2020
DOI: 10.1101/2020.11.20.392274
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Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience

Abstract: 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… Show more

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Cited by 12 publications
(22 citation statements)
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“…No such formal constraint exists for the dynamical form of the collapsing bound. This spans a family of DDM variants that is much broader than what is currently being used in the field (Fengler et al, 2020; Shinn et al, 2020). For example, this family includes decision models that trigger a decision when decision confidence reaches a bound (Lee and Daunizeau, 2020; Tajima et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
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“…No such formal constraint exists for the dynamical form of the collapsing bound. This spans a family of DDM variants that is much broader than what is currently being used in the field (Fengler et al, 2020; Shinn et al, 2020). For example, this family includes decision models that trigger a decision when decision confidence reaches a bound (Lee and Daunizeau, 2020; Tajima et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…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%
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“…Several authors have suggested using simulation-based methods to compute the firstpassage time distribution for complex diffusion models (e.g., Brandon & Sederberg, 2014;Fengler, Govindarajan, Chen, & Frank, 2020;Radev, Mertens, Voss, & Köthe, 2020;Wood, 2010).…”
Section: Introductionmentioning
confidence: 99%