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Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task

Kühnel, A; Teckentrup, V; Neuser, MP; Huys, QJM; Burrasch, C; Walter, M; Kroemer, NB; (2020) Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task. European Neuropsychopharmacology , 35 pp. 17-29. 10.1016/j.euroneuro.2020.03.023. Green open access

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Abstract

When facing decisions to approach rewards or to avoid punishments, we often figuratively go with our gut, and the impact of metabolic states such as hunger on motivation are well documented. However, whether and how vagal feedback signals from the gut influence instrumental actions is unknown. Here, we investigated the effect of non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm in 39 healthy human participants (23 female) after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that taVNS acutely impaired decision-making, p = .041. Computational reinforcement learning models identified the cause of this as a reduction in the learning rate through taVNS (∆α = -0.092, pboot = .002), particularly after punishment (∆αPun = -0.081, pboot = .012 vs. ∆αRew =-0.031, pboot = .22). However, taVNS had no effect on go biases, Pavlovian response biases or response time. Hence, taVNS appeared to influence learning rather than action execution. These results highlight a novel role of vagal afferent input in modulating reinforcement learning by tuning the learning rate according to homeostatic needs.

Type: Article
Title: Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.euroneuro.2020.03.023
Publisher version: https://doi.org/10.1016/j.euroneuro.2020.03.023
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Computational modeling, Instrumental action, Metabolic state, Reinforcement learning, tVNS
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry
URI: https://discovery.ucl.ac.uk/id/eprint/10103010
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