动追踪序列观测中的自相关问题。特别地，本文阐释了两种无需使用观测滞后一期预测变量的创新序列处理方法：通过广义估计方程获得的一阶自回归模型，以及循环双状态生存模型。综合四项不同分析的结果表明，眼动追踪数据分析中仍存在未解决的问题，并为分析方法的拓展指明了新方向。