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VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

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  • Jean Daunizeau
  • Vincent Adam
  • Lionel Rigoux

Abstract

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.

Suggested Citation

  • Jean Daunizeau & Vincent Adam & Lionel Rigoux, 2014. "VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-16, January.
    • Handle: RePEc:plo:pcbi00:1003441
    DOI: 10.1371/journal.pcbi.1003441
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    References listed on IDEAS

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    1. Jean Daunizeau & Kerstin Preuschoff & Karl Friston & Klaas Stephan, 2011. "Optimizing Experimental Design for Comparing Models of Brain Function," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-18, November.
    2. Jean Daunizeau & Hanneke E M den Ouden & Matthias Pessiglione & Stefan J Kiebel & Klaas E Stephan & Karl J Friston, 2010. "Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-10, December.
    3. repec:dau:papers:123456789/1908 is not listed on IDEAS
    4. Will D Penny & Klaas E Stephan & Jean Daunizeau & Maria J Rosa & Karl J Friston & Thomas M Schofield & Alex P Leff, 2010. "Comparing Families of Dynamic Causal Models," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-14, March.
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