Modeling the Uncertainty in 2D Moving Target Selection

Understanding the selection uncertainty of moving targets is a fundamental research problem in HCI. However, the only few works in this domain mainly focus on selecting 1D moving targets with certain input devices, where the model generalizability has not been extensively investigated. In this paper, we propose a 2D Ternary-Gaussian model to describe the selection uncertainty manifested in endpoint distribution for moving target selection. We explore and compare two candidate methods to generalize the problem space from 1D to 2D tasks, and evaluate their performances with three input modalities including mouse, stylus, and finger touch. By applying the proposed model in assisting target selection, we achieved 56.7% improvement in selection speed and 78.8% improvement in pointing accuracy. In addition, we found that when predicting pointing errors, our model can fit the data of error rates with 0.94 R2.