Details

Autor(en) / Beteiligte

• Maia, Tiago V
• Frank, Michael J

Titel

From reinforcement learning models to psychiatric and neurological disorders

Ist Teil von

• Nature neuroscience, 2011-02, Vol.14 (2), p.154-162

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2011

Link zum Volltext

Quelle

Psychology and Behavioral Sciences Collection

Beschreibungen/Notizen

• Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.