The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

By Roger Ratcliff, Gail McKoon. Neural Computation, 2008

2,903 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Economics and Econometrics, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/1dccc2d2207d25b4d7bde33d74f33b9ec97f0eaf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Economics and Econometrics

In General Decision Sciences

Citations in the corpus

A taxonomy of errors in multiple-target visual search (2013, background): “…like that seen here; for example, models like the leaky, competing accumulator model (Usher & McClelland, 2001) and the diffusion decision model (Ratcliff & McKoon, 2007), posit that when an observer fixates an item to make a decision about whether it is a target or not, perceptual evidence is…”
Neural computations associated with goal-directed choice. (2010, background): “The process terminates when the relative value action becomes sufficiently biased towards one of the choices [48,51–55].”
Relative visual saliency differences induce sizable bias in consumer choice (2012, background): “In previous work (Krajbich et al., 2010) we have shown that these types of choices seem to be made through a temporal integration model that looks like an attention modulated version of the Drift-Diffusion-Model (Ratcliff & Smith, 2004; Ratcliff & McKoon, 2008).”
Speed, Accuracy, and the Optimal Timing of Choices (2018, methodology): “(For recent reviews see Ratcliff and McKoon (2008) and Shadlen et al. (2006).) DDM-style models have also been applied to choice experiments, where subjects are choosing from a set of consumption goods presented to them: Roe, Busemeyer and Townsend (2001), Clithero and Rangel (2013); Krajbich, Armel”
How cognitive modeling can benefit from hierarchical Bayesian models. (2011, methodology): “As a final example, the Ratcliff diffusion model (e.g., Ratcliff & McKoon, 2008) provides a mapping from 1 Throughout, we use a graphical model formalism to characterize different hierarchical modeling structures.”
The Cognitive Neuroscience of Metacognition (2014, background): “high regardless of the degree of confidence [54, 55].”
Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy (2009, methodology): “…paper is to provide a selective overview of the recent practical developments in RT analysis using the Ratcliff diffusion model (for a recent, largely complementary review see Ratcliff & McKoon, 2008; for authoritative reviews on earlier developments see e.g., Luce, 1986; Townsend & Ashby, 1983).”
Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making. (2012, background): “The position of the particle in the DDM is generally not interpreted as encoding a posterior probability [but see [35 ,37,41,43]], but rather as providing a purely mechanistic account of the decision-making process [for example [44]].”
What cognitive processes drive response biases? A diffusion model analysis (2011, methodology): “In the diffusion model, there are two ways to model response bias (Ratcliff, 1985; Ratcliff et al., 1999; Ratcliff & McKoon, 2008).”
How to measure post-error slowing: A confound and a simple solution (2012, methodology): “In two simulation studies we used the diffusion model (Ratcliff, 1978; Ratcliff & McKoon, 2008; Wagenmakers, 2009) to make the scenarios described above more concrete.”
Diffusion Decision Model: Current Issues and History (2016, methodology): “Concise introductions to this model, which we will refer to as ‘the diffusion model’ in this review, are available elsewhere [7], as are more general comparative studies of the large class of sequential sampling models (Figure 1).”