Department of Psychology, University of Tübingen, Schleichstr. 4, 72076, Tübingen, Germany.
Department of Statistics, Faculty of Economics and Statistics, Universität Innsbruck, Universitätsstr. 15, 6020, Innsbruck, Austria.
Behav Res Methods. 2018 Jun;50(3):1217-1233. doi: 10.3758/s13428-017-0937-z.
In multinomial processing tree (MPT) models, individual differences between the participants in a study can lead to heterogeneity of the model parameters. While subject covariates may explain these differences, it is often unknown in advance how the parameters depend on the available covariates, that is, which variables play a role at all, interact, or have a nonlinear influence, etc. Therefore, a new approach for capturing parameter heterogeneity in MPT models is proposed based on the machine learning method MOB for model-based recursive partitioning. This procedure recursively partitions the covariate space, leading to an MPT tree with subgroups that are directly interpretable in terms of effects and interactions of the covariates. The pros and cons of MPT trees as a means of analyzing the effects of covariates in MPT model parameters are discussed based on simulation experiments as well as on two empirical applications from memory research. Software that implements MPT trees is provided via the mpttree function in the psychotree package in R.
在多项处理树 (MPT) 模型中，研究参与者之间的个体差异可能导致模型参数的异质性。虽然可以用受试者协变量来解释这些差异，但通常事先并不知道参数是如何依赖于可用协变量的，即哪些变量起作用、相互作用或具有非线性影响等。因此，基于基于模型的递归分区的机器学习方法 MOB，提出了一种捕获 MPT 模型中参数异质性的新方法。该过程递归地划分协变量空间，从而得到一个 MPT 树，其中的子组可以根据协变量的效应和相互作用进行直接解释。基于模拟实验以及记忆研究中的两个实证应用，讨论了 MPT 树作为分析 MPT 模型参数中协变量效应的一种手段的优缺点。R 中的 psychotree 包中的 mpttree 函数提供了实现 MPT 树的软件。
