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Abstract
The anterior cingulate cortex (ACC) and the related medial wall play a critical role in recruiting cognitive control. Although ACC exhibits selective error and conflict responses, it has been unclear how these develop and become context-specific. With use of a modified stop-signal task, we show from integrated computational neural modeling and neuroimaging studies that ACC learns to predict error likelihood in a given context, even for trials in which there is no error or response conflict. These results support a more general error-likelihood theory of ACC function based on reinforcement learning, of which conflict and error detection are special cases.
