Effects of conflict and strategic processing on neural responses to errors in schizophrenia

Beyond a blunted ERN - Biobehavioral correlates of performance monitoring in schizophrenia

Cognitive deficits are well documented in schizophrenia. Here, we reviewed alterations in performance monitoring as potential marker of cognitive deficits in schizophrenia. We found that performance monitoring alterations in schizophrenia are specific to early (indexed by blunted error-related negativity (ERN)) and late (reflected in blunted error positivity (Pe)) internal error processing, while external performance feedback processing in simple response feedback tasks is relatively preserved. We propose, that these performance monitoring deficits may best be interpret as one aspect of disrupted theta band (4-8 Hz) oscillations over medial frontal recordings sites. Midfrontal theta dynamics are an increasingly established direct neural index of the recruitment of cognitive control and are impaired in several clinical populations. While theta-related ERPs (the ERN) may be an easy to assess marker of cognitive deficits in schizophrenia, further work investigating the trial-by-trial dynamics of theta in both the time and time-frequency domain is needed to parse cognitive deficits in schizophrenia into finer levels of detail and evaluate theta modulation as a therapeutic tool.

The effect of static and dynamic visual stimulations on error-evoked brain responses

Error-related potentials (ErrPs) can reflect the brain's response to errors. Recently, it has been used in the studies on neural mechanisms of human cognition, such as error detection and conflict monitoring. Moreover, ErrPs have provided technical support for the development of brain-computer interface (BCI). However, the different effects of visual stimulation modes (dynamic or static) on ErrPs have not been revealed. This may seriously affect the recognition accuracy of the ErrPs in practical applications. Therefore, the aim of this study was to investigate how people respond to different types of visual stimulations. Nineteen participants were recruited in the ErrPs-based tasks with two visual stimulation modes (dynamic and static). The ErrPs were analyzed and the feature values (N1, P2, P3, N6 and P8, named by the occurrence time) were statistically compared. The results showed that the difference between correctness and error was reflected in P3, N6, P8 in dynamic stimulation; and N1, P3, N6 and P8 in static stimulation. In the event-related potential based on error, the differences between dynamic and static tasks were reflected in N1 and P2. In conclusion, this study found that the features with later occurrence were significantly affected by correctness and error in both cases, while the error-related change in N1 only existed under the static stimulation. We also found that the recognition of stimulation modes came earlier within about 300 ms after the start of visual stimulation.

The Effect of Static and Dynamic Visual Stimulations on Error Detection Based on Error-Evoked Brain Responses

Error-related potentials (ErrPs) have provided technical support for the brain-computer interface. However, different visual stimulations may affect the ErrPs, and furthermore, affect the error recognition based on ErrPs. Therefore, the study aimed to investigate how people respond to different visual stimulations (static and dynamic) and find the best time window for different stimulation. Nineteen participants were recruited in the ErrPs-based tasks with static and dynamic visual stimulations. Five ErrPs were statistically compared, and the classification accuracies were obtained through linear discriminant analysis (LDA) with nine different time windows. The results showed that the P3, N6, and P8 with correctness were significantly different from those with error in both stimulations, while N1 only existed in static. The differences between dynamic and static errors existed in N1 and P2. The highest accuracy was obtained in the time window related to N1, P3, N6, and P8 for the static condition, and in the time window related to P3, N6, and P8 for the dynamic. In conclusion, the early components of ErrPs may be affected by stimulation modes, and the late components are more sensitive to errors. The error recognition with static stimulation requires information from the entire epoch, while the late windows should be focused more within the dynamic case.

Differentiating electrophysiological indices of internal and external performance monitoring: Relationship with perfectionism and locus of control

The impact of individual differences on performance monitoring and psychopathology is a question of active debate. Personality traits associated with psychopathology may be related to poor internal performance monitoring (as measured by the error-related negativity [ERN]) but intact external performance monitoring (as measured by the reward positivity [RewP]), suggesting that there are underlying neural differences between internal and external performance monitoring processes. We tested the relationships between individual difference measures of perfectionism, locus of control, and ERN, error-positivity (Pe), and RewP component difference amplitude in a healthy undergraduate sample. A total of 128 participants (69 female, M(SD)age = 20.6(2.0) years) completed two tasks: a modified version of the Eriksen Flanker and a doors gambling task along with the Frost Multidimensional Perfectionism scale, the Rotter Locus of Control scale, and the Levenson Multidimensional Locus of Control scale to quantify perfectionism and locus of control traits, respectively. Linear regressions adjusting for age and gender showed that neither ΔERN nor ΔRewP amplitude were significantly moderated by perfectionism or locus of control scores. Findings suggest that, in psychiatrically-healthy individuals, there is not a strong link between perfectionism, locus of control, and ERN or RewP amplitude. Future research on individual difference measures in people with psychopathology may provide further insight into how these personality traits affect performance monitoring.