Towards the Ambiguous and Imprecise: Evaluating Feedforward Uncertainty Visualizations for Probabilistic Input Mechanisms

Abstract

While mixed reality interfaces increasingly adopt probabilistic input techniques for more expressive and accessible interactions, these techniques (including eye gaze, voice, and gestures) introduce increased degrees of ambiguity and imprecision. Thus, feedforward mechanisms are key to communicating this uncertainty around the inputs to users in real time. In this paper, I propose an updated design space for feedforward visualizations that communicate ambiguity through various dimensions of visual augmentation, and I evaluate these visualizations in a study with 15 university students to assess their accuracy, confusion, and observations regarding a target selection task informed by the feedforward visualizations. Participants reported that the visualizations that directly modified physical attributes of the targets and the space around the targets were most effective at clearly communicating the presence and source of ambiguity in the target selection to allow participants to adjust their input in real time. I then propose different types of environments and targets in which each visualization would be most suitable, and I conclude by identifying directions for future work with respect to feedforward visualizations in the context of various mixed reality applications.