Space-Time Modeling of Intensive Binary Time Series Eye-Tracking Data Using a Generalized Additive Logistic Regression Model

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 (e.g., 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. Translational Abstract A common technique for studying cognitive processes is the use of eye-tracking technology to monitor where the eyes are looking as a participant completes a task. Eye-tracking systems can generate data that is both spatially and temporally precise, and that can be used to make inferences about the underlying cognitive processes that support the task at hand. A common way of examining eye-tracking data is to obtain a measure of where, at each moment in time, the participant was looking. When the researcher is interested in a single target interest area, the researcher is working with intensive binary time-series data indicating whether or not, at each moment in time, the participant was fixating the target. In such data structures, we can expect strong temporal autocorrelation, as well as spatial-temporal correlations, because at certain time points, the distance between the current fixation position and the target location, and potential nontarget lures, m