Lecturer: Ronald Sladky
Fields: Cognitive Modeling, Data Modeling
Content: Originally, the idea of free energy minimization has been used as a tool for data modeling, in particular, to model effective connectivity in the brain based on neuroimaging data. On top of this, Karl Friston has proposed the free energy principle as a general principle for understanding brain functions. A bold statement – but, if true, one of the biggest breakthroughs in cognitive science.
In the form of active inference, the free energy principle could provide a neuro-computational explanation for predictive processing, the Bayesian brain hypothesis, and enactive cognition. It could provide a unified conceptual and computational framework to link previously distant and isolated research fields in the cognitive sciences that could be compatible with how we understand self-organization of living systems in a physical world.
In this course we will talk about how we make sense of data using models. By looking at how cognitive neuroscientists study the human brain using fMRI and brain connectivity methods. We discuss how models shape the way we interpret the world. I will teach you how these models work so you will find out on your own what to do with them. To what degree is the free energy principle useful for you? Modeling data, modeling the brain – or modeling the mind?
Session 1 covers how we turn fMRI data into brain activation and connectivity models. Session 2 focusses on dynamic causal modeling to study effective connectivity in the brain. Session 3 extends the formalism used in DCM to describe brain functions and life as we know it. Session 4 will review state of the art applications, theoretical developments, and empirical evidence for the free energy principle and active inference in action. As an illustration, I will use my own research on amygdala functions and dysfunctions and its connectivity. So, we will also talk about fear, trust, and other emotions – not just data, brains, and methods.
Literature
Friston, K. (2009). The free-energy principle: a rough guide to the brain?. Trends in cognitive sciences, 13(7), 293-301.
Marreiros, A. C., Stephan, K. E., & Friston, K. J. (2010). Dynamic causal modeling. Scholarpedia, 5(7), 9568.
Friston, K. (2013). Life as we know it. Journal of the Royal Society Interface, 10(86), 20130475.
van Es, T. (2021). Living models or life modelled? On the use of models in the free energy principle. Adaptive Behavior, 29(3), 315-329.
Corcoran, A. W., Hohwy, J., & Friston, K. J. (2023). Accelerating scientific progress through Bayesian adversarial collaboration. Neuron, 111(22), 3505-3516.
Sladky, R., Kargl, D., Haubensak, W., & Lamm, C. (2023). An active inference perspective for the amygdala complex. Trends in Cognitive Sciences.
Lecturer
Ronald Sladky. My research focuses on the amygdala and emotion processing in the human brain. In addition, I am always working on new neuroimaging, data processing, and modeling methods. One of these new methods is real-time functional MRI, where people can learn to regulate their own brain states while they are inside the MRI scanner. This method is not only a promising therapeutic tool, it will also allow for completely new ways of discovering how our brains work.
Affiliation: University of Vienna
Website: http://sweetneuron.at