Intelligent text entry (ITE) methods, such as word suggestions, are widely used in mobile typing, yet improving ITE systems is challenging because the cognitive mechanisms behind suggestion use remain poorly understood, and evaluating new systems often requires long-term user studies to account for behavioral adaptation. We present WSTypist, a reinforcement learning-based model that simulates how typists integrate word suggestions into typing. It builds on recent hierarchical control models of typing, but focuses on the cognitive mechanisms that underlie the high-level decision-making for effectively integrating word suggestions into manual typing: assessing efficiency gains, considering orthographic uncertainties, and including personal reliance on AI support. Our evaluations show that WSTypist simulates diverse human-like suggestion-use strategies, reproduces individual differences, and generalizes across different systems. Importantly, we demonstrate on four design cases how computational rationality models can be used to inform what-if analyses during the design process, by simulating how users might adapt to changes in the UI or in the algorithmic support, reducing the need for long-term user studies.

翻译：智能文本输入方法（如词语建议）在移动端输入中广泛应用，然而改进智能文本输入系统具有挑战性，因为建议使用背后的认知机制仍不甚明了，且评估新系统通常需要长期用户研究以考虑行为适应。我们提出了WSTypist，一种基于强化学习的模型，用于模拟打字者如何将词语建议整合到打字过程中。该模型建立在近期分层次控制的打字模型基础上，但重点关注有效将词语建议整合到手动打字中的高层决策所依赖的认知机制：评估效率增益、考虑拼写不确定性以及纳入个人对AI支持的依赖程度。我们的评估表明，WSTypist能够模拟多样化、类人的建议使用策略，重现个体差异，并在不同系统间实现泛化。重要的是，我们通过四个设计案例展示了计算理性模型如何在设计过程中用于指导假设分析，通过模拟用户可能如何适应UI或算法支持的变化，从而减少对长期用户研究的需求。