Abstract
RationaleDisorders of compulsivity such as stimulant use disorder (SUD) and obsessive-compulsive disorder (OCD) are characterised by deficits in behavioural flexibility, some of which have been captured using probabilistic reversal learning (PRL) paradigms.ObjectivesThis study used computational modelling to characterise the reinforcement learning processes underlying patterns of PRL behaviour observed in SUD and OCD and to show how the dopamine D2/3 receptor agonist pramipexole and the D2/3 antagonist amisulpride affected these responses.MethodsWe applied a hierarchical Bayesian method to PRL data across three groups: individuals with SUD, OCD, and healthy controls. Participants completed three sessions where they received placebo, pramipexole, and amisulpride, in a double-blind placebo-controlled, randomised design. We compared seven models using a bridge sampling estimate of the marginal likelihood.ResultsStimulus-bound perseveration, a measure of the degree to which participants responded to the same stimulus as before irrespective of outcome, was significantly increased in SUD, but decreased in OCD, compared to controls (on placebo). Individuals with SUD also exhibited reduced reward-driven learning, whilst both the SUD and OCD groups showed increased learning from punishment (nonreward). Pramipexole and amisulpride had similar effects on the control and OCD groups; both increased punishment-driven learning. These D2/3-modulating drugs affected the SUD group differently, remediating reward-driven learning and reducing aspects of perseverative behaviour, amongst other effects.ConclusionsWe provide a parsimonious computational account of how perseverative tendencies and reward- and punishment-driven learning differentially contribute to PRL in SUD and OCD. D2/3 agents modulated these processes and remediated deficits in SUD in particular, which may inform therapeutic effects.
Highlights
Optimal functioning and wellbeing requires flexible adaptation of behaviour to maximise rewards and minimise punishments
Compulsivity is a hallmark of stimulant use disorder (SUD) and obsessive-compulsive disorder (OCD), where behaviour to obtain reward or avoid punishment, inappropriately persists, resulting in undesirable consequences
Conventional analyses of probabilistic reversal learning (PRL) assess sensitivity to immediate reinforcement (Murphy et al 2003; Ersche et al 2011) and do not account for the possibility that choice behaviour is influenced by an integration of feedback history from multiple experiences (Rygula et al 2015)
Summary
Optimal functioning and wellbeing requires flexible adaptation of behaviour to maximise rewards and minimise punishments. The contingencies are reversed, and participants must update their choices to maximise rewards again In these experiments, analysed using classical statistics, individuals with SUD show perseverative deficits—impairments in the ability to update behaviour when circumstances change (Ersche et al 2008, 2011). Individuals with depression (Murphy et al 2003; Taylor Tavares et al 2008) instead show hypersensitivity to spurious negative feedback in PRL, manifested by inappropriately changing behaviour following punishment when it is rare. Nobody has compared the microstructure of behaviour in PRL between disorders of compulsivity using computational models of reinforcement learning (RL).
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