Generative mechanistic models of decision making (left) can be encapsulated into likelihood approximation networks (LANs, right), allowing the fast estimation of parameters using Bayesian inference, including estimation of brain signals from different experiments as covariates (top). LANs only require expensive computational cost once upfront, and can then be recycled for repeated use (middle). Image credit: A. Fengler and M. J. Frank (CC BY 4.0)
Cognitive neuroscience studies the links between the physical brain and cognition. Computational models that attempt to describe the relationships between the brain and specific behaviours quantitatively is becoming increasingly popular in this field. This approach may help determine the causes of certain behaviours and make predictions about what behaviours will be triggered by specific changes in the brain.
Many of the computational models used in cognitive neuroscience are built based on experimental data. A good model can predict the results of new experiments given a specific set of conditions with few parameters. Candidate models are often called ‘generative’: models that can simulate data. However, typically, cognitive neuroscience studies require going the other way around: they need to infer models and their parameters from experimental data. Ideally, it should also be possible to properly assess the remaining uncertainty over the parameters after having access to the experimental data. To facilitate this, the Bayesian approach to statistical analysis has become popular in cognitive neuroscience.
Common software tools for Bayesian estimation require a ‘likelihood function’, which measures how well a model fits experimental data for given values of the unknown parameters. A major obstacle is that for all but the most common models, obtaining precise likelihood functions is computationally costly. In practice, this requirement limits researchers to evaluating and comparing a small subset of neurocognitive models for which a likelihood function is known. As a result, it is convenience, rather than theoretical interest, that guides this process.
In order to provide one solution for this problem, Fengler et al. developed a method that allows users to perform Bayesian inference on a larger number of models without high simulation costs. This method uses likelihood approximation networks (LANs), a computational tool that can estimate likelihood functions for a broad class of models of decision making, allowing researchers to estimate parameters and to measure how well models fit the data. Additionally, Fengler et al. provide both the code needed to build networks using their approach and a tutorial for users.
The new method, along with the user-friendly tool, may help to discover more realistic brain dynamics underlying cognition and behaviour as well as alterations in brain function.
