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

• Published in: Journal of mathematical psychology Vol. 82; pp. 111 - 130
• Main Authors: Klauer, Karl Christoph, Kellen, David
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.02.2018
• Subjects: Hierarchical models; Multinomial models; Response times; Response times; Hierarchical models; Multinomial models
• ISSN: 0022-2496; 1096-0880
• DOI: 10.1016/j.jmp.2017.12.003

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. •Multinomial processing-tree models are extended to fit response times.•RT-MPTs account for accuracy and latency data and individual differences therein.•RT-MPTs estimate process-completion and response-execution times.•RT-MPTs provide principled competitors to traditional diffusion models.•RT-MPTs allow one to test assumptions about processing architecture.