Computational Psychiatry, The Self & Brain Imaging | Prof. Karl Friston

In this episode, Professor Karl Friston and I explore brain imaging, dynamic causal modelling, statistical parametric mapping, computational psychiatry, and selfhood through active inference. We discuss how functional brain imaging works, how it helps reveal specialised brain regions, and how researchers study the connectivity and causal interactions between different parts of the brain. Professor Friston also explains dynamic causal modelling, Bayesian model comparison, and how scientists test which models best explain brain imaging data. We then turn to computational psychiatry, false inference, belief updating, precision, prediction errors, neuromodulation, and how psychiatric conditions can be understood through changes in the way the brain updates beliefs. Finally, we discuss treatment, relaxing overly rigid priors, psychedelics, CBT, statistical parametric mapping, the disconnection hypothesis, the self as a hypothesis in active inference, depersonalisation, non-dual states, and the relationship between selfhood, consciousness, and the brain.