Interoceptive feedback transmitted via the vagus nerve plays a vital role in motivation by tuning actions according to physiological needs. Whereas vagus nerve stimulation (VNS) reinforces actions and enhances dopamine transmission in animals, motivational effects elicited by VNS in humans are still largely elusive. Here, we applied non-invasive transcutaneous auricular VNS (taVNS) on the left or the right ear using a randomized cross-over design (vs. sham). During stimulation, 81 healthy participants had to exert effort to earn food or monetary rewards. We reasoned that taVNS enhances motivation and tested whether it does so by increasing prospective benefits (i.e., vigor) or reducing costs of action (i.e., maintenance) compared to sham stimulation. In line with preclinical studies, taVNS generally enhanced invigoration of effort (p = .004, Bayes factor, BF10 = 7.34), whereas stimulation on the left side primarily facilitated vigor for food rewards (left taVNS: Stimulation × Reward Type, p = .003, BF10 = 11.80). In contrast, taVNS did not affect effort maintenance (ps ≥ .09, BF10 < 0.52). Critically, during taVNS, vigor declined less steeply with decreases in wanting (∆b = -.046, p = .031) indicating a boost in the drive to work for rewards. Collectively, our results suggest that taVNS enhances reward-seeking by boosting vigor, not effort maintenance and that the side of the stimulation affects generalization beyond food reward. We conclude that taVNS may enhance the pursuit of prospective rewards which may pave new avenues for treatment of motivational deficiencies. taVNS increases vigor Neuser et al.3

AbstractWhen 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 vagus nerve stimulation (tVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm (Guitart-Masip et al., 2012) in 39 healthy, participants after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that tVNS acutely impaired decision-making, p = .045. Computational reinforcement learning models identified the cause of this as a reduction in the learning rate through tVNS (Δα = −0.092, pboot= .002), particularly after punishment (ΔαPun= −0.081, pboot= .012 vs. ΔαRew= −0.031, p = .22). However, tVNS had no effect on go biases, Pavlovian response biases or response time. Hence, tVNS 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.

Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. Here, we developed an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) for repeated assessment over weeks. In this task, players have to identify the most effective medication by selecting the best option after integrating offered points with changing probabilities (according to random Gaussian walks). Participants can complete up to 31 levels with 150 trials each. To encourage replay on their preferred device, in-game screens provide feedback on the progress. Using an initial validation sample of 127 players (2904 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show low to medium intra-class correlations (ICC: 0.22-0.52), indicating substantial within- and between-subject variance. Notably, state items showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.