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eNeuro. 2017 May 26;4(3). pii: ENEURO.0061-17.2017. doi: 10.1523/ENEURO.0061-17.2017. eCollection 2017 May-Jun.

Proactive Control: Neural Oscillatory Correlates of Conflict Anticipation and Response Slowing.

Chang A1, Ide JS2, Li HH1, Chen CC1,3, Li CR2,4,5,6.

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Department of Psychology, National Taiwan University, Taipei, Taiwan 10617.
Department of Psychiatry, Yale University, New Haven, CT 06520.
Center for Neurobiology and Cognitive Science, National Taiwan University, Taipei, Taiwan 10617.
Department of Neuroscience, Yale University, New Haven, CT 06520.
Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520.
Beijing Huilongguan Hospital, Beijing 100096, China.


Proactive control allows us to anticipate environmental changes and adjust behavioral strategy. In the laboratory, investigators have used a number of different behavioral paradigms, including the stop-signal task (SST), to examine the neural processes of proactive control. Previous functional MRI studies of the SST have demonstrated regional responses to conflict anticipation-the likelihood of a stop signal or P(stop) as estimated by a Bayesian model-and reaction time (RT) slowing and how these responses are interrelated. Here, in an electrophysiological study, we investigated the time-frequency domain substrates of proactive control. The results showed that conflict anticipation as indexed by P(stop) was positively correlated with the power in low-theta band (3-5 Hz) in the fixation (trial onset)-locked interval, and go-RT was negatively correlated with the power in delta-theta band (2-8 Hz) in the go-locked interval. Stimulus prediction error was positively correlated with the power in the low-beta band (12-22 Hz) in the stop-locked interval. Further, the power of the P(stop) and go-RT clusters was negatively correlated, providing a mechanism relating conflict anticipation to RT slowing in the SST. Source reconstruction with beamformer localized these time-frequency activities close to brain regions as revealed by functional MRI in earlier work. These are the novel results to show oscillatory electrophysiological substrates in support of trial-by-trial behavioral adjustment for proactive control.


Bayesian model; Electroencephalogram (EEG); neural oscillation; proactive control; stop-signal task

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