Department of Psychology, New York University, New York, New York, United States of America.
Cognition and Brain Plasticity Unit, IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.
PLoS Biol. 2021 Sep 7;19(9):e3001119. doi: 10.1371/journal.pbio.3001119. eCollection 2021 Sep.
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.
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方法，统计单词和规则学习主要是通过离线（熟悉后）测试进行研究，这使得对 SL 的动态及其神经基础的了解非常有限。在这里，我们利用一项新颖的任务，该任务跟踪简单句法结构的在线 SL，并结合计算建模，表明在线 SL 响应根植于纹状体功能的强化学习原则。具体来说，我们证明了基于预测误差的时间差分模型可以解释参与者的在线学习行为。然后，我们表明，通过学习来逐步发展预测，与腹侧和背侧纹状体的活动密切相关。因此，我们的研究结果为语言相关的 SL 提供了详细的机制解释，并为纹状体在 SL 任务中的常见影响提供了一种解释。因此，这项工作弥合了语言学习和强化学习现象之间由来已久的差距。
