标题： 选择任务中反应时间的条件广义线性混合模型 显示英文标题

标题： Conditional GLMMs for reaction times in choice tasks

摘要： 本研究将两种用于建模选择任务中反应时间（RTs）的方法联系起来：(1) 一个具有单个障碍的简单扩散模型的第一个击中时间，代表导致响应的认知过程，以及 (2) 广义线性混合模型（GLMMs）。我们通过分析每个响应选项条件下的RT分布来实现这一点。由于某些扩散模型变体会产生逆高斯（IG）和伽马分布作为第一个击中时间，我们可以证明在RT模型中使用这些分布是合理的。相反，在GLMMs中使用IG和伽马分布使我们能够推断出潜在的认知过程。我们通过模拟来演示这一概念，并将其应用于之前发表的真实世界数据。最后，我们讨论了我们方法的范围和潜在扩展。 显示英文摘要

摘要： This study connects two methods for modeling reaction times (RTs) in choice tasks: (1) the first-hitting time of a simple diffusion model with a single barrier, representing the cognitive process leading to a response, and (2) Generalized Linear Mixed Models (GLMMs). We achieve this by analyzing RT distributions conditioned on each response alternative. Because certain diffusion model variants yield Inverse Gaussian (IG) and Gamma distributions for first-hitting times, we can justify using these distributions in RT models. Conversely, employing IG and Gamma distributions within GLMMs allows us to infer the underlying cognitive processes. We demonstrate this concept through simulations and apply it to previously published real-world data. Finally, we discuss the scope and potential extensions of our approach.