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Background: Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising brain stimulation method for the treatment of pharmaco-resistant epilepsy and depression. Its clinical efficacy is thought to depend on taVNS-induced activation of the locus coeruleus. However, unlike for invasive VNS, there is little evidence for an effect of taVNS on noradrenergic activity. Objective: We attempted to replicate recently published findings by Sharon et al. (2021), showing that short bursts of taVNS transiently increased pupil size and decreased EEG alpha power, two correlates of central noradrenergic activity. Methods: Following the original study, we used a single-blind, sham-controlled, randomized cross-over design. We applied short-term (3.4 s) taVNS in healthy human volunteers (n=29), while collecting resting-state pupil-size and EEG data. To analyze the data, we used scripts provided by Sharon and colleagues. Results: Consistent with Sharon et al. (2021), pupil dilation was significantly larger during taVNS than during sham stimulation (p = .009; Bayes factor supporting the difference = 7.45). However, we failed to replicate the effect of taVNS on EEG alpha power (p = .37); the data were four times more likely under the null hypothesis (BF10 = 0.28). Conclusion: Our findings support the effectiveness of short-term taVNS in inducing transient pupil dilation, a correlate of phasic noradrenergic activity. However, we failed to replicate the recent finding by Sharon et al. (2021) that taVNS attenuates EEG alpha activity. Overall, this study highlights the need for continued research on the neural mechanisms underlying taVNS efficacy and its potential as a treatment option for pharmaco-resistant conditions. It also highlights the need for direct replications of influential taVNS studies.

Decision making relies on the interplay between two distinct learning mechanisms, namely habitual model-free learning and goal-directed model-based learning. Recent literature suggests that this interplay is significantly shaped by the environmental structure as represented by an internal model. We employed a modified two-stage but one-decision Markov decision task to investigate how two internal models differing in the predictability of stage transitions influence the neural correlates of feedback processing. Our results demonstrate that fronto-central theta and the feedback-related negativity (FRN), two correlates of reward prediction errors in the medial frontal cortex, are independent of the internal representations of the environmental structure. In contrast, centro-parietal delta and the P3, two correlates possibly reflecting feedback evaluation in working memory, were highly susceptible to the underlying internal model. Model-based analyses of single-trial activity showed a comparable pattern, indicating that while the computation of unsigned reward prediction errors is represented by theta and the FRN irrespective of the internal models, the P3 adapts to the internal representation of an environment. Our findings further substantiate the assumption that the feedback-locked components under investigation reflect distinct mechanisms of feedback processing and that different internal models selectively influence these mechanisms.

The goal of temporal difference (TD) reinforcement learning is to maximize outcomes and improve future decision-making. It does so by utilizing a prediction error (PE), which quantifies the difference between the expected and the obtained outcome. In gambling tasks, however, decision-making cannot be improved because of the lack of learnability. On the basis of the idea that TD utilizes two independent bits of information from the PE (valence and surprise), we asked which of these aspects is affected when a task is not learnable. We contrasted behavioral data and ERPs in a learning variant and a gambling variant of a simple two-armed bandit task, in which outcome sequences were matched across tasks. Participants were explicitly informed that feedback could be used to improve performance in the learning task but not in the gambling task, and we predicted a corresponding modulation of the aspects of the PE. We used a model-based analysis of ERP data to extract the neural footprints of the valence and surprise information in the two tasks. Our results revealed that task learnability modulates reinforcement learning via the suppression of surprise processing but leaves the processing of valence unaffected. On the basis of our model and the data, we propose that task learnability can selectively suppress TD learning as well as alter behavioral adaptation based on a flexible cost–benefit arbitration.