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Systematic parameter reviews in cognitive modeling: Towards a robust and cumulative characterization of psychological processes in the diffusion decision model

Citation

Tran, N-H and van Maanen, L and Heathcote, A and Matzke, D, Systematic parameter reviews in cognitive modeling: Towards a robust and cumulative characterization of psychological processes in the diffusion decision model, Frontiers in Psychology, 11 pp. 1-14. ISSN 1664-1078 (2021) [Refereed Article]


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Copyright Statement

© 2021. Tran, van Maanen, Heathcote and Matzke. This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

DOI: doi:10.3389/fpsyg.2020.608287

Abstract

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial-if not indispensable-information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee, 2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon, 2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

Item Details

Item Type:Refereed Article
Keywords:Bayesian inference, cognitive modeling, diffusion decision model, prior distributions, cumulative science
Research Division:Psychology
Research Group:Cognitive and computational psychology
Research Field:Decision making
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:146561
Year Published:2021
Web of Science® Times Cited:2
Deposited By:Psychology
Deposited On:2021-09-13
Last Modified:2021-10-13
Downloads:9 View Download Statistics

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