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

By Jean Daunizeau, Vincent Adam, Lionel Rigoux. 2014

348 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David M. Blei
2. Jean Daunizeau
3. Klaas E. Stephan
4. Alp Kucukelbir
5. Jon D. McAuliffe
6. Karl J. Friston
7. Stephan Mandt
8. Lei Zhang

In Cognitive Neuroscience

1. Karl J. Friston
2. Jean Daunizeau
3. Klaas E. Stephan
4. W. D. Penny
5. Christoph Mathys
6. David M. Blei
7. Alp Kucukelbir
8. Jon D. McAuliffe

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, methodology): “There is a software toolbox that uses variational methods for solving neuroscience and psychology research problems (Daunizeau et al. 2014).”
• The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn? (2014, neutral): “com/p/mbbvb-toolbox/) [34].”
• Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package (2016, methodology): “An exception is the Variational Bayesian Analysis (VBA) MATLAB toolbox (Daunizeau, Adam, & Rigoux, 2014), which allows users to fit and compare various models using variational Bayesian algorithms.”
• Uncertainty in perception and the Hierarchical Gaussian Filter (2014, methodology): “For our simulation study, we used VB as implemented in the DAVB toolbox, available at http://goo . gl/As8p7 (Daunizeau et al., 2009, 2014).”
• Direct Cochlear Recordings in Humans Show a Theta Rhythmic Modulation of Auditory Nerve Activity by Selective Attention (2022, methodology): “, 2016) and 187 was implemented with the VBA Toolbox (Daunizeau et al., 2014).”