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

Shinn, Maxwell; Lam, Norman H; Murray, John D.

doi:10.7554/elife.56938

Cited by 102 publications

(106 citation statements)

References 94 publications

(170 reference statements)

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“…Other approaches exist for estimating generalized diffusion models. A recent example, not discussed thus far, is the pyDDM Python toolbox ( Shinn et al, 2020 ), which allows maximum like-lihood estimation of generalized drift diffusion models (GDDM). The underlying solver is based on the Fokker-Planck equations, which allow access to approximate likelihoods (where the degree of approximation is traded off with computation time / discretization granularity) for a flexible class of diffusion-based models, notably allowing arbitrary evidence trajectories, starting point and non-decision time distributions.…”

Section: Discussionmentioning

confidence: 99%

Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience

Fengler

Frank

Govindarajan

et al. 2020

Preprint

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In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.

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Section: Discussionmentioning

confidence: 99%

Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience

Fengler

Frank

Govindarajan

et al. 2020

Preprint

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“…Higher-order moments can also be derived from efficient semi-analytical solutions to the issue of deriving the joint choice/RT distribution ( Navarro and Fuss, 2009 ). However, more complex variants of the DDM (including, e.g., collapsing bounds) are much more difficult to simulate, and require either sampling schemes or numerical solvers of the underlying Fokker-Planck equation ( Fengler et al, 2020 ; Shinn et al, 2020 ).…”

Section: Model Formulation and Impact Of Ddm Parametersmentioning

confidence: 99%

“…We note that, under such generalized DDM variant, no analytical solution is available to derive RT moments. Applying the method of moments or the method of trial means to such generalized DDM variant thus involves either sampling schemes or numerical solvers for the underlying Fokker-Planck equation ( Shinn et al, 2020 ). However, the computational cost of deriving trial-by-estimates of RT moments precludes routine data analysis using these methods, which is why most model-based studies are currently restricted to the vanilla DDM ( Fengler et al, 2020 ).…”

Section: Parameter Recovery Analysismentioning

confidence: 99%

“…Although close to optimal from a statistical perspective, they suffer from a challenging computational bottleneck that lies in the trial-by-trial derivation of RT first-order moments. This is why they are typically constrained to simple DDM variants, for which analytical solutions exist ( Navarro and Fuss, 2009 ; Srivastava et al, 2016 ; Fengler et al, 2020 ; Shinn et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

Feltgen

Daunizeau

2021

Front. Artif. Intell.

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Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a “self-consistency” equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model “overcomplete,” which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this “overcomplete” approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

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“…There are packages or toolboxes that implement model fitting for most regression analyses (Seabold & Perktold, 2010) and standard models of behavior, such as the Drift Diffusion Model (Shinn, Lam, & Murray, 2020;Wiecki, Sofer, & Frank, 2013) or Reinforcement Learning Models (e.g. (Daunizeau, Adam, & Rigoux, 2014) ).…”

Section: Model Fittingmentioning

confidence: 99%

A practical guide for studying human behavior in the lab

Barbosa¹,

Stein²,

Summerfield³

et al. 2021

Preprint

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In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.

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

Cited by 102 publications

References 94 publications

Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience

Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

A practical guide for studying human behavior in the lab

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