Cognitive flexibility allows us to adaptively switch between different responsibilities in important domains of our daily life. Previous work has elucidated the neurochemical basis underlying the ability to switch responses to a previously nonreinforced exemplar and to switch between attentional sets. However, the role of neuromodulators in task switching, the ability to rapidly switch between two or more cognitive tasks afforded by the same stimuli, is still poorly understood. We attempted to fill this gap by manipulating norepinephrine levels using stress manipulation (Study 1a, n = 48; between-group design), transcutaneous vagus nerve stimulation at two different intensities (Study 1b, n = 48; sham-controlled between-group design), and pharmacological manipulation (Study 2, n = 24; double-blind crossover design), all of which increased salivary cortisol measures. Participants repeatedly switched between two cognitive tasks (classifying a digit as high/low [Task 1] or as odd/even [Task 2]), depending on the preceding cue. On each trial, a cue indicated the task to be performed. The cue–stimulus interval was varied to manipulate the time to prepare for the switch. Participants showed typical switch costs, which decreased with the time available for preparation. None of the manipulations modulated the size of the switch costs or the preparation effect, as supported by frequentist and Bayesian model comparisons. Task-switching performance reflects a complex mix of cognitive control and bottom–up dynamics of task-set representations. Our findings suggest that norepinephrine does not affect either of these aspects of cognitive flexibility.

To make optimal predictions in a dynamic environment, the impact of new observations on existing beliefs—that is, the learning rate—should be guided by ongoing estimates of change and uncertainty. Theoretical work has proposed specific computational roles for various neuromodulatory systems in the control of learning rate, but empirical evidence is still sparse. The aim of the current research was to examine the role of the noradrenergic and cholinergic systems in learning rate regulation. First, we replicated our recent findings that the centroparietal P3 component of the EEG—an index of phasic catecholamine release in the cortex—predicts trial-to-trial variability in learning rate and mediates the effects of surprise and belief uncertainty on learning rate (Study 1, n = 17). Second, we found that pharmacological suppression of either norepinephrine or acetylcholine activity produced baseline-dependent effects on learning rate following nonobvious changes in an outcome-generating process (Study 1). Third, we identified two genes, coding for α2A receptor sensitivity ( ADRA2A ) and norepinephrine reuptake ( NET ), as promising targets for future research on the genetic basis of individual differences in learning rate (Study 2, n = 137). Our findings suggest a role for the noradrenergic and cholinergic systems in belief updating and underline the importance of studying interactions between different neuromodulatory systems.

People tend to slow down after they commit an error, a phenomenon known as post-error slowing (PES). It has been proposed that slowing after negative feedback or unforeseen errors is linked to the activity of the locus coeruleus–norepinephrine (LC–NE) system, but there is little direct evidence for this hypothesis. Here, we assessed the causal role of the noradrenergic system in modulating PES by applying transcutaneous vagus nerve stimulation (tVNS), a new noninvasive and safe method to stimulate the vagus nerve and to increase NE concentrations in the brain. A single-blind, sham-controlled, between-group design was used to assess the effect of tVNS in healthy young volunteers ( n = 40) during two cognitive tasks designed to measure PES. Results showed increased PES during active tVNS, as compared with sham stimulation. This effect was of similar magnitude for the two tasks. These findings provide evidence for an important role of the noradrenergic system in PES.

The adaptive regulation of the balance between exploitation and exploration is critical for the optimization of behavioral performance. Animal research and computational modeling have suggested that changes in exploitative versus exploratory control state in response to changes in task utility are mediated by the neuromodulatory locus coeruleus–norepinephrine (LC–NE) system. Recent studies have suggested that utility-driven changes in control state correlate with pupil diameter, and that pupil diameter can be used as an indirect marker of LC activity. We measured participants' pupil diameter while they performed a gambling task with a gradually changing payoff structure. Each choice in this task can be classified as exploitative or exploratory using a computational model of reinforcement learning. We examined the relationship between pupil diameter, task utility, and choice strategy (exploitation vs. exploration), and found that (i) exploratory choices were preceded by a larger baseline pupil diameter than exploitative choices; (ii) individual differences in baseline pupil diameter were predictive of an individual's tendency to explore; and (iii) changes in pupil diameter surrounding the transition between exploitative and exploratory choices correlated with changes in task utility. These findings provide novel evidence that pupil diameter correlates closely with control state, and are consistent with a role for the LC–NE system in the regulation of the exploration–exploitation trade-off in humans.

The information processing capacity of the human mind is limited, as is evidenced by the attentional blink—a deficit in identifying the second of two targets (T1 and T2) presented in close succession. This deficit is thought to result from an overinvestment of limited resources in T1 processing. We previously reported that intensive mental training in a style of meditation aimed at reducing elaborate object processing, reduced brain resource allocation to T1, and improved T2 accuracy [Slagter, H. A., Lutz, A., Greischar, L. L., Francis, A. D., Nieuwenhuis, S., Davis, J., et al. Mental training affects distribution of limited brain resources. PloS Biology, 5, e138, 2007]. Here we report EEG spectral analyses to examine the possibility that this reduction in elaborate T1 processing rendered the system more available to process new target information, as indexed by T2-locked phase variability. Intensive mental training was associated with decreased cross-trial variability in the phase of oscillatory theta activity after successfully detected T2s, in particular, for those individuals who showed the greatest reduction in brain resource allocation to T1. These data implicate theta phase locking in conscious target perception, and suggest that after mental training the cognitive system is more rapidly available to process new target information. Mental training was not associated with changes in the amplitude of T2-induced responses or oscillatory activity before task onset. In combination, these findings illustrate the usefulness of systematic mental training in the study of the human mind by revealing the neural mechanisms that enable the brain to successfully represent target information.

People typically respond faster to a stimulus when it is accompanied by a task-irrelevant accessory stimulus presented in another perceptual modality. However, the mechanisms responsible for this accessory-stimulus effect are still poorly understood. We examined the effects of auditory accessory stimulation on the processing of visual stimuli using scalp electrophysiology (Experiment 1) and a diffusion model analysis (Experiment 2). In accordance with previous studies, lateralized readiness potentials indicated that accessory stimuli do not speed motor execution. Surface Laplacians over the motor cortex, however, revealed a bihemispheric increase in motor activation—an effect predicted by nonspecific arousal models. The diffusion model analysis suggested that accessory stimuli do not affect parameters of the decision process, but expedite only the nondecision component of information processing. Consequently, we conclude that accessory stimuli facilitate stimulus encoding. The visual P1 and N1 amplitudes on accessory-stimulus trials were modulated in a way that is consistent with multisensory energy integration, a possible mechanism for this facilitation.