A gaze-based experimenter platform for designing and evaluating adaptive interventions in information visualizations

We present an experimenter platform for designing and evaluating user-adaptive support in information visualizations. Specifically, this platform leverages eye-tracking data in real time to deliver adaptive support in visualizations based on the user's attentional patterns and individual needs. We describe the main functionalities of this platform, and show an application to support processing of textual documents with embedded bar charts, by dynamically providing highlighting in the charts to guide a user's attention to the relevant information.