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