Getting more out of Area of Interest (AOI) analysis with SPLOT

To analyze eye-tracking data the viewed image is often divided into areas of interest (AOI). However, the temporal dynamics of eye movements towards the AOI is often lost either in favor of summary statistics (e.g., proportion of fixations or dwell time) or is significantly reduced by binning the data and computing the same summary statistic over each time bin. This paper introduces SPLOT: smoothed proportion of looks over time method for analyzing the eye movement dynamics across AOI. SPLOT comprises of a complete workflow, from visualization of the time-course to performing statistical analysis on it using cluster-based permutation testing. The possibilities of SPLOT are illustrated by applying it to an existing dataset of eye movements of radiologists diagnosing a chest X-ray.