Research keyloggers are essential for cognitive studies of text production, yet most fail to capture the on-screen transformations performed by Input Method Editors (IMEs) for non-alphabetic scripts. To address this methodological gap, we present Hylog, a novel hybrid logging system that combines analytical keylogging with ecological text logging for a more complete and finer-grained analysis. Our modular, open-source system uses plug-ins for standard applications (Microsoft Word, Google Chrome) to capture both keyboard output and rendered text, which a hybridizer module then synchronizes into a dual trace. To validate the system's technical feasibility and demonstrate its analytical capabilities, we conducted a proof-of-concept study where two volunteers translated a text into simplified Chinese. Hylog successfully captured keypresses and temporal intervals between Latin letters, Chinese characters, and IME confirmations -- some measurements invisible to traditional keyloggers. The resulting data enable the formulation of new, testable hypotheses about the cognitive restrictions and affordances at different linguistic layers in IME-mediated typing. Our plug-in architecture enables extension to other IME systems and fosters more inclusive multilingual text-production research.

翻译：研究型键盘记录器对于文本生成的认知研究至关重要，然而大多数现有工具无法捕捉输入法编辑器（IME）为非字母文字执行的在屏转换。为填补这一方法学空白，我们提出了Hylog——一种新颖的混合记录系统，它将分析型键盘记录与生态文本记录相结合，以实现更完整且更细粒度的分析。我们的模块化开源系统通过标准应用程序（Microsoft Word、Google Chrome）的插件来捕获键盘输出与渲染文本，随后由混合器模块将二者同步为双重轨迹。为验证系统的技术可行性并展示其分析能力，我们开展了一项概念验证研究，由两名志愿者将一篇文本翻译为简体中文。Hylog成功捕获了拉丁字母、汉字与IME确认操作之间的按键事件及时间间隔——其中部分测量结果是传统键盘记录器无法观测的。所得数据能够为IME介导的输入过程中不同语言层级的认知限制与可供性提出新的可检验假设。我们的插件架构支持扩展至其他IME系统，有助于推动更具包容性的多语言文本生成研究。