Additive models of interaction performance, such as the Keystroke-Level Model (KLM), are tools that allow designers to compare and optimize the performance of user interfaces by summing the predicted times for the atomic components of a specific interaction to predict the total time it would take to complete that interaction. There has been extensive work in creating such additive models for 2D interfaces, but this approach has rarely been explored for 3D user interfaces. We propose a KLM-style additive model, based on existing atomic task models in the literature, to predict task completion time for 3D interaction tasks. We performed two studies to evaluate the feasibility of this approach across multiple input modalities, with one study using a simple menu selection task and the other a more complex manipulation task. We found that several of the models from the literature predicted actual task performance with less than 20% error in both the menu selection and manipulation study. Overall, we found that additive models can predict both absolute and relative performance of input modalities with reasonable accuracy.

翻译：交互性能的加性模型（如击键层级模型KLM）是一种设计工具，通过累加特定交互原子组件的预测时间来预估完成该交互所需的总时长，从而帮助设计者比较和优化用户界面的性能。针对二维界面的此类加性模型已有大量研究，但该方法在三维用户界面中鲜有探索。我们基于文献中现有的原子任务模型，提出了一种KLM风格的加性模型，用于预测三维交互任务的完成时间。我们通过两项研究评估了该方法在多种输入模态下的可行性：一项研究采用简单的菜单选择任务，另一项采用更复杂的操控任务。研究发现，在菜单选择和操控研究中，文献中的多个模型对实际任务性能的预测误差均低于20%。总体而言，加性模型能够以合理精度预测输入模态的绝对性能与相对性能。