Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as actively maintaining precise information in working memory (WM). It is commonly thought that WM and PFC show more protracted development than RL and BG systems, yet their contributions are rarely assessed in tandem. Here, we used a simple learning task to test how RL and WM contribute to changes in learning across adolescence. We tested 187 subjects ages 8 to 17 and 53 adults (25-30). Participants learned stimulus-action associations from feedback; the learning load was varied to be within or exceed WM capacity. Participants age 8-12 learned slower than participants age 13-17, and were more sensitive to load. We used computational modeling to estimate subjects’ use of WM and RL processes. Surprisingly, we found more protracted changes in RL than WM during development. RL learning rate increased with age until age 18 and WM parameters showed more subtle, gender- and puberty-dependent changes early in adolescence. These results can inform education and intervention strategies based on the developmental science of learning.

多个神经认知系统同时有助于学习。例如，多巴胺和基底神经节（BG）系统被认为通过增量更新选择的值来支持强化学习（RL），而前额叶皮层（PFC）则贡献不同的计算，例如主动维护工作记忆（WM）中的精确信息）。人们普遍认为，WM 和 PFC 比 RL 和 BG 系统的发展时间更长，但很少同时评估它们的贡献。在这里，我们使用一个简单的学习任务来测试 RL 和 WM 如何促进青春期学习的变化。我们测试了 187 名 8 至 17 岁的受试者和 53 名成年人 (25-30)。参与者从反馈中学习刺激与行动的关联；学习负荷变化在 WM 能力之内或之上。8-12 岁的参与者学习速度比 13-17 岁的参与者慢，并且对负荷更敏感。我们使用计算模型来估计受试者对 WM 和 RL 过程的使用。令人惊讶的是，我们发现在开发过程中 RL 的变化比 WM 的变化更持久。RL 学习率随着年龄的增长而增加，直到 18 岁，而 WM 参数在青春期早期表现出更微妙的、性别和青春期依赖性的变化。这些结果可以为基于学习发展科学的教育和干预策略提供信息。