I describe and compare procedures for binary eye-tracking (ET) data. The basic GLM model is a logistic mixed model combined with random effects for persons and items. Additional models address error correlation in eye-tracking serial observations. In particular, three novel approaches are illustrated that address serial without the use of an observed lag-1 predictor: a first-order autoregressive model and a first-order moving average models obtained with generalized estimating equations, and a recurrent two-state survival model used with run-length encoded data. Altogether, the results of five different analyses point to unresolved issues in the analysis of eye-tracking data and new directions for analytic development. A more traditional model incorporating a lag-1 observed outcome for serial correlation is also included.

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。基础GLM模型为结合个体与项目随机效应的逻辑混合模型。其他模型则用于处理眼动追踪序列观测中的误差相关性。特别地，本文阐述了三种无需使用观测滞后一阶预测变量即可处理序列相关性的新方法：通过广义估计方程获得的一阶自回归模型与一阶移动平均模型，以及应用于游程编码数据的循环双状态生存模型。综合五项不同分析的结果，揭示了眼动追踪数据分析中尚未解决的问题及分析发展的新方向。同时包含了一个采用滞后一阶观测结果处理序列相关性的传统模型。