Error-rate Prediction for Mouse-based Rectangular-target Pointing with no Knowledge of Movement Angles

Abstract

In rectangular-target pointing, movement angles towards targets are known to affect error rates. When designers determine target sizes, however, they would not know the frequencies of cursor-approaching directions for each target. Thus, assuming that there are unbiasedly various angles, we derived models to predict error rates depending only on the target width and height. We conducted two crowdsourced experiments: a cyclic pointing task with a predefined movement angle and a multi-directional pointing task. The shuffle-split cross-validation with 60% training data showed R^2 > 0.81, MAE < 1.3%, and RMSE < 2.1%, suggesting good prediction accuracy even for predicting untested target sizes when designers newly set UI elements.