VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data
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This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization.
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The HGF workflow included extraction of empirical priors from pilot and Lawson data, parameter recovery, model identifiability, posterior predictive checks, and random-effect Bayesian model comparison.
“The workflow includes the extraction of empirical priors from a combination of our pilot data and the data shared from Lawson et al. [17], parameter recovery and model identifiability based on simulated data, posterior predictive checks and random-effect analysis Bayesian model comparison as implemented in the VBA toolbox [41].”
From Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework (2026) · cites this paper as [41]
Model fitting was performed using a variational Bayesian estimation procedure using the well-validated VBA toolbox.
“Model fitting was performed using a variational Bayesian estimation procedure using the well-validated VBA toolbox 58 .”
From Stress-sensitive inference of task controllability (2022) · cites this paper as [58]
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- Authors
- Daunizeau, J., Adam, V., & Rigoux, L.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- Institut du Cerveau (FR)
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-01-23
- Volume
- 10
- Issue
- 1
- Pages
- e1003441
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3900378
- OpenAlex
- W2049657715
- Cited by
- 385
- References
- 41
- Citations by year
- 2026: 28 · 2025: 36 · 2024: 44 · 2023: 37 · 2022: 39 · 2021: 47 · 2020: 47 · 2019: 24 · 2018: 22 · 2017: 24 · 2016: 21 · 2015: 7 · 2014: 9
- FWCI
- 21.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- stochastic modeling, nonlinear models, parameter estimation, model selection, data simulation, experimental design optimization, behavioral data, computational neuroscience, reproducibility
- MeSH terms
- Algorithms, Bayes Theorem, Cognition, Computer Simulation, Decision Making, Humans, Models, Biological, Models, Neurological, Nerve Net, Probability, Software, Stochastic Processes, Normal Distribution, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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