Understanding user performance of acquiring targets with motion-in-depth in virtual reality

Abstract

Virtual reality (VR) user interfaces contain numerous dynamic interactive tasks, among which acquiring moving targets is a common basic one. Previous studies have investigated user performance in moving target acquisition in desktop and touchscreen settings. However, these findings are not directly transferable to VR where targets and user input have complete freedom in three dimensions. This paper concentrates on motion-in-depth, that is, where a target predominantly exhibits approaching or receding movement as opposed to lateral motion across the user’s field of view. We report on two studies investigating how various factors including texture, shadow, alignment, moving speed and moving direction affect: 1) perception accuracy of 3D targets with motion-in-depth, and 2) user performance, which we define as the combination of movement time (MT) and error rate (ER), in a target acquisition task involving motion-in-depth. Our data reveal a number of empirical results that are distinct from the depth perception of static targets and the user performance of 1D/2D target acquisition. We found that MT and ER when acquiring targets with motion-in-depth have strong regularities as the data showed good fits with Jagacinski’s model for movement time estimation and a Ternary-Gaussian model for error rate prediction. We conclude with implications derived from this study for future designs.