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In this paper, we present BubbleView, an alternative methodology for eyetracking using discrete mouse clicks to measure which information peopleconsciously choose to examine. BubbleView is a mouse-contingent, moving-windowinterface in which participants are presented with a series of blurred imagesand click to reveal "bubbles" - small, circular areas of the image at originalresolution, similar to having a confined area of focus like the eye fovea.Across 10 experiments with 28 different parameter combinations, we evaluatedBubbleView on a variety of image types: information visualizations, naturalimages, static webpages, and graphic designs, and compared the clicks to eyefixations collected with eye-trackers in controlled lab settings. We found thatBubbleView clicks can both (i) successfully approximate eye fixations ondifferent images, and (ii) be used to rank image and design elements byimportance. BubbleView is designed to collect clicks on static images, andworks best for defined tasks such as describing the content of an informationvisualization or measuring image importance. BubbleView data is cleaner andmore consistent than related methodologies that use continuous mouse movements.Our analyses validate the use of mouse-contingent, moving-window methodologiesas approximating eye fixations for different image and task types.

BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention