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Peggy Seri├Ęs

  1. Date: July 04, 2017. 12:00
    Location: CCU Seminar Room

    Title: Mental Illness as impaired Bayesian Inference.

    Affiliaiton: Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, UK.


    A growing idea in computational neuroscience is that the brain can be
    viewed as a sort of “guessing machine”, constantly trying to guess
    what is present in the external world, what is the best action to take
    and automatically trying to predict the next moment.
    The way the brain would do that is by maintaining and updating
    internal probabilistic models of the world that serve to interpret the
    environment and guide our actions, and using calculations akin to the
    well known statistical methods of “Bayesian inference”.
    This idea 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 sensory information.

    I will 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 prior beliefs.
    I will describe recent results relevant to the study of Schizophrenia,
    Autism and Depression.

    Lab webpage

  2. Hosts:

    Alfonso Renart

    Christian Machens