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.
Document type :
Master Thesis
Complete list of metadatas

https://dumas.ccsd.cnrs.fr/dumas-02112675
Contributor : Université Paris Descartes - Scd <>
Submitted on : Friday, April 26, 2019 - 6:34:20 PM
Last modification on : Friday, May 10, 2019 - 10:40:16 AM

File

ThExe_BEAUMONT_Sami_DUMAS.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

Identifiers

  • HAL Id : dumas-02112675, version 1

Citation

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

Share

Metrics

Record views

27

Files downloads

59