Catecholaminergic modulation of meta-learning

The remarkable expedience of human learning is thought to be underpinned by meta-learning, whereby slow accumulative learning processes are rapidly adjusted to the current learning environment. To date, the neurobiological implementation of meta-learning remains unclear. A burgeoning literature argu...

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Published ineLife Vol. 8
Main Authors Cook, Jennifer L, Swart, Jennifer C, Froböse, Monja I, Diaconescu, Andreea O, Geurts, Dirk EM, den Ouden, Hanneke EM, Cools, Roshan
Format Journal Article
LanguageEnglish
Published England eLife Sciences Publications Ltd 18.12.2019
eLife Sciences Publications, Ltd
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ISSN2050-084X
2050-084X
DOI10.7554/eLife.51439

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Summary:The remarkable expedience of human learning is thought to be underpinned by meta-learning, whereby slow accumulative learning processes are rapidly adjusted to the current learning environment. To date, the neurobiological implementation of meta-learning remains unclear. A burgeoning literature argues for an important role for the catecholamines dopamine and noradrenaline in meta-learning. Here, we tested the hypothesis that enhancing catecholamine function modulates the ability to optimise a meta-learning parameter (learning rate) as a function of environmental volatility. 102 participants completed a task which required learning in stable phases, where the probability of reinforcement was constant, and volatile phases, where probabilities changed every 10–30 trials. The catecholamine transporter blocker methylphenidate enhanced participants’ ability to adapt learning rate: Under methylphenidate, compared with placebo, participants exhibited higher learning rates in volatile relative to stable phases. Furthermore, this effect was significant only with respect to direct learning based on the participants’ own experience, there was no significant effect on inferred-value learning where stimulus values had to be inferred. These data demonstrate a causal link between catecholaminergic modulation and the adjustment of the meta-learning parameter learning rate.
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These authors contributed equally to this work.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.51439