Peggy Series: Bayesian Approaches to Understanding Mental Illness
From Belle Taylor
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A growing idea in computational neuroscience is that perception and cognition can be described in terms of predictive processing or Bayesian inference: the nervous system would maintain and update internal probabilistic models that serve to interpret the world and guide our actions. This approach is increasingly recognised to also be of interest to Psychiatry. Mental illness could correspond to the brain trying to interpret the world through distorted internal models, or incorrectly combining such internal models with incoming sensory information. I describe work pursued in my lab that aims at uncovering such internal models, using behavioural experiments and computational methods. In health, we are particularly interested in clarifying how prior beliefs affect perception and decision-making, how long they take to build up or be unlearned, how complex they can be, and how they can inform us on the type of computations and learning that the brain performs. In mental illness, we are interested in understanding whether/how the machinery of probabilistic inference could be impaired, and/or relies on the use of distorted priors. I also introduce the emerging field of Computational Psychiatry and describe recent results relevant to the study of Schizophrenia and Autism.
This talk was part of the research day Interfaces between Statistics, Machine Learning and AI hosted by the Centre for Statistics and the Bayes Centre.