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 with diffusion kernel overcome shortcomings of previous RT-MPT models.

We propose an extension of the widely used class of multinomial processing tree models by incorporating response times via diffusion-model kernels. Multinomial processing tree models are models of categorical data in terms of a number of cognitive and guessing processes estimating the probabilities with which each process outcome occurs. The new method allows one to estimate completion times of each process along with outcome probability and thereby provides process-oriented accounts of accuracy and latency data in all domains in which multinomial processing tree models have been applied. Furthermore, the new models are implemented hierarchically so that individual differences are explicitly accounted for and do not bias the population-level estimates. The new approach overcomes a number of shortcomings of previous extensions of multinomial models to incorporate response times. We evaluate the new method’s performance via a recovery study and simulation-based calibration. The method allows one to test hypotheses about processing architecture, and it provides an extension of traditional diffusion model analyses where multinomial models have been proposed for the modeled paradigm. We illustrate these and other benefits of the new model class using five existing data sets from recognition memory.

Multinomial models

Response times

Diffusion models

Hierarchical models

© 2024 The Author(s). Published by Elsevier Inc.