IC3 – A short introduction to Bayesian descriptions of information processing in the brain

Lecturer: Chris Mathys
Fields: Cognitive neuroscience, computational modelling


Assuming that the brain is an organ of prediction is one of the most fruitful approaches to understanding what it does and how it works. In this short introduction, we will look at how we can describe the brain\’s activity as reflecting updates to predictions in response to new information. Beyond that, we will see that actions too can be understood as being driven by predictions. But how do we update predictions when our environment changes? If we want our predictions to be accurate and our uncertainty about them realistic, we need to observe the rules of probability. This means that our belief updates can be described in terms of Bayesian inference, and so can the brain\’s neural activity, which we will see in several examples.


  • 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
  • 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


Chris Mathys

Chris Mathys is Associate Professor of Cognitive Science at Aarhus University. He originally trained as a physicist and has a PhD in Information Technology from ETH Zurich.

Affiliation: Interacting Minds Centre, Aarhus University
Homepage: https://chrismathys.com