Modeling electrophysiological measures of decision‐making and performance monitoring in neurotypical children engaging in a speeded flanker task

This study aims to use structural equation modeling (SEM) to investigate the role of error processing in behavioral adaptation in children by testing relationships between error‐related and stimulus‐related event‐related potentials (ERPs) obtained from two sessions of a speeded Eriksen flanker task. First, path models of averaged ERP components and mean response times (N1 → P2 → N2 → P3 → RTs) while controlling for trait effects, age, and sex, on each was examined separately for correct and incorrect trials from each session. While the model demonstrated acceptable fit statistics, the four models yielded diverse results. Next, path models for correct and incorrect trials were tested using latent variables defined by factoring together respective measures of ERP component amplitudes from each session. Comparison of correct and incorrect models revealed significant differences in the relationships between the successive measures of neural processing after controlling for trait effects. Moreover, latent variable models controlling for both trait and session‐specific state variables yielded excellent model fit while models without session‐specific state variables did not. In the final model, the error‐related neural activity (i.e., the ERN and Pe) from incorrect trials was found to significantly relate to the stream of neural processes contributing to trials with the correct behavior. Importantly, the relationship between RT and error detection in the final model signifies a brain‐and‐behavior feedback loop. These findings provided empirical evidence that supports the adaptive orienting theory of error processing by demonstrating how the neural signals of error processing influence behavioral adaptations that facilitate correct behavioral performance. Our research builds on Wessel’s adaptive orienting theory where post‐error psychological processes of automatic inhibition and attentional orientation lead to improved performance accuracy. Novel use of structural equation modeling of correct trials demonstrates that phases of brain activity predict response time. Additionally, response time predicts performance monitoring (ERN/PE amplitudes) which in turn predicts attention‐based components (N1/N2 amplitudes) in the brain processing of correct trials supporting the adaptive orienting theory.