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Temporal dynamics of flexible cognitive control
Journal article   Open access   Peer reviewed

Temporal dynamics of flexible cognitive control

Chengyuan Wu, Carol A. Seger, Yixuan Ku, Canhuang Luo, Ying Zhou, Jiefeng Jiang and Qi Chen
NeuroImage (Orlando, Fla.), Vol.328, 121787
03/01/2026
DOI: 10.1016/j.neuroimage.2026.121787
PMID: 41654283
url
https://doi.org/10.1016/j.neuroimage.2026.121787View
Published (Version of record) Open Access

Abstract

•Different oscillatory components associated with learning rate, predicted conflict level, and prediction error underlie flexible cognitive control.•Directional connectivity patterns among oscillatory components enable the coordination of task-relevant cognitive processes.•Directional connections that reflect updates of learning rate and predicted conflict level respectively provide neural signatures for reactive and proactive control. In dynamic environments, flexible cognitive control adaptively adjusts processing through proactive mechanisms deployed in advance and reactive mechanisms engaged upon conflict. Previous studies have primarily focused on identifying neural networks supporting specific control components, while less is known about how multiple components interact over time to support adaptive control. To characterize these temporal dynamics, we combined electroencephalography (EEG) recordings with a face-word Stroop paradigm under changing conflict environment. A hierarchical Bayesian model was used to estimate trial-wise learning rate, predicted conflict level, and prediction error, providing computational indices of cognitive control flexibility. Neural correlation analysis indicated that these variables correlated with Theta, Alpha, and Beta oscillations in distinct brain regions. Granger causality analyses revealed connectivity patterns among these regions that varied across different task phase. Furthermore, connections reflecting updates to predicted conflict level prior to stimulus onset indexed individual strength in proactive control, while connections reflecting learning rate updates after stimulus onset indexed reactive control. These findings highlight how oscillatory dynamics coordinate multiple control components and provide new insight into how proactive and reactive control emerge as distinct modes within this interconnected neural architecture of flexible cognitive control.
 Cognitive control flexibility Hierarchical Bayesian model Proactive control Reactive control Temporal dynamics

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