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Updated: May 16, 2026

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task
Published on: May 24, 2020
Trial-By-Trial Changes in Neural Indices of Performance Monitoring Uniquely Correspond to Behavioral Adjustments
Miranda C Lutz1, Bohyun Park2, Philippe Rast3
1Department of Psychology, Education & Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands.
Abstract:
Behavioral and neural indices of performance monitoring are key to understanding behavioral adaptation during task performance. However, associations between performance monitoring event-related potentials (ERPs) and task behavior have been inconsistent. This inconsistency may partly reflect reliance on single-subject averages that obscure trial-by-trial changes in ERPs and behavior, and a tendency to examine only one or two ERP indices at a time. Our objective was to uncover how neural variability during performance monitoring contributes to behavioral adaptation, revealing variability as a functional signature of cognitive control. We investigated whether current-trial response times (RTs) and accuracy can be predicted from previous- and current-trial congruency and accuracy and ERP indices of performance monitoring (N2, P3, error-related negativity [ERN], error positivity, [Pe]). Flanker data from 291 healthy participants (54% female) were analyzed using multilevel location-scale modeling. This modeling framework facilitates simultaneous examination of mean and variance relationships of single-trial data. Previous- and current-trial ERP amplitudes uniquely predict current-trial RTs and accuracy, beyond previous- and current-trial congruency and accuracy effects. Previous- and current-trial N2, P3, ERN, and Pe were concurrently related to the mean and variance of RTs and to accuracy. The observed within-person changes in the relationship between performance-monitoring ERPs and task behavior indicate that trial-by-trial neural fluctuations reflect dynamic adjustments in cognitive control across successive actions. These findings demonstrate the value of modeling intraindividual variability in neurophysiological measures to understand adaptive behavior.
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