Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions

By Birte U. Forstmann, Roger Ratcliff, Eric-Jan Wagenmakers. Annual Review of Psychology, 2015

528 citations recorded by the source corpus.

Sequential sampling models assume that people make speeded decisions by gradually accumulating noisy information until a threshold of evidence is reached. In cognitive science, one such model—the diffusion decision model—is now regularly used to decompose task performance into underlying processes such as the quality of information processing, response caution, and a priori bias. In the cognitive neurosciences, the diffusion decision model has recently been adopted as a quantitative tool to study the neural basis of decision making under time pressure. We present a selective overview of several recent applications and extensions of the diffusion decision model in the cognitive neurosciences.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f4fe95be65f253071cc1445866f336b9ca96559f

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In Cognitive Neuroscience

1. Eric-Jan Wagenmakers
2. M. Janczyk
3. Heinrich R. Liesefeld
4. Birte U. Forstmann

Citations in the corpus

• Bayes factor design analysis: Planning for compelling evidence (2016, background): “This principle of “accumulation of evidence” is also central to optimal models for human perceptual decision making (e.g., random walk models, diffusion models; e.g., Bogacz et al. 2006; Forstmann et al. 2016).”