ABSTRACT Conflict monitoring and error processing are fundamental mechanisms underlying cognitive control and adaptive behavior and have been consistently associated with increased activity in the anterior midcingulate cortex (aMCC). Here, we used transcranial ultrasound stimulation (TUS), an emerging technique that enables non-invasive, deep, and focal neuromodulation, and EEG to investigate the causal role of the aMCC in cognitive control. Our findings demonstrate that TUS of aMCC improved performance, modulated the relationship between conflict monitoring and the stimulus-locked N2, and strengthened the suppression of distracting flankers as revealed by drift-diffusion model analyses. This suggests that TUS of aMCC enhances proactive control. Interestingly, TUS did not affect the error-related negativity as a measure of error monitoring. Finally, the TUS effects were observed during early compared with later task blocks corroborating previous findings which suggested that TUS effects are temporally dynamic and characterized by a limited post-stimulation window. HIGHLIGHTS aMCC-TUS and PCC-TUS both increase behavioral accuracy during the early stages of task performance. aMCC-TUS transiently reduces the effect of incongruence on the stimulus-locked N2 suggesting reduced response conflict. Exploratory DDM analyses suggest that aMCC-TUS improves suppression of conflict- inducing distractors. aMCC thus selectively enhances proactive control, thereby reducing response conflict. TUS effects show a transient temporal profile, peaking 17–27 minutes after stimulation and declining after ∼37 minutes.

To address suggestions that human brain responses to autonomous system errors may be used as brain-based measures of trust in automation, the present study asked participants to monitor the performance of either a virtual human or an autonomous system partner performing a novel, complex, real-world image classification task. We predicted visual feedback of partner errors would elicit the feedback-related negativity and P3 ERP components, and that these components would differ between the human and system groups. Behavioral results showed that while participants calibrated their trust in their partner according to our intended manipulation of error rates, no group differences were found. The ERP data, however, revealed FRN and P3 effects for both groups, modulated by accuracy and error rate. An unexpected finding was that the P3 topography differed between groups, with both a frontal P3a and posterior P3b component seen for the human condition, while for the system condition only the posterior P3b was observed and the P3a was completely absent. We suggest that this selective absence of the P3a may reflect reduced frontal attention during system monitoring in passive task conditions potentially resulting from reduced social and emotional processing for the system partner. This study demonstrates the potential for EEG-based measures of trust in automation to surpass the sensitivity of traditional measures of trust while additionally uncovering a potential neural signature of automation complacency in the absent P3a. This identifies potential boundary conditions under which the application of human correlates of performance monitoring may not apply to the monitoring of an automated system.

Abstract Sleep deprivation is known to impair cognitive performance, yet its effects on error awareness and subsequent behavioral adjustments remain incompletely understood. Here, we investigated how sleep loss affects the use of subjective performance evaluation to guide post-error adaptations. Thirty healthy adults completed a novel, gamified error awareness multi-rule Simon task once while well rested and once after 24 h of total sleep deprivation. On each trial, participants reported both their task response and subjective evaluation of response accuracy. This design allowed us to dissociate objective performance from subjective error awareness and to examine their influence on subsequent behavior over time. Sleep deprivation slowed responses, reduced accuracy, increased missed responses, and decreased the proportion of consciously detected errors. These effects increased with time on task and were accompanied by greater instability in sustained attention. Critically, post-error adjustments were driven by subjective error awareness rather than factual error commission. Reaction times slowed most strongly after subjectively perceived errors, including instances in which the preceding response had been objectively correct. Accuracy showed post-error decreases that were most pronounced following unaware errors. Sleep deprivation further altered these awareness-dependent control processes, particularly in later task phases. Together, these findings indicate that sleep deprivation disrupts both error awareness and the effective use of awareness signals for behavioral regulation. Statement of significance One night of total sleep deprivation reduces behavioral error awareness and disrupts post-error adjustments in a time-dependent manner. Crucially, our findings show that adaptive cognitive control is strongly shaped by subjective error awareness—even when that awareness is inaccurate. By identifying conscious performance evaluation as a key mechanism linking sustained attention, sleep loss, and behavioral regulation, this work highlights the importance of considering subjective awareness when studying adaptive control under fatigue.

This chapter outlines the involvement of the posterior medial frontal cortex (pMFC) in performance monitoring, cognitive control, and decision making. We first describe the neuroanatomy of the pMFC. We then review the functional contributions of the pMFC to performance monitoring, resulting adaptations, and decision making. Based on the evidence reviewed in this chapter, we conclude that a broad array of performance monitoring and decision-making signals are represented in the pMFC. Specific properties of these signals allow the pMFC to monitor performance, implement necessary adjustments, and to perform complex reward and environment structure learning. Future research should consider the substantial interindividual variability of pMFC anatomy to parse out the representations of these signals within the pMFC into finer level of detail.

The Concealed Information Test (CIT) is frequently used to determine the presence of crime-related information in a suspect's memory. In this paper, we conducted a meta-analysis to test the validity of the CIT to differentiate between guilty and innocent individuals based on amplitude differences of the P300 component of the event-related potential. We included k = 54 experimental studies that used either the mock-crime paradigm or the personal-item paradigm. The results show a large mean effect size (d*) of 1.59 for the P300. Moderation analysis showed that P300 effects in CIT are affected by the choice of paradigm (personal-item vs. mock-crime paradigm), the chosen trial protocol (complex vs. original) and the likelihood of subjects to employ countermeasures. Based on our findings, we conclude that the P300 is useful to determine the presence of crime-related information and that people interested in using the CIT should use the complex trial protocol to maximize effect sizes.

Learning an association does not always succeed on the first attempt. Previous studies associated increased error signals in posterior medial frontal cortex with improved memory formation. However, the neurophysiological mechanisms that facilitate post-error learning remain poorly understood. To address this gap, participants performed a feedback-based association learning task and a 1-back localizer task. Increased hemodynamic responses in posterior medial frontal cortex were found for internal and external origins of memory error evidence, and during post-error encoding success as quantified by subsequent recall of face-associated memories. A localizer-based machine learning model displayed a network of cognitive control regions, including posterior medial frontal and dorsolateral prefrontal cortices, whose activity was related to face-processing evidence in the fusiform face area. Representation strength was higher during failed recall and increased during encoding when subsequent recall succeeded. These data enhance our understanding of the neurophysiological mechanisms of adaptive learning by linking the need for learning with increased processing of the relevant stimulus category.

The ability to calibrate learning according to new information is a fundamental component of an organism's ability to adapt to changing conditions. Yet, the exact neural mechanisms guiding dynamic learning rate adjustments remain unclear. Catecholamines appear to play a critical role in adjusting the degree to which we use new information over time, but individuals vary widely in the manner in which they adjust to changes. Here, we studied the effects of a low dose of methamphetamine (MA), and individual differences in these effects, on probabilistic reversal learning dynamics in a within-subject, double-blind, randomized design. Participants first completed a reversal learning task during a drug-free baseline session to provide a measure of baseline performance. Then they completed the task during two sessions, one with MA (20 mg oral) and one with placebo (PL). First, we showed that, relative to PL, MA modulates the ability to dynamically adjust learning from prediction errors. Second, this effect was more pronounced in participants who performed moderately low at baseline. These results present novel evidence for the involvement of catecholaminergic transmission on learning flexibility and highlights that baseline performance modulates the effect of the drug.

A prominent account of decision-making assumes that information is accumulated until a fixed response threshold is crossed. However, many decisions require weighting of information appropriately against time. Collapsing response thresholds are a mathematically optimal solution to this decision problem. However, our understanding of the neurocomputational mechanisms underlying dynamic response thresholds remains significantly incomplete. To investigate this issue, we used a multistage drift–diffusion model (DDM) and also analyzed EEG β power lateralization (BPL). The latter served as a neural proxy for decision signals. We analyzed a large dataset (n = 863; 434 females and 429 males) from a speeded flanker task and data from an independent confirmation sample (n = 119; 70 females and 49 males). We showed that a DDM with collapsing decision thresholds, a process wherein the decision boundary reduces over time, captured participants' time-dependent decision policy more accurately than a model with fixed thresholds. Previous research suggests that BPL over motor cortices reflects features of a decision signal and that its peak, coinciding with the motor response, may serve as a neural proxy for the decision threshold. We show that BPL around the response decreased with increasing RTs. Together, our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes.

The cholinergic system plays a key role in motor function, but whether pharmacological modulation of cholinergic activity affects motor sequence learning is unknown. The acetylcholine receptor antagonist biperiden, an established treatment in movement disorders, reduces attentional modulation, but whether it influences motor sequence learning is not clear. Using a randomized, double-blind placebo-controlled crossover design, we tested thirty healthy young participants and show that biperiden impairs production of sequential finger movements following a fixed but not a random sequence. A similar interaction was observed in widespread oscillatory broadband power changes (4-25 Hz) in the motor sequence learning network after receiving biperiden, with greater power in the theta, alpha, and beta bands over ipsilateral motor and bilateral parietal–occipital areas. The reduced theta power during a fixed compared to random sequence, likely reflecting disengagement of top-down attention to sensory processes, was disrupted by biperiden. The alpha synchronization during learned sequences, reflecting sensory gating and lower visuospatial attention requirements for the learned, compared with visuomotor responses to a random sequence, was greater after biperiden, potentially reflecting excessive visuospatial attention reduction following biperiden, also affecting visuomotor responding required to enable sequence learning. Beta oscillations facilitate sequence learning by integrating visual and somatosensory inputs, stabilizing learned sequences, and promoting prediction of the next stimulus. The beta synchronization after biperiden fits with a disruption of the selective visuospatial attention enhancement associated with initial sequence learning. These findings highlight the role of cholinergic processes in motor sequence learning.

With the discovery of event-related potentials elicited by errors more than thirty years ago, a new avenue of research on performance monitoring, cognitive control, and decision making was opened. Since then, the field has developed and expanded fulminantly. After a brief overview on the EEG correlates of performance monitoring, this article reviews recent advancements in the field of performance monitoring based on single-trial analyses using independent component analysis, multiple regression, and multivariate pattern classification. Given the close interconnection between performance monitoring and reinforcement learning, computational modeling and model-based EEG analyses have made a particularly strong impact. The reviewed findings demonstrate that error- and feedback-related EEG dynamics represent variables reflecting how performance monitoring signals are weighted and transformed into an adaptation signal that guides future decisions and actions. The model-based single-trial analysis approach goes far beyond conventional peak-and-trough analyses of event-related potentials and enables testing mechanistic theories of performance monitoring, cognitive control and decision making.

Deficits in reward learning are core symptoms across many mental disorders. Recent work suggests that such learning impairments arise by a diminished ability to use reward history to guide behaviour, but the neuro-computational mechanisms through which these impairments emerge remain unclear. Moreover, limited work has taken a transdiagnostic approach to investigate whether the psychological and neural mechanisms that give rise to learning deficits are shared across forms of psychopathology. To provide insight into this issue, we explored probabilistic reward learning in patients diagnosed with major depressive disorder (n = 33) or schizophrenia (n = 24) and 33 matched healthy controls by combining computational modelling and single-trial EEG regression. In our task, participants had to integrate the reward history of a stimulus to decide whether it is worthwhile to gamble on it. Adaptive learning in this task is achieved through dynamic learning rates that are maximal on the first encounters with a given stimulus and decay with increasing stimulus repetitions. Hence, over the course of learning, choice preferences would ideally stabilize and be less susceptible to misleading information. We show evidence of reduced learning dynamics, whereby both patient groups demonstrated hypersensitive learning (i.e. less decaying learning rates), rendering their choices more susceptible to misleading feedback. Moreover, there was a schizophrenia-specific approach bias and a depression-specific heightened sensitivity to disconfirmational feedback (factual losses and counterfactual wins). The inflexible learning in both patient groups was accompanied by altered neural processing, including no tracking of expected values in either patient group. Taken together, our results thus provide evidence that reduced trial-by-trial learning dynamics reflect a convergent deficit across depression and schizophrenia. Moreover, we identified disorder distinct learning deficits.

Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.

AbstractThe occurrence of tics in Tourette syndrome (TS) has often been linked to impaired cognitive control, but empirical findings are still inconclusive. A recent view proposes that tics may be the result of an abnormally strong interrelation between perceptual processes and motor actions, commonly referred to as perception-action binding. The general aim of the present study was to examine proactive control and binding effects in the context of task switching in adult human patients with TS and matched healthy controls. A cued task switching paradigm was employed in 24 patients (18 male, 6 female) and 25 controls while recording electroencephalography (EEG). Residue iteration decomposition (RIDE) was applied to analyze cue-locked proactive cognitive control and target-locked binding processes. Behavioral task switching performance was unaltered in patients with TS. A cue-locked parietal switch positivity, reflecting proactive control processes involved in the reconfiguration of the new task did not differ between groups. Importantly, target-locked fronto-central (N2) and parietal (P3) modulations, reflecting binding processes between perception and action, differed between groups. Underlying neurophysiological processes were best depicted after temporal decomposition of the EEG signal. The present results argue for unaltered proactive control but altered perception-action binding processes in the context of task switching, supporting the view that the integration of perception and action is processed differently in patients TS. Future studies should further investigate the specific conditions under which binding may be altered in TS and the influence of top-down processes, such as proactive control, on bindings.

Stroke survivors not only suffer from severe motor, speech and neurocognitive deficits, but in many cases also from a “lack of pleasure” and a reduced motivational level. Especially apathy and anhedonic symptoms can be linked to a dysfunction of the reward system. Rewards are considered as important co-factor for learning, so the question arises as to why and how this affects the rehabilitation of stroke patients.We investigated reward behaviour, learning ability and brain network connectivity in acute (3-7d) mild to moderate stroke patients (n = 28) and age-matched healthy controls (n = 26). Reward system activity was assessed using the Monetary Incentive Delay task (MID) during magnetoencephalography (MEG). Coherence analyses were used to demonstrate reward effects on brain functional network connectivity.The MID-task showed that stroke survivors had lower reward sensitivity and required greater monetary incentives to improve performance and showed deficits in learning improvement. MEG-analyses showed a reduced network connectivity in frontal and temporoparietal regions. All three effects (reduced reward sensitivity, reduced learning ability and altered cerebral connectivity) were found to be closely related and differed strongly from the healthy group.Our results reinforce the notion that acute stroke induces reward network dysfunction, leading to functional impairment of behavioural systems. These findings are representative of a general pattern in mild strokes and are independent of the specific lesion localisation. For stroke rehabilitation, these results represent an important point to identify the reduced learning capacity after stroke and to implement individualised recovery exercises accordingly.