Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate-on 2 different cohorts-that a temporal difference model, which relies on prediction errors, accounts for participants' online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

中文翻译：

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语言统计学习响应植根于纹状体的强化学习原则。

统计学习 (SL) 是从环境中提取规律的能力。在语言领域，这种能力是学习单词和结构规则的基础。由于缺乏可靠的在线测量，统计词和规则学习主要使用离线（后熟悉）测试进行研究，这对 SL 的动态及其神经基础的了解有限。在这里，我们利用跟踪简单句法结构的在线 SL 并结合计算建模的新任务来表明在线 SL 响应植根于纹状体功能的强化学习原则。具体来说，我们在 2 个不同的队列中证明了依赖于预测误差的时间差异模型可以解释参与者的在线学习行为。然后我们表明，通过学习进行的逐次试验发展与腹侧和背侧纹状体的活动密切相关。因此，我们的结果提供了与语言相关的 SL 的详细机械解释，并解释了纹状体在 SL 任务中经常被引用的含义。因此，这项工作弥合了语言学习和强化学习现象之间长期存在的差距。