Literature DB >> 35318590

Problems when fixing the response bias parameter z in drift diffusion analysis : A Commentary on Stafford et al. (2020).

Rainer W Alexandrowicz1, Bartosz Gula2.   

Abstract

In a simulation study, Stafford et al. (Behavior Research Methods, 52, 2142-2155, 2020) explored the effect of sample size on detecting group differences in ability in the presence of speed-accuracy trade-offs using the Drift Diffusion Model (DDM) and introduced an online tool to perform a power analysis. They found that the DDM approach was superior to analyzing the observed response times and response accuracies alone. In their simulation, they applied the EZ method to estimate the model parameters. In this article, we demonstrate that the EZ method, which cannot estimate the response bias parameter of the DDM, causes severe estimation bias for all parameters if the true response bias is not 0.5. Moreover, the bias patterns differ between EZ and the equivalent maximum likelihood estimation with z fixed at 0.5. This should be taken into consideration when using the otherwise excellent power analysis tool for experimental designs, in which z≠ 0.5 cannot be ruled out or even stipulate it.
© 2022. The Author(s).

Entities:  

Keywords:  Drift diffusion modeling; EZ; Estimation bias; Speed–accuracy trade-off

Year:  2022        PMID: 35318590     DOI: 10.3758/s13428-021-01786-0

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  11 in total

1.  Fast-dm: a free program for efficient diffusion model analysis.

Authors:  Andreas Voss; Jochen Voss
Journal:  Behav Res Methods       Date:  2007-11

2.  Combining speed and accuracy to control for speed-accuracy trade-offs(?).

Authors:  Heinrich René Liesefeld; Markus Janczyk
Journal:  Behav Res Methods       Date:  2019-02

3.  A comparison of methods to combine speed and accuracy measures of performance: A rejoinder on the binning procedure.

Authors:  André Vandierendonck
Journal:  Behav Res Methods       Date:  2017-04

4.  Speed-accuracy manipulations and diffusion modeling: Lack of discriminant validity of the manipulation or of the parameter estimates?

Authors:  Veronika Lerche; Andreas Voss
Journal:  Behav Res Methods       Date:  2018-12

5.  Diffusion model drift rates can be influenced by decision processes: an analysis of the strength-based mirror effect.

Authors:  Jeffrey J Starns; Roger Ratcliff; Corey N White
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2012-04-30       Impact factor: 3.051

6.  How prior information and police experience impact decisions to shoot.

Authors:  David J Johnson; Joseph Cesario; Timothy J Pleskac
Journal:  J Pers Soc Psychol       Date:  2018-10

7.  The EZ diffusion model provides a powerful test of simple empirical effects.

Authors:  Don van Ravenzwaaij; Chris Donkin; Joachim Vandekerckhove
Journal:  Psychon Bull Rev       Date:  2017-04

8.  Empirical validation of the diffusion model for recognition memory and a comparison of parameter-estimation methods.

Authors:  Nina R Arnold; Arndt Bröder; Ute J Bayen
Journal:  Psychol Res       Date:  2014-10-04

9.  Quantifying the benefits of using decision models with response time and accuracy data.

Authors:  Tom Stafford; Angelo Pirrone; Mike Croucher; Anna Krystalli
Journal:  Behav Res Methods       Date:  2020-03-30
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