Effects of methylphenidate on reinforcement learning depend on working memory capacity

Mojtaba Rostami Kandroodi, Jennifer L. Cook, Jennifer C. Swart, Monja I. Froböse, Dirk E. M. Geurts, Abdol-Hossein Vahabie, Majid Nili Ahmadabadi, Roshan Cools, Hanneke E. M. den Ouden

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Abstract

Rationale
Brain catecholamines have long been implicated in reinforcement learning, exemplified by catecholamine drug and genetic effects on probabilistic reversal learning. However, the mechanisms underlying such effects are unclear.

Objectives and methods
Here we investigated effects of an acute catecholamine challenge with methylphenidate (20 mg, oral) on a novel probabilistic reversal learning paradigm in a within-subject, double-blind randomised design. The paradigm was designed to disentangle effects on punishment avoidance from effects on reward perseveration. Given the known large individual variability in methylphenidate’s effects, we stratified our effects by working memory capacity and trait impulsivity, putatively modulating the effects of methylphenidate, in a large sample (n = 102) of healthy volunteers.

Results
Contrary to our prediction, methylphenidate did not alter performance in the reversal phase of the task. Our key finding is that methylphenidate altered learning of choice-outcome contingencies in a manner that depended on individual variability in working memory span. Specifically, methylphenidate improved performance by adaptively reducing the effective learning rate in participants with higher working memory capacity.

Conclusions
This finding emphasises the important role of working memory in reinforcement learning, as reported in influential recent computational modelling and behavioural work, and highlights the dependence of this interplay on catecholaminergic function.
Original languageEnglish
JournalPsychopharmacology
Early online date21 Oct 2021
DOIs
Publication statusE-pub ahead of print - 21 Oct 2021

Keywords

  • Methylphenidate
  • Working memory
  • Reinforcement learning
  • Reversal learning
  • Dopamine
  • Computational modelling of behaviour

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