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Estimating across-trial variability parameters of the Diffusion Decision Model: expert advice and recommendations

Citation

Boehm, U and Annis, J and Frank, MJ and Hawkins, GE and Heathcote, A and Kellen, D and Krypotos, A-M and Lerche, V and Logan, GD and Palmeri, TJ and van Ravenzwaaij, D and Servant, M and Singmann, H and Starns, JJ and Voss, A and Wiecki, TV and Matzke, D and Wagenmakers, E-J, Estimating across-trial variability parameters of the Diffusion Decision Model: expert advice and recommendations, Journal of Mathematical Psychology, 87 pp. 46-75. ISSN 0022-2496 (2018) [Refereed Article]

Copyright Statement

© 2018 Elsevier Inc. All rights reserved

DOI: doi:10.1016/j.jmp.2018.09.004

Abstract

For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s across-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters.

Item Details

Item Type:Refereed Article
Keywords:Diffusion Decision Model, across-trial variability parameters, parameter estimation
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Decision Making
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Psychology and Cognitive Sciences
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:129244
Year Published:2018
Web of Science® Times Cited:14
Deposited By:Psychology
Deposited On:2018-11-19
Last Modified:2019-04-15
Downloads:0

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