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.