Cited by 1,630Neural and Behavioral Psychology StudiesDecision-Making and Behavioral EconomicsVisual perception and processing mechanisms

Diffusion decision model: Current issues and history

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Ratcliff, Roger, Smith, Philip L., Brown, Scott D., McKoon, Gail·2016·Trends in Cognitive Sciences

Abstract

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 review, we relate the models to both earlier and more recent research in psychology.

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Crossref · 10.1016/j.tics.2016.01.007

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  • In the standard drift-diffusion model, a decision variable accumulates sensory evidence with stochastic noise and produces a decision at reaction time when it reaches either the upper or lower bound.

    “A DV in standard drift-diffusion model (DDM) (3–5, 7) (Fig. 4A) accumulates sensory evidence (drift), accompanied by stochastic internal or sensory noise (diffusion) that, when reaches either upper (correct trials) or lower (error trials) bound, produces a decision at RT.”
  • A canonical framework of perceptual decision-making considers the decision process as linear integration of evidence over time during sequential noisy sensory sampling until a decision bound is reached.

    “A canonical framework of perceptual decision-making (7) considers the decision process as the linear integration of evidence over time during sequential noisy sensory sampling until a decision bound is reached.”
  • The neural activity ramp during epoch 4 is consistent with a bounded integration mechanism of decision-making.

    “In what follows we argue the neural activity ramp during epoch 4 is consistent with a bounded integration mechanism of decision-making (3–5, 7).”
  • Internal decision variables are accumulated as ramping neural activity and transformed into a decision when a decision boundary is reached.

    “Here, close to the top of the cortical hierarchy, internal decision variables (DV) are accumulated in a form of ramping neural activity (3–6) that is transformed into a decision when a decision boundary is reached (7).”
  • The single latent variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound.

    “We show that this variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound.”

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Authors
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G.
Affiliations
  • The Ohio State University (US)
  • The University of Melbourne (AU)
  • University of Newcastle Australia (AU)
Corresponding author
Ratcliff, Roger
Venue
Trends in Cognitive Sciences
Publisher
Elsevier BV
Published
2016-04
Volume
20
Issue
4
Pages
260-281
ISSN
1364-6613 (ISSN-L)
OpenAlex
W2290798512
Cited by
1,630
References
216
Citations by year
2026: 186 · 2025: 209 · 2024: 181 · 2023: 206 · 2022: 188 · 2021: 197 · 2020: 142 · 2019: 137 · 2018: 98 · 2017: 70 · 2016: 13 · 2015: 1
FWCI
11.19
Type
journal article
Language
en
Open access
Locations
  • Trends in Cognitive Sciences (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • NOVA (University of Newcastle Australia) (submittedVersion)
Funders
  • National Institute on Aging (R01-AG041176)
  • Department of Education/Institute of Educational Sciences (R305A120189)
  • Australian Research Council (DP140102970, FT120100244)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
diffusion models, sequential sampling models, speeded decision making, evidence accumulation, neuronal populations, Optimality Theory, individual differences, psychometrics
MeSH terms
Cognition, Decision Making, Electroencephalography, Humans, Magnetic Resonance Imaging, Models, Psychological, Brain Waves

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