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Master Thesis

La psychiatrie computationnelle : vers une nouvelle approche théorique de la psychopathologie

Abstract : The aetiology of psychiatric disorders remains poorly understood, despite significant progress in neurosciences and psychopharmacology. These approaches might be limited by their lack of causal account linking psychiatric symptoms and neurobiological data. This dissertation presents a new theoretical approach of psychopathology, i.e. computational psychiatry, that aims to develop and test hypotheses about the algorithms that the brain implements to produce observable behavior. To that purpose, we review two major computational models. First, models from reinforcement learning and neuro-economics give a novel account of symptoms as a decision making process gone awry (such as addiction, impulsiveness, compulsions or apathy). Then, we examine how Bayesian modeling of probabilistic inference enlightens psychotic symptoms. Finally, we discuss the limitations and future applications of computational approaches in psychiatry.
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Master Thesis
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Submitted on : Friday, April 26, 2019 - 6:34:20 PM
Last modification on : Thursday, November 18, 2021 - 11:49:21 AM


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  • HAL Id : dumas-02112675, version 1


Sami Beaumont. La psychiatrie computationnelle : vers une nouvelle approche théorique de la psychopathologie. Médecine humaine et pathologie. 2018. ⟨dumas-02112675⟩



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