RT Journal Article
SR Electronic
T1 Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.20.392274
DO 10.1101/2020.11.20.392274
A1 Alexander Fengler
A1 Lakshmi N. Govindarajan
A1 Tony Chen
A1 Michael J. Frank
YR 2020
UL http://biorxiv.org/content/early/2020/11/22/2020.11.20.392274.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.