Eye Tracking Metrics: A Toolbox for Assessing the Effects of Clutter on Attention Allocation

Abstract

Display clutter is a problem that affects operators in various data-rich environments. Clutter measurement techniques such as image processing and performance measures can provide an estimate of clutter but are largely not suited to tracing the effects of clutter on the dynamic allocation of attention. Eye tracking is a promising process-oriented tool that can help assess in real-time the attentional costs associated with the different aspects of clutter. In this experiment, we investigated which of a number of eye tracking metrics in the literature are sensitive to clutter. Twenty-two participants were asked to look for a target in static and dynamic images that were classified as either high or low in clutter. Response time and error rate were recorded, and an eye tracker was used to compute the identified eye tracking metrics. Results showed that, in both the static and dynamic conditions, a large number of eye tracking metrics were significantly affected by an increase in clutter. This suggests that eye tracking can be used to supplement other clutter measurement techniques by providing information about dynamic attention allocation.