ArticleeLife2022
Flexible and efficient simulation-based inference for models of decision-making.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 48 citations in OpenAlex.
- The perils of omitting omissions when modeling evidence accumulation.PLoS computational biology · 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
- Simulation-based inference at the theoretical limit for fast, robust microstructural MRI with minimal diffusion data.Communications medicine · 2026Article
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- Model-based design of subthalamic nucleus neurons using hybrid optimization.Journal of neurophysiology · 2026Article
- A Framework Integrating Spiking Cortical Circuit Modeling and Simulation-Based Inference to Probe Biomarkers of Cortical Dysfunction in Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2026Article
- Latent variable sequence identification for cognitive models with neural network estimators.Behavior research methods · 2025Article
- A Python toolbox for neural circuit parameter inference.NPJ systems biology and applications · 2025Article
- Biophysical modeling and experimental analysis of the dynamics of C. elegans body-wall muscle cells.PLoS computational biology · 2025Article
- Rapid, systematic updating of movement by accumulated decision evidence.Nature communications · 2024Article
- The neural implausibility of the diffusion decision model doesn't matter for cognitive psychometrics, but the Ornstein-Uhlenbeck model is better.Psychonomic bulletin & review · 2024Article
- AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.PLoS computational biology · 2024Article
- Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.PLoS computational biology · 2024Article
- Artificial neural networks for model identification and parameter estimation in computational cognitive models.PLoS computational biology · 2024Article
- Artificial neural networks for model identification and parameter estimation in computational cognitive models.bioRxiv : the preprint server for biology · 2024Article
- Naturalistic reinforcement learning.Trends in cognitive sciences · 2024Review
- Rapid, systematic updating of movement by accumulated decision evidence.bioRxiv : the preprint server for biology · 2024Article
- Cognitive mechanisms of learning in sequential decision-making under uncertainty: an experimental and theoretical approach.Frontiers in behavioral neuroscience · 2024Article
- Simulation-based inference for efficient identification of generative models in computational connectomics.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring parameters of computational models that capture experimental data are a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model-however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making, but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator, and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations, and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.
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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.