Hierarchical Gaussian Filters (HGFs) for behavioral modelling in computational psychiatry.

Thursday, February 4, 2021 – 4:00 – 5:00 pm

Dr. Chris Mathys, Associate Professor, Interacting Minds Centre, Aarhus University

What?

How can we model behavior in a dynamically changing environment? In particular, how can we quantify individual differences in the way people adapt their learning rates when their environment changes? Going further, can we understand these individual differences in terms of the computational mechanisms driving them? Finally, can insight into the mechanisms of belief adaptation help our understanding of psychopathology?

I approach these questions from the point of view of hierarchical Gaussian filtering, a time series analysis technique which is based on tracking inferred beliefs about a hierarchy of environmental states.

Why?

Hierarchical Gaussian filtering (HGF) is a powerful well validated approach. It has been used extensively and is well-established in computational psychiatry because it is flexible and adaptable to a variety of modeling scenarios. In particular, it has interpretable update equations, is modular and extensible, and it accommodates all varieties of uncertainty present in nonlinear dynamic environments. Furthermore, HGFs can be applied to any time series and are not restricted to behavioral modelling.

How?

Prerequisites:

• basic Matlab programming skills (Matlab requires a license).
• The HGF Toolbox:https://translationalneuromodeling.github.io/tapas is free software that runs efficiently on any laptop. It includes an interactive demo in the form of a Matlab Live Script, which works like a Jupyter notebook.

Reference publications:

• Mathys, C., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A Bayesian foundation for individual learning under uncertainty. Frontiers in Human Neuroscience, 5, 39. https://doi.org/10.3389/fnhum.2011.00039
• Mathys, C., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in Human Neuroscience, 8, 825 https://doi.org/10.3389/fnhum.2014.00825
• Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013). Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning. Neuron, 80(2), 519–530. https://doi.org/10.1016/j.neuron.2013.09.009
• Powers, A. R., Mathys, C., & Corlett, P. R. (2017). Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science, 357(6351), 596–600. https://doi.org/10.1126/science.aan3458

Datasets: Any behavioral time series. More advanced users will be able to apply HGFs to any kind of time series.

Registration Information

You must subscribe to the Workshop series using your email to receive Zoom information for the virtual workshop: https://medicine.yale.edu/psychiatry/map/