title: Reinforcement learning and Tourette syndrome.
abstract: In this chapter, we report the first experimental explorations of reinforcement learning in Tourette syndrome, realized by our team in the last few years. This report will be preceded by an introduction aimed to provide the reader with the state of the art of the knowledge concerning the neural bases of reinforcement learning at the moment of these studies and the scientific rationale beyond them. In short, reinforcement learning is learning by trial and error to maximize rewards and minimize punishments. This decision-making and learning process implicates the dopaminergic system projecting to the frontal cortex-basal ganglia circuits. A large body of evidence suggests that the dysfunction of the same neural systems is implicated in the pathophysiology of Tourette syndrome. Our results show that Tourette condition, as well as the most common pharmacological treatments (dopamine antagonists), affects reinforcement learning performance in these patients. Specifically, the results suggest a deficit in negative reinforcement learning, possibly underpinned by a functional hyperdopaminergia, which could explain the persistence of tics, despite their evident inadaptive (negative) value. This idea, together with the implications of these results in Tourette therapy and the future perspectives, is discussed in Section 4 of this chapter.
authors.affiliation: Laboratoire des Neurosciences Cognitives (LNC), Ecole Normale Supèrieure (ENS), Paris, France. Electronic address: stefano.palminteri@gmail.com.
authors.forename: Stefano
authors.initials: S
authors.lastname: Palminteri
authors.forename: Mathias
authors.initials: M
authors.lastname: Pessiglione
mesh_terms: D006801:Humans; D007859:Learning Disabilities / Q000209:etiology*; D012054:Reinforcement, Psychology; D005879:Tourette Syndrome / Q000150:complications* / Q000523:psychology
keywords: Computational modeling; Decision-making; Dopamine; Learning; Punishment; Reward