Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability

By Roger Ratcliff, Francis Tuerlinckx. Psychonomic Bulletin & Review, 2002

841 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e6137dcc74821bcd74ca09c7c74a774867a5d898

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Citations in the corpus

Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG (2009, methodology): “From these, accuracy values and quantile RTs for the largest half of the drift rates and the smallest half of the drift rates were obtained.”
The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. (2006, methodology): “We estimated the parameters of the extended DDM using a combination of the weighted least squares fitting method (Ratcliff & Tuerlinckx, 2002) and the method of Bogacz & Cohen (2004).”
Fast-dm: A free program for efficient diffusion model analysis (2007, methodology): “A more exhaustive description of the interpretation of the parameters in terms of cognitive processes might be found elsewhere (e.g., Ratcliff & Tuerlinckx, 2002; Voss, et al., 2004).”
How cognitive modeling can benefit from hierarchical Bayesian models. (2011, methodology): “Ratcliff and Tuerlinckx (2002) pioneered a mixture approach in which a monolithic account of responding, based on the Ratcliff Diffusion model, was supplemented with the possibility of contaminant trials.”
Symbols and quantities in parietal cortex: elements of a mathematical theory of number representation and manipulation (2007, background): “The idea was imported into psychology by Stone (1960) and Laming (1968), then extended by Link (1975, 1990, 1992), Ratcliff (1988), Ratcliff and Rouder, 1998), and many others (e.”
Fast and accurate calculations for first-passage times in Wiener diffusion models (2009, methodology): “…1, w).99 Accordingly, we now turn our attention to the calculations involved in this case.100 When calculating WFPT densities, a typical approach is to make use of this “large time”101 expansion (e.g., Ratcliff 1978; Luce 1986; Ratcliff & Tuerlinckx 2002; Ratcliff & Smith102 2004; Tuerlinckx 2004).”
The speed-accuracy tradeoff: history, physiology, methodology, and behavior (2014, methodology): “Combining aspects of both the SATF and CAF is the quantile probability plot (Audley and Pike, 1965; see also Ratcliff and Tuerlinckx, 2002).”
A diffusion model account of the lexical decision task. (2004, methodology): “In research on fitting the diffusion model to data with the chi-square method (Ratcliff & Tuerlinckx, 2002), it was found that parameter values could not be recovered accurately when there were enough long or short outlier RTs in the data to seriously affect the quantile RTs.”
Use of Response Time for Measuring Cognitive Ability (2016, methodology): “The performance of the programs have been compared using simulations [84,85].”
How to measure post-error slowing: A confound and a simple solution (2012, background): “7 Another attempt to formally describe how participantsadjusttheirbehaviortomeetdifferenttaskconstraints is made by Simen, Cohen, and Holmes (2006), who propose a neurologically plausible model of how participants’ estimates of reward rates are translated to a dynamic setting of response thresholds.”
An introduction to good practices in cognitive modeling (2015, methodology): “Models of simple perceptual decision making, particularly Ratcliff’s diffusion model ([5, 21, 10]), have around six basic parameters.”
What cognitive processes drive response biases? A diffusion model analysis (2011, methodology): “The method we used to fit the diffusion model was the chi-square method, which works as follows (cf. Ratcliff & Tuerlinckx, 2002).”
Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy (2009, background): “…variability in nondecision time, called st, was introduced to account for the relatively gradual rise in the leading edge of the RT distribution (Ratcliff & Tuerlinckx, 2002); uniformly distributed trial-to-trial variability in starting point, called sz, was introduced to account for error…”
Striatum and pre-SMA facilitate decision-making under time pressure (2008, background): “2C, the x axis shows the response time for the quantile estimates, and the y axis shows the associated proportion of data, which makes the graphs into defective cumulative distribution functions, which are commonly used in response time analysis (22).”
A Tutorial on Bayesian Nonparametric Models (2011, methodology): “…over n follows from assuming that each observation is conditionally independent given its latent cluster assignment and the cluster 2A number of papers in the psychology literature have adopted a mixture model approach to modeling RTs (e.g., Ratcliff and Tuerlinckx, 2002; Wagenmakers et al., 2008).”