Reward masks the learning of cognitive control demand

Cognitive control refers to a set of cognitive functions that modulate other cognitive processes to align with internal goals. Recent research has shown that cognitive control can flexibly adapt to internal and external factors such as reward, effort, and environmental demands. This suggests that learning processes track changes in these factors and drive an optimization process to determine how cognitive control should be applied in changing situations. In real life, multiple factors often simultaneously affect how cognitive control is deployed. However, previous studies mainly concern how cognitive control adjusts to changes in a single factor. Here, we investigate how cognitive control learns to adjust to two concurrently changing factors: statistical regularity in cognitive control demand and performance-contingent reward. We consider two competing hypotheses: reward promotes cognitive control to adjust to cognitive control demand, and the processing of reward information obstructs the adaptation to cognitive control demand. In our experiment, statistical regularity in cognitive control demand is manipulated within subjects such that some stimuli require higher levels of cognitive control than others. Reward is manipulated across subjects. Using a computational model that captures temporal changes in cognitive control, we find that in the absence of reward, participants can adjust to different levels of cognitive control demand. Importantly, when performance-contingent reward is available, participants fail to adapt to changes in cognitive control demand. The findings support the hypothesis that reward blocks the learning of cognitive control.