Correct response negativity may reflect subjective value of reaction time under regulatory fit in a speed-rewarded task

Response monitoring in math-anxious individuals in an arithmetic task

We examine whether math anxiety is related to altered response monitoring in an arithmetic task. Response-locked event-related brain potentials (ERPs) were evaluated in 23 highly (HMA) and 23 low math-anxious (LMA) individuals while they performed an arithmetic verification task. We focused on two widely studied ERPs elicited during error processing: error-related negativity (ERN) and error positivity (Pe). Correct-related negativity (CRN), an ERP elicited after a correct response, was also studied. The expected ERN following errors was found, but groups did not differ in its amplitude. Importantly, LMA individuals showed less negative CRN and more positive Pe amplitudes than their more anxious peers, suggesting more certainty regarding response accuracy and better adaptive behavioral adjustment after committing errors in an arithmetic task in the LMA group. The worse control over response performance and less awareness of correct responses in the HMA group might reduce their ability to 'learn from errors'.

On the Comparison Between the Nc/CRN and the Ne/ERN

After the Error Negativity (Ne or ERN) has been described on full-blown errors and on partial error, a smaller Error Negativity-like wave (CRN or Nc) has also been evidenced on correct trials, first in patients with schizophrenia and, later on, in healthy subjects. The functional significance of the Nc as compared to the Ne is of critical importance since most models accounting for the genesis of the Ne on errors and partial errors cannot account for the existence of the Nc if this Nc simply corresponds to a small Ne. On the contrary, if the Nc and the Ne are two completely distinct components, then the existence of a Nc poses no constraint to the existing models. To this end, we examine in the present review the similarities and the differences existing between the Ne and the Nc regarding their functional properties and their anatomical origin.

Combining Neural and Behavioral Measures Enhances Adaptive Training

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4–13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance.