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Cited by 133Neural and Behavioral Psychology StudiesDecision-Making and Behavioral EconomicsVisual perception and processing mechanisms

A flexible framework for simulating and fitting generalized drift-diffusion models

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Shinn, Maxwell, Lam, Norman H, Murray, John D·2020·eLife
DOI 10.7554/elife.56938PMC PMC7462609

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Abstract

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

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  • A generalized drift-diffusion model including exponentially collapsing bounds and a leaky integrator produces a better fit with BIC of -705.

    “To accommodate this, we introduced additional model components [Generalized DDM (49)] including exponentially collapsing bounds (6) and a leaky integrator (30) that produce a better fit (BIC of −705).”

    From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [49]

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Authors
Shinn, M., Lam, N. H., & Murray, J. D.
Affiliations
Yale University (US)
Corresponding author
Shinn, Maxwell
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2020-08-04
Volume
9
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.56938
PMC
PMC7462609
OpenAlex
W3047413465
Cited by
133
References
98
Citations by year
2026: 19 · 2025: 30 · 2024: 25 · 2023: 23 · 2022: 20 · 2021: 11 · 2020: 5
FWCI
7.92
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute of Mental Health (R01MH112746)
  • Gruber Foundation
  • NSERC (PGSD2-502866-2017)
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
drift-diffusion model, decision models, Fokker-Planck equation, response time distribution, maximum likelihood fitting, perceptual decision making
MeSH terms
Data Analysis, Animals, Decision Making, Humans, Models, Neurological, Models, Psychological, Software

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