A comparison of sequential sampling models for two-choice reaction time.

By Roger Ratcliff, Philip L. Smith. Psychology Review, 2004

1,221 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/74844df8c320fba315cef9c7cefd0ab2a5b2dbb5

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In Cognitive Neuroscience

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Citations in the corpus

Diffusion Decision Model: Current Issues and History (2016, methodology): “The assumption that model parameters vary from trial to trial is made by most current models that successfully account for experimental data in psychological applications [1,40].”
A probabilistic account of exemplar and category generation. (2013, methodology): “Sampling has also been used to account for numerous results from the judgment and decision-making literature (Ratcliff & Smith, 2004; Stewart, Chater, & Brown, 2006; Tversky & Kahneman, 1974; Vul & Pashler, 2008).”
Swarm cognition in honey bees (2007, background): “Such tests have received significant attention in mathematical and cognitive psychology where diffusion (Ratcliff 1978), accumulator (Usher and McClelland 2001), and other mathematical models (Busemeyer and Townsend 1993) have been developed to explain the results of reaction-time tests for humans a”
The neural basis of decision making. (2007, methodology): “These models mirror the mathematical description of a random 1998, Ratcliff & Smith 2004, Smith 2000 Smith & Ratcliff 2004): The accumulation of noisy evidence creates a virtual trajectory equivalent to the dancing movements of a tiny particle in Brownian motion ( Figure 2 ). the neurobiology of…”
A dual-stage two-phase model of selective attention. (2010, methodology): “The formulas for the probabilities p(A) and p(B) and for the corresponding expectation of the FPT, E(T A), given Response A, and E(T B), given Response B, can be found elsewhere (e.g., Ashby, 2000; Ratcliff, 1978; Ratcliff & Smith, 2004).”
Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. (2010, methodology): “Ratcliff and colleagues have shown that in order to simultaneously account for this pattern of results DSD requires trial-by-trial variability in the process parameters (Ratcliff & Rouder, 1998; Ratcliff & Smith, 2004; Ratcliff et al., 1999).”
Attention and choice: a review on eye movements in decision making. (2013, methodology): “Earlier versions of the drift diffusion model (Ratcliff & Smith, 2004) were concerned with perceptual discrimination; the aDDM extended this modus operandi to decision making (see Rangel, 2009 for an overview of both models).”
The simplest complete model of choice response time: linear ballistic accumulation. (2008, methodology): “One of the reasons for the success of (Ratcliff's, 1978, 2002; Ratcliff & Smith, 2004) diffusion model was its welldeveloped analytic solutions for binary choices.”
Observing the Observer (II): Deciding When to Decide (2010, background): “One might think that there is an irreconcilable difference between this deterministic scheme and stochastic diffusion models of binary decisions ([24]; see also [25], for a related Bayesian treatment).”
One and Done? Optimal Decisions From Very Few Samples (2014, methodology): “Sequential sampling models (Ratcliff & Smith, 2004) based on the SPRT (Wald, 1947), drift diffusion (Gold & Shadlen, 2000; Ratcliff, 1978), or accumulators (Brown & Heathcote, 2008; Vickers, 1979) have been applied not only to sampling data from the environment but also to more internal processes,…”
The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure (2010, methodology): “Our results show that a popular seven-parameter version of the DDM (Ratcliff & McKoon , 2008; Ratcliff & Rouder, 1998; Ratcliff & Smith, 2004; Ratcliff, et al., 1999) can account for the data with high-accuracy in both the high and low time pressure conditions .”
A diffusion model account of the lexical decision task. (2004, methodology): “These are summarized in the diffusion model into one parameter, the nondecision component of RT with mean Ter (which is not shown in Figure 2). Recently, Ratcliff and Tuerlinckx (2002) investigated whether the diffusion model should include variability in the nondecision component of RT. In the cour”