Early correlates of error-related brain activity predict subjective timing of error awareness

Social power modulates individuals' neural responses to monetary and social rewards

Although previous research has shown that social power modulates individuals' sensitivity to rewards, it is currently unclear whether social power increases or decreases individuals' sensitivity to rewards. This study employed event-related potentials (ERPs) to investigate the effects of social power on individuals' neural responses to monetary and social rewards. Specifically, participants underwent an episodic priming task to manipulate social power (high-power vs. low-power) and then completed monetary and social delayed incentive tasks while their behavioral responses and electroencephalograms (EEG) were recorded. According to ERP analysis, during the anticipatory stage, low-power individuals exhibited a greater cue-P3 amplitude than high-power individuals in both monetary and social tasks. In the consummatory stage, though no impact of social power on the reward positivity (RewP) was found, low-power individuals showed a higher feedback-P3 (FB-P3) amplitude than high-power individuals, regardless of task types (the MID and SID tasks). In conclusion, these results provide evidence that social power might decrease one's sensitivity to monetary and social rewards in both the anticipatory and consummatory stages.

Error-related cardiac deceleration: Functional interplay between error-related brain activity and autonomic nervous system in performance monitoring

Coordinated interactions between the central and autonomic nervous systems are crucial for survival due to the inherent propensity for human behavior to make errors. In our ever-changing environment, when individuals make mistakes, these errors can have life-threatening consequences. In response to errors, specific reactions occur in both brain activity and heart rate to detect and correct errors. Specifically, there are two brain-related indicators of error detection and awareness known as error-related negativity and error positivity. Conversely, error-related cardiac deceleration denotes a momentary slowing of heart rate following an error, signaling an autonomic response. However, what is the connection between the brain and the heart during error processing? In this review, we discuss the functional and neuroanatomical connections between the brain and heart markers of error processing, exploring the experimental conditions in which they covary. Given the current limitations of available data, future research will continue to investigate the neurobiological factors governing the brain-heart interaction, aiming to utilize them as combined markers for assessing cognitive control in healthy and pathological conditions.

Slip or fallacy? Effects of error severity on own and observed pitch error processing in pianists

Errors elicit a negative, mediofrontal, event-related potential (ERP), for both own errors (error-related negativity; ERN) and observed errors (here referred to as observer mediofrontal negativity; oMN). It is unclear, however, if the action-monitoring system codes action valence as an all-or-nothing phenomenon or if the system differentiates between errors of different severity. We investigated this question by recording electroencephalography (EEG) data of pianists playing themselves (Experiment 1) or watching others playing (Experiment 2). Piano pieces designed to elicit large errors were used. While active participants' ERN amplitudes differed between small and large errors, observers' oMN amplitudes did not. The different pattern in the two groups of participants was confirmed in an exploratory analysis comparing ERN and oMN directly. We suspect that both prediction and action mismatches can be coded in action monitoring systems, depending on the task, and a need-to-adapt signal is sent whenever mismatches happen to indicate the magnitude of the needed adaptation.

Two Oscillatory Correlates of Attention Control in the Alpha-Band with Distinct Consequences on Perceptual Gain and Metacognition

Behavioral consequences and neural underpinnings of visuospatial attention have long been investigated. Classical studies using the Posner paradigm have found that visual perception systematically benefits from the use of a spatially informative cue pointing to the to-be-attended spatial location, compared with a noninformative cue. Lateralized α amplitude modulation during visuospatial attention shifts has been suggested to account for such perceptual gain. However, recent studies on spontaneous fluctuations of prestimulus α amplitude have challenged this notion. These studies showed that spontaneous fluctuations of prestimulus α amplitude were associated with the subjective appreciation of stimulus occurrence, while objective accuracy was instead best predicted by the frequency of α oscillations, with faster prestimulus α frequency accounting for better perceptual performance. Here, in male and female humans, by using an informative cue in anticipation of lateralized stimulus presentation, we found that the predictive cue not only modulates preparatory α amplitude but also α frequency in a retinotopic manner. Behaviorally, the cue significantly impacted subjective performance measures (metacognitive abilities [meta-d']) and objective performance gain (d'). Importantly, α amplitude directly accounted for confidence levels, with ipsilateral synchronization and contralateral desynchronization coding for high-confidence responses. Crucially, the contralateral α amplitude selectively predicted interindividual differences in metacognitive abilities (meta-d'), thus anticipating decision strategy and not perceptual sensitivity, probably via excitability modulations. Instead, higher perceptual accuracy both within and across participants (d') was associated with faster contralateral α frequency, likely by implementing higher sampling at the attended location. These findings provide critical new insights into the neural mechanisms of attention control and its perceptual consequences.SIGNIFICANCE STATEMENT Prior knowledge serves the anticipation of sensory input to reduce sensory ambiguity. The growing interest in the neural mechanisms governing the integration of sensory input into our internal representations has highlighted a pivotal role of brain oscillations. Here we show that distinct but interacting oscillatory mechanisms are engaged during attentional deployment: one relying on α amplitude modulations and reflecting internal decision processes, associated with subjective perceptual experience and metacognitive abilities; the other relying on α frequency modulations and enabling mechanistic sampling of the sensory input at the attended location to influence objective performance. These insights are crucial for understanding how we reduce sensory ambiguity to maximize the efficiency of our conscious experience, but also in interpreting the mechanisms of atypical perceptual experiences.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

Error cancellation

The human cognitive system houses efficient mechanisms to monitor ongoing actions. Upon detecting an erroneous course of action, these mechanisms are commonly assumed to adjust cognitive processing to mitigate the error's consequences and to prevent future action slips. Here, we demonstrate that error detection has far earlier consequences by feeding back directly onto ongoing motor activity, thus cancelling erroneous movements immediately. We tested this prediction of immediate auto-correction by analysing how the force of correct and erroneous keypress actions evolves over time while controlling for cognitive and biomechanical constraints relating to response time and the peak force of a movement. We conclude that the force profiles are indicative of active cancellation by showing indications of shorter response durations for errors already within the first 100 ms, i.e. between the onset and the peak of the response, a timescale that has previously been related solely to error detection. This effect increased in a late phase of responding, i.e. after response force peaked until its offset, further corroborating that it indeed reflects cancellation efforts instead of consequences of planning or initiating the error.