Abstract The response-time extended multinomial processing tree (RT-MPT; Klauer and Kellen, 2018) model class and its implementation ( rtmpt ; Hartmann et al., in press) in the programming language R enable one to estimate process-completion times and encoding plus motor-execution times along with the process probabilities of traditional multinomial processing tree (MPT) models via an MCMC algorithm in a hierarchical Bayesian framework. This implementation is, however, restricted to RT-MPT models without process repetition in any of the model’s processing paths, implying that models such as the pair-clustering model (Batchelder and Riefer, 1980, 1986) cannot be fitted. Here, we develop a new MCMC algorithm that overcomes this restriction. Furthermore, we validate the algorithm, and demonstrate its usefulness on a dataset from recognition-memory research.

中文翻译：

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扩展 RT-MPT 以实现相同的处理时间

摘要 响应时间扩展多项式处理树（RT-MPT；Klauer 和 Kellen，2018）模型类及其在编程语言 R 中的实现（rtmpt；Hartmann 等人，出版中）使人们能够估计过程完成时间和编码加上运动执行时间以及传统多项式处理树 (MPT) 模型的过程概率，通过分层贝叶斯框架中的 MCMC 算法。然而，这种实现仅限于在模型的任何处理路径中没有过程重复的 RT-MPT 模型，这意味着不能拟合诸如成对聚类模型 (Batchelder and Riefer, 1980, 1986) 之类的模型。在这里，我们开发了一种新的 MCMC 算法来克服这个限制。此外，我们验证了算法，