Evidence accumulation models (EAMs) are an important class of cognitive models used to analyse both response time and response choice data. The linear ballistic accumulation model (LBA) and the diffusion decision model (DDM) are two common EAMs, with modern applications employing hierarchical Bayesian versions. The first contribution of the paper is to propose EAMs having covariates, which we call Regression Evidence Accumulation Models (RegEAMs). The second contribution is to develop efficient exact and approximate Bayesian methods for estimating RegEAMs, including a simulation consistent particle Metropolis-within-Gibbs sampler and two variational Bayes approximate methods. The constrained VB method assumes that the posterior distribution of the subject level parameters are independent, but it is much faster than the regular VB for a dataset with many subjects. Initialising the VB method for complex EAMs can be very challenging, and two initialisation methods for the VB method are proposed. The initialisation method based on maximum a posteriori estimation (MAP) is shown to be scalable in the number of subjects. The new estimation methods are illustrated by applying them to simulated and real data, and through pseudo code. The code implementing the methods is freely available.

翻译：累积证据模型（EAMs）是一类重要的认知模型，用于分析反应时间和反应选择数据。线性弹道累积模型（LBA）和扩散决策模型（DDM）是两种常见的EAMs，其现代应用采用分层贝叶斯版本。本文的第一个贡献是提出带有协变量的EAMs，我们称之为回归累积证据模型（RegEAMs）。第二个贡献是开发了用于估计RegEAMs的高效精确和近似贝叶斯方法，包括一种模拟一致性粒子Metropolis-within-Gibbs采样器和两种变分贝叶斯近似方法。约束VB方法假设受试者水平参数的后验分布是独立的，但对于含大量受试者的数据集，其速度远快于常规VB。为复杂EAMs初始化VB方法极具挑战性，本文提出了两种针对VB方法的初始化方案。基于最大后验估计（MAP）的初始化方法被证明在受试者数量上具有可扩展性。通过将新估计方法应用于模拟数据和真实数据，并结合伪代码进行说明。实现这些方法的代码已公开可获取。