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Learn how hierarchical Bayesian models can be applied to understand psychosis through this conference talk by Andreea O. Diaconescu from the University of Toronto. Explore the mathematical frameworks and computational approaches used to model the complex neural mechanisms underlying psychotic disorders. Discover how Bayesian inference principles can illuminate the hierarchical processing disruptions that characterize conditions like schizophrenia and other psychotic spectrum disorders. Examine the theoretical foundations of predictive coding and how aberrant precision weighting in hierarchical message passing may contribute to the symptoms and cognitive patterns observed in psychosis. Gain insights into how these computational models bridge neuroscience, psychiatry, and machine learning to advance our understanding of mental health conditions and potentially inform therapeutic interventions.
