title: Impulse control disorders in Parkinson's disease are associated with dysfunction in stimulus valuation but not action valuation.
abstract: A substantial subset of Parkinson's disease (PD) patients suffers from impulse control disorders (ICDs), which are side effects of dopaminergic medication. Dopamine plays a key role in reinforcement learning processes. One class of reinforcement learning models, known as the actor-critic model, suggests that two components are involved in these reinforcement learning processes: a critic, which estimates values of stimuli and calculates prediction errors, and an actor, which estimates values of potential actions. To understand the information processing mechanism underlying impulsive behavior, we investigated stimulus and action value learning from reward and punishment in four groups of participants: on-medication PD patients with ICD, on-medication PD patients without ICD, off-medication PD patients without ICD, and healthy controls. Analysis of responses suggested that participants used an actor-critic learning strategy and computed prediction errors based on stimulus values rather than action values. Quantitative model fits also revealed that an actor-critic model of the basal ganglia with different learning rates for positive and negative prediction errors best matched the choice data. Moreover, whereas ICDs were associated with model parameters related to stimulus valuation (critic), PD was associated with parameters related to action valuation (actor). Specifically, PD patients with ICD exhibited lower learning from negative prediction errors in the critic, resulting in an underestimation of adverse consequences associated with stimuli. These findings offer a specific neurocomputational account of the nature of compulsive behaviors induced by dopaminergic drugs.
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6500 HB Nijmegen, the Netherlands, p.piray@donders.ru.nl a.moustafa@uws.edu.au.
authors.forename: Payam
authors.initials: P
authors.lastname: Piray
authors.affiliation: Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada.
authors.forename: Yashar
authors.initials: Y
authors.lastname: Zeighami
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, 14395-515 Tehran, Iran.
authors.forename: Fariba
authors.initials: F
authors.lastname: Bahrami
authors.affiliation: Psychogeriatric Research Center, Department of Psychiatry, School of Medicine, Ain Shams University, 11566 Cairo, Egypt, and.
authors.forename: Abeer M
authors.initials: AM
authors.lastname: Eissa
authors.affiliation: Psychogeriatric Research Center, Department of Psychiatry, School of Medicine, Ain Shams University, 11566 Cairo, Egypt, and.
authors.forename: Doaa H
authors.initials: DH
authors.lastname: Hewedi
authors.affiliation: School of Social Sciences and Psychology and Marcs Institute for Brain and Behaviour, University of Western Sydney, NSW 2751, Australia p.piray@donders.ru.nl a.moustafa@uws.edu.au.
authors.forename: Ahmed A
authors.initials: AA
authors.lastname: Moustafa
mesh_terms: D000368:Aged; D014150:Antipsychotic Agents / Q000627:therapeutic use*; D003198:Computer Simulation; D007174:Disruptive, Impulse Control, and Conduct Disorders / Q000150:complications*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D010300:Parkinson Disease / Q000150:complications* / Q000188:drug therapy*; D011337:Probability Learning; D011678:Punishment; D012054:Reinforcement, Psychology; D012201:Reward; D012720:Severity of Illness Index
chemical_list: D014150:Antipsychotic Agents
keywords: Parkinson's disease; computational modeling; dopamine; impulse control disorders; prediction error; reinforcement learning