RT-MPTs: process models for response-time distributions with diffusion-model kernels
Abstract: 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
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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Journal of mathematical psychology. - 120-121 (2024) , 102857, ISSN: 0022-2496
- Classification
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Psychologie
- Event
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Veröffentlichung
- (where)
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Freiburg
- (who)
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Universität
- (when)
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2024
- Creator
- DOI
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10.1016/j.jmp.2024.102857
- URN
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urn:nbn:de:bsz:25-freidok-2514698
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 14.08.2025, 10:51 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
Time of origin
- 2024
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