Extending RT-MPTs to enable equal process times

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. •We extend the RT-MPT modelling approach to accommodate models with repeated processes.•We validate the new approach using simulation-based calibration.•We show the usefulness of the new approach using a dataset from recognition memory.