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We all have experienced that the amount of effort required to perform a task can rapidly decrease over the course of practice. Previous studies have shown that short-term automatization of stimulus-response transformations is associated with a reorganization of functional coupling between different large-scale brain networks. However, it has remained an open question how changing connectivity patterns translate into more efficient stimulus-response processing over the course of learning. Here, we employed a control-theoretic approach to test the hypothesis that the amount of control energy required for stimulus-response processing decreases from early to late practice for networks involved in task control. Using fMRI data from a learning group, N = 70, and a control group, N = 67, stimulus-response transformations were modeled as trajectories of activity starting in the visual network and ending in the sensorimotor network. The stimulusresponse trajectories were determined by the functional connectivity matrices derived from the fMRI data plus additional control activation exerted by task-related networks. Based on this analysis approach, we found that the cingulo-opercular network can control stimulus-response transformations with increasing efficiency over the course of learning, while no change in control energy was observed for the fronto-parietal network, highlighting the central role of the cinguloopercular network for short-term task automatization.

The cingulo-opercular network controls stimulus-response transformations with increasing efficiency over the course of learning