Using eye-tracking for visual attention feedback

Abstract

In the age of big data, decision-makers are confronted with enormous amounts of information coming from various resources at high velocity. However, humans have limited cognitive capabilities such as attentional resources. Inappropriate attentional resource allocation can lead to severe losses in performance. Nowadays, the usage of eye-tracking devices brings the opportunity to design neuro-adaptive information systems that support users in better managing their limited attentional resources. In this study, we investigated the design of an attentive information dashboard which provides visual attention feedback (VAF) as live biofeedback. Later, we examined how three different VAF types assist decision makers in their visual attention allocation (VAA) performance and focused attention while conducting a data exploration task. The results show that providing an individualized VAF as live biofeedback using real-time gaze data supports users in managing their attention better than general VAFs.