Uncertainty Visualization Influences how Humans Aggregate Discrepant Information

Abstract

The number of sensors in our surroundings that provide the same information steadily increases. Since sensing is prone to errors, sensors may disagree. For example, a GPS-based tracker on the phone and a sensor on the bike wheel may provide discrepant estimates on traveled distance. This poses a user dilemma, namely how to reconcile the conflicting information into one estimate. We investigated whether visualizing the uncertainty associated with sensor measurements improves the quality of users' inference. We tested four visualizations with increasingly detailed representation of uncertainty. Our study repeatedly presented two sensor measurements with varying degrees of inconsistency to participants who indicated their best guess of the "true" value. We found that uncertainty information improves users' estimates, especially if sensors differ largely in their associated variability. Improvements were larger for information-rich visualizations. Based on our findings, we provide an interactive tool to select the optimal visualization for displaying conflicting information.