Diffusion Decision Model: Current Issues and History
By Roger Ratcliff, Philip L. Smith, Scott D. Brown, Gail McKoon. Trends in Cognitive Sciences, 2016
1,384 citations recorded by the source corpus.
There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this article, we relate the models to both earlier and more recent research in psychology.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, General Decision Sciences
View Paper: https://www.semanticscholar.org/paper/c56db2df697dbdc4523c7ba4373073127d67e75d
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 Experimental and Cognitive Psychology
Citations in the corpus
- Use of Response Time for Measuring Cognitive Ability (2016, background): “The diffusion model [80] is a popular model for representing cognitive and neural processes underlying simple two-choice decision making (see [81], Box 2 for a comprehensive summary of the domains of its applications).”
- Large-scale analysis of test–retest reliabilities of self-regulation measures (2019, methodology): “We compare the reliability of raw DVs (response times and accuracies) to parameters of the DDM, a well-established model that addresses speed–accuracy trade-offs and offers interpretable latent variables (34, 35).”
- Oscillatory signatures of monitoring and anticipatory strategies for probabilistic vs deterministic cues (2025, unknown): “…(i.e., believers ) versus minimal decisional modulations (i.e., empiricists ) based on priors, we fit participants’ decision using the drift diffusion model (DDM), the most widely used computational model of two-alternative decision-making tasks ( Ratcliff et al., 2016; Wiecki et al., 2013).”