A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses

Eye-tracking data is traditionally analyzed by looking at where on a visual stimulus subjects fixate, or, to facilitate more advanced analyses, by using area-of-interests (AOI) defined onto visual stimuli. Recently, there is increasing interest in methods that capture what users are looking at rather than where they are looking. By instrumenting visualization code that transforms a data model into visual content, gaze coordinates reported by an eye-tracker can be mapped directly to granular data shown on the screen, producing temporal sequences of data objects that subjects viewed in an experiment. Such data collection, which is called gaze to object mapping (GTOM) or data-of-interest analysis (DOI), can be done reliably with limited overhead and can facilitate research workflows not previously possible. Our paper contributes to establishing a foundation of DOI analyses by defining a DOI data model and highlighting its differences to AOI data in structure and scale; by defining and exemplifying a space of DOI enabled tasks; by describing three concrete examples of DOI experimentation in three different domains; and by discussing immediate research challenges in creating a framework of visual support for DOI experimentation and analysis.