A Crowdsourced Alternative to Eye-tracking for Visualization Understanding

A Crowdsourced Alternative to Eye-tracking for Visualization Understanding

Nam Wook Kim, Zoya Bylinskii, Michelle A. Borkin, Aude Oliva, Krzysztof Z. Gajos, and Hanspeter Pfister.

(ACM: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, 2015.)

In this study we investigate the utility of using mouse clicks as an alternative for eye fixations in the context of understanding data visualizations. We developed a crowdsourced study online in which participants were presented with a series of images containing graphs and diagrams and asked to describe them. Each image was blurred so that the participant needed to click to reveal bubbles - small, circular areas of the image at normal resolution. This is similar to having a confined area of focus like the human eye fovea. We compared the bubble click data with the fixation data from a complementary eye-tracking experiment by calculating the similarity between the resulting heatmaps. A high similarity score suggests that our methodology may be a viable crowdsourced alternative to eye-tracking experiments, especially when little to no eye-tracking data is available. This methodology can also be used to complement eye-tracking studies with an additional behavioral measurement, since it is specifically designed to measure which information people consciously choose to examine for understanding visualizations.