Computational modelling reveals contrasting effects on reinforcement learning and cognitive flexibility in stimulant use disorder and obsessive-compulsive disorder: remediating effects of dopaminergic D2/3 receptor agents

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.Electronic supplementary materialThe online version of this article (10.1007/s00213-019-05325-w) contains supplementary material, which is available to authorized users.

Probabilistic Reversal Learning Deficits in Patients With Methamphetamine Use Disorder—A Longitudinal Pilot Study

“…Patients showed greater difficulties in adjusting their behavior following a rule change compared with HC. This finding is in line with earlier studies suggesting similar deficits in patients suffering from amphetamine (24,27) and cocaine (42) dependence from the United Kingdom. Using a combination of a "Go/No-Go" task with reversal learning, a study has shown maladaptive choices among cocaine and alcoholdependent patients when contingency change.…”

Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma

“…Our finding that lower action precision was associated with fewer wins, and a greater number of shifts to a new choice after a win, suggest a failure of SUDs to settle on a behavior strategy despite sufficient evidence. This appears consistent with previous work suggesting that substance users are less likely than HCs to stick to successful decision strategies (Kanen et al, 2019;Myers et al, 2016). Future work will be necessary to better understand the possible bases of this difference (e.g., underconfidence, distractibility, reduced awareness, etc.).…”

“…Both opioid and stimulant (cocaine and/or amphetamine) users are less likely than HCs to stick to successful decision strategies and instead: (a) choose to switch responses even when a previous response has been rewarding (Myers et al, 2016); or (b) perseverate on responses independent of outcomes (Kanen et al, 2019). Similar difficulties in flexibly adjusting behavior following punishments have also been reported in other stimulant user samples ((Ersche et al, 2016;Ersche et al, 2011); but see (Kanen et al, 2019)). In contrast, stimulant users appear to exhibit heightened sensitivity to monetary reward (Ahn et al, 2014).…”

“…Moreover, opioid users perform more poorly than HCs while learning to avoid punishment under high memory load (Myers et al, 2017) and higher ambiguity tolerance predicts prospective opioid use (Konova et al, 2019). Both opioid and stimulant (cocaine and/or amphetamine) users are less likely than HCs to stick to successful decision strategies and instead: (a) choose to switch responses even when a previous response has been rewarding (Myers et al, 2016); or (b) perseverate on responses independent of outcomes (Kanen et al, 2019). Similar difficulties in flexibly adjusting behavior following punishments have also been reported in other stimulant user samples ((Ersche et al, 2016;Ersche et al, 2011); but see (Kanen et al, 2019)).…”

Modulation of value-based decision making behavior by subregions of the rat prefrontal cortex

“…A recent study showed changes in positive and negative feedback learning, as well as in response persistence (stickiness), in people with stimulant abuse disorder and obsessivecompulsive disorder (Kanen et al 2019), two psychopathologies in which the human PFC has been implicated (Bechara and Van Der Linden 2005;Volkow and Morales 2015). This may provide interesting clinical relevance to our findings, especially since these same authors provided a potential drug target (the dopamine D 2/3 receptor) for modulating these components of learning and decision-making in humans (Kanen et al 2019). In addition, the subregion-specific involvement of the mOFC and PrL in stickiness may be of special clinical importance, given the suggested involvement of maladaptive response persistence-irrespective of outcome-in addictive behaviors (Everitt and Robbins 2016).…”