Vasta Nicola, Xu Shengjie, Verguts Tom, Braem Senne
Department of Psychology and Cognitive Science, University of Trento, Corso Bettini, 31, 38068, Rovereto, TN, Italy.
Department of Experimental Psychology, Ghent University, Ghent, Belgium.
Mem Cognit. 2025 Apr;53(3):1008-1021. doi: 10.3758/s13421-024-01626-4. Epub 2024 Aug 28.
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.
认知控制是指超越优势反应倾向以实现目标导向行为的能力。另一方面，强化学习是指通过反馈和奖励来学习行为。尽管认知控制和强化学习在驱动行为方面常被视为相反的力量，但最近的理论强调了它们潜在过程中可能存在的相似性。通过这项研究，我们旨在一方面调查在控制学习过程中是否能观察到类似的整合时间窗口，另一方面调查强化学习范式中的学习率。为此，我们对一个大型公共数据集（n = 522）进行了相关分析，该数据集包括来自两个强化学习任务的数据，即概率选择任务和概率威斯康星卡片分类任务（WCST），以及来自一个经典冲突任务（即斯特鲁普任务）的数据。结果显示，在概率WCST中，控制指标的时间尺度与学习率之间存在预期的相关性。此外，两个强化学习任务的学习率参数彼此不相关。这些发现共同表明，这两个传统上不同的领域依赖于一种共享的学习机制，同时强调价值更新过程仍然可能非常依赖于特定任务。我们推测，斯特鲁普任务和WCST中的更新过程可能更相关，因为这两个任务都需要对刺激特征（如颜色、词义、图案、形状）进行特定任务的更新，而不是对刺激身份进行更新。
