Modeling Error Rates in Spatiotemporal Moving Target Selection

Abstract

When we try to acquire a moving target such as hitting a virtual tennis in a computer game, we must hit the target instantly when it flies over our hitting range. In other words, we have to acquire the target in spatial and temporal domains simultaneously. We call this type of task spatiotemporal moving target selection, which we find is common yet less studied in HCI. This paper presents a tentative model for predicting the error rates in spatiotemporal moving target selection. Our model integrates two latest models, the Ternary-Gaussian model and the Temporal Pointing model, to explain the influence of spatial and temporal constraints on pointing errors. In a 12-subject pointing experiment with a computer mouse, our model shows high fitting results with 0.904 R-2, We discuss future research directions on this topic and how it could potentially help the design in dynamical user interfaces.