Journal ArticleDOI
Retest reliability of the parameters of the Ratcliff diffusion model
Veronika Lerche,Andreas Voss +1 more
TLDR
The reliability of the main parameters of the Ratcliff Diffusion Model (in particular of the speed of information accumulation and the threshold separation with rs > 0.70 for all three tasks) is satisfying and the influence of the number of trials on the retest reliability is analyzed.Abstract:
In the recent years, there is a growing interest to use the Ratcliff Diffusion Model (1978) for diagnostic purposes as the parameters of the model capture interindividual differences in specific cognitive processes. The parameters are estimated using reaction time data from binary classification tasks. For a potential diagnostic application of parameter values sufficient reliability is a necessary precondition. In two studies, each with two sessions separated by 1 week, the retest reliability of the diffusion model parameters was assessed. In Study 1, 105 participants completed a lexical decision task and a recognition memory task. In Study 2, 128 participants worked on an associative priming task. Results show that the reliability of the main parameters of the Ratcliff Diffusion Model (in particular of the speed of information accumulation and the threshold separation with rs > 0.70 for all three tasks) is satisfying. Besides, we analyzed the influence of the number of trials on the retest reliability using different estimation methods (Kolmogorov-Smirnov, Maximum Likelihood, Chi-square and EZ) and both empirical and simulated datasets.read more
Citations
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Journal ArticleDOI
The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences.
TL;DR: The very reason such tasks produce robust and easily replicable experimental effects – low between-participant variability – makes their use as correlational tools problematic, and it is demonstrated that taking reliability estimates into account has the potential to qualitatively change theoretical conclusions.
Journal ArticleDOI
Model Complexity in Diffusion Modeling: Benefits of Making the Model More Parsimonious
Veronika Lerche,Andreas Voss +1 more
TL;DR: The results suggest that less complex models (fixing intertrial variabilities of the drift rate and the starting point at zero) can improve the estimation of the psychologically most interesting parameters.
Journal ArticleDOI
How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria
TL;DR: In a series of simulation studies, the efficiency and robustness of parameter recovery were compared for models differing in complexity (i.e., in number of free parameters) and trial numbers (ranging from 24 to 5,000) using three different optimization criteria (maximum likelihood, Kolmogorov-Smirnov, and chi-square) that are all implemented in the latest version of fast-dm as mentioned in this paper.
How many trials are required for parameter estimation in diffusion modeling
TL;DR: The results revealed that maximum likelihood is superior for uncontaminated data, but in the presence of fast contaminants, Kolmogorov–Smirnov outperforms the other two methods and under certain conditions even small numbers of trials are sufficient for robust parameter estimation.
Journal ArticleDOI
Estimating across-trial variability parameters of the Diffusion Decision Model: expert advice and recommendations
Udo Böhm,Jeffrey Annis,Michael J. Frank,Guy E. Hawkins,Andrew Heathcote,David Kellen,Angelos-Miltiadis Krypotos,Veronika Lerche,Gordon D. Logan,Thomas J. Palmeri,Don van Ravenzwaaij,Mathieu Servant,Henrik Singmann,Jeffrey J. Starns,Andreas Voss,Thomas V. Wiecki,Dora Matzke,Eric-Jan Wagenmakers +17 more
TL;DR: This 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.
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