A shared temporal window of integration across cognitive control and reinforcement learning paradigms: A correlational study

Cognitive control refers to the ability to override prepotent response tendencies to achieve goal-directed behavior. On the other hand, reinforcement learning refers to the learning of actions through feedback and reward. Although cognitive control and reinforcement learning are often viewed as opposing forces in driving behavior, recent theories have emphasized possible similarities in their underling processes. With this study, we aimed to investigate whether a similar time window of integration could be observed during the learning of control on the one hand, and the learning rate in reinforcement learning paradigms on the other. To this end, we performed a correlational analysis on a large public dataset (n = 522) including data from two reinforcement learning tasks, i.e., a probabilistic selection task and a probabilistic Wisconsin Card Sorting Task (WCST), and data from a classic conflict task (i.e., the Stroop task). Results showed expected correlations between the time scale of control indices and learning rate in the probabilistic WCST. Moreover, the learning-rate parameters of the two reinforcement learning tasks did not correlate with each other. Together, these findings suggest a reliance on a shared learning mechanism between these two traditionally distinct domains, while at the same time emphasizing that value updating processes can still be very task-specific. We speculate that updating processes in the Stroop and WCST may be more related because both tasks require task-specific updating of stimulus features (e.g., color, word meaning, pattern, shape), as opposed to stimulus identity.