不知道运动角度的矩形目标鼠标指向出错率预测
Error-rate Prediction for Mouse-based Rectangular-target Pointing with no Knowledge of Movement Angles
摘要：
在矩形目标指向中，已知朝向目标的运动角度会影响出错率。然而，当设计者确定目标尺寸时，他们不会知道每个目标的光标接近方向的频率。因此，假设存在无偏的各种角度，我们推导出仅依赖于目标宽度和高度来预测出错率的模型。我们进行了两个众包实验：一个循环指向任务与预定义的运动角度和多方向指向任务。使用60%训练数据的混洗-分割交叉验证显示R^2 > 0.81，MAE <1.3%，RMSE <2.1%，这表明即使在设计人员新设置UI元素时预测未经测试的目标尺寸也具有良好的预测准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
