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:证据积累模型(EAM)是用来分析反应时间和反应选择数据的重要认知模型类别。线性弹道积累模型(LBA)和扩散决定模型(DDM)是两种常见的EMM,使用巴伊西亚等级版本的现代应用。本文的第一种贡献是提出具有共变的EMs,我们称之为回归证据累积模型(RegEAMS),第二个贡献是制定有效准确和近似巴伊西亚方法来估计RegEAMs,包括模拟一致粒子大都会内部采样器和两种变异贝类近似方法。受限制的VB方法假定主题级参数的后端分布是独立的,但比常规VB系统要快得多。为复杂的 EAMs初始化模型(RegEAMs)采用VB方法可能非常具有挑战性,为VB方法提出了两种初始化方法。基于后端估计(MAP)的初始化方法在主题数量上可以缩放。新的估算方法通过模拟和模拟方法加以说明。新的估算方法通过实际编码加以执行。