RT-MPTs: Process models for response-time distributions based on multinomial processing trees with applications to recognition memory

Multinomial processing tree models have been widely used for characterizing categorical responses in terms of a finite set of discrete latent states, and a number of processes arranged serially in a processing tree. We extend the scope of this model class by proposing a method for incorporating response times. This extension enables the estimation of the completion times of each process and the testing of alternative process orderings. In line with previous developments, the proposed method is hierarchical and implemented using Bayesian methods. We apply our method to the two-high-threshold model of recognition memory, using previously published data. The results provide interesting insights into the ordering of memory-retrieval and guessing processes and show that the model performs at least as well as established benchmarks such as the diffusion model.