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

Autor: Ullsperger M; Department of Neuropsychology, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.; Center for Behavioral Brain Sciences, Magdeburg, Germany.; German Center for Mental Health (DZPG), partner site Halle-Jena-Magdeburg, Magdeburg, Germany.; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Halle-Jena-Magdeburg, Germany.
Jazyk: angličtina
Zdroj: Psychophysiology [Psychophysiology] 2024 Jul; Vol. 61 (7), pp. e14553. Date of Electronic Publication: 2024 Feb 28.
DOI: 10.1111/psyp.14553
Abstrakt: 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.
(© 2024 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)
Databáze: MEDLINE
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