标题： 贝叶斯回归分析与漂移扩散模型 显示英文标题

标题： Bayesian Regression Analysis with the Drift-Diffusion Model

摘要： 漂移扩散模型（DDM）在神经心理学研究中被广泛用于通过结合反应时间和受试者反应来理解决策过程。 已经开发了各种模型来估计DDM参数，其中一些采用了贝叶斯推断。 然而，在检查感兴趣表型与DDM参数之间的关联时，大多数研究采用两步方法：首先估计DDM参数，然后对估计值应用单独的统计模型。 尽管存在潜在偏差，这种做法仍然很常见，主要是由于研究人员对贝叶斯建模不熟悉。 为了解决这个问题，本教程介绍了拟合统一贝叶斯分层回归模型的实现和优势，该模型整合了试验级别的漂移扩散建模和DDM参数与其他变量之间的受试者级别回归。 本教程中开发并演示的R包RegDDM，促进了这种综合建模方法。 显示英文摘要

摘要： The Drift-Diffusion Model (DDM) is widely used in neuropsychological studies to understand the decision process by incorporating both reaction times and subjects' responses. Various models have been developed to estimate DDM parameters, with some employing Bayesian inference. However, when examining associations between phenotypes of interest and DDM parameters, most studies adopt a two-step approach: first estimating DDM parameters, then applying a separate statistical model to the estimated values. Despite the potential for bias, this practice remains common, primarily due to researchers' unfamiliarity with Bayesian modeling. To address this issue, this tutorial presents the implementations and advantages of fitting a unified Bayesian hierarchical regression model that integrates trial-level drift-diffusion modeling and subject-level regression between DDM parameters and other variables. The R package RegDDM, developed and demonstrated in this tutorial, facilitates this integrated modeling approach.