Beyond peaks and troughs: Multiplexed performance monitoring signals in the EEG

Abstract With the discovery of event‐related potentials elicited by errors more than 30 years ago, a new avenue of research on performance monitoring, cognitive control, and decision making emerged. 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 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.