ArticleeLife2021
Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences.PLoS computational biology · 2026Article
- The perils of omitting omissions when modeling evidence accumulation.PLoS computational biology · 2026Article
- Control adjustment costs limit goal flexibility: Empirical evidence and a computational account.Psychological review · 2026Article
- cpm: A python library for theory-driven modelling in computational psychiatry.PLoS computational biology · 2026Article
- HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neurocognitive Modeling.bioRxiv : the preprint server for biology · 2026Article
- Efficient Inference in First Passage Time Models.Statistics and computing · 2026Article
- Quantifying uncertainty in drift diffusion models of decision making under temporal dependence and parameter variability.bioRxiv : the preprint server for biology · 2026Article
- Simulation-based inference captures non-Markovian effects as exemplified in protein production kinetics through cell division.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A novel approach-avoidance task to study decision making under outcome uncertainty.Psychonomic bulletin & review · 2026Article
- Bayesian hierarchical cognitive modeling with the EMC2 package.Behavior research methods · 2026Article
- The late positive component in recognition memory is linked to mnemonic evidence accumulation.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.Statistics and computing · 2026Article
- Learning expectations shape cognitive control allocation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Slower transitions between control states lead to reductions in cognitive flexibility over the lifespan.bioRxiv : the preprint server for biology · 2025Article
- Parameter estimation of hyper-spherical diffusion models with a time-dependent threshold: An integral equation method.Behavior research methods · 2025Article
- Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice.Behavior research methods · 2025Article
- Article
- Preparing to act follows Bayesian inference rules.iScience · 2025Article
- Heightened familiarity drives the negative retrieval bias in depression: Evidence from the PRISM task.Computational brain & behavior · 2025Article
- Computational Phenotyping of Effort-Based Decision Making in Unmedicated Adults With Remitted Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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-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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.