Human language learning differs significantly across individuals in the process and ultimate attainment. Although decades of research exploring the neural substrates of language learning have identified distinct and overlapping neural networks subserving learning of different components, the neural mechanisms that drive the large interindividual differences are still far from being understood. Here we examine to what extent the neural dynamics of multiple brain networks in men and women across sessions of training contribute to explaining individual differences in learning multiple linguistic components (i.e., vocabulary, morphology, and phrase and sentence structures) of an artificial language in a 7 d training and imaging paradigm with functional MRI. With machine-learning and predictive modeling, neural activation patterns across training sessions were highly predictive of individual learning success profiles derived from the four components. We identified four neural learning networks (i.e., the Perisylvian, frontoparietal, salience, and default-mode networks) and examined their dynamic contributions to the learning success prediction. Moreover, the robustness of the predictions systematically changes across networks depending on specific training phases and the learning components. We further demonstrate that a subset of network nodes in the inferior frontal, insular, and frontoparietal regions increasingly represent newly acquired language knowledge, while the multivariate connectivity between these representation regions is enhanced during learning for more successful learners. These findings allow us to understand why learners differ and are the first to attribute not only the degree of success but also patterns of language learning across components, to neural fingerprints summarized from multiple neural network dynamics.

SIGNIFICANCE STATEMENT Individual differences in learning a language are widely observed not only within the same component of language but also across components. This study demonstrates that the dynamics of multiple brain networks across four imaging sessions of a 7 d artificial language training contribute to individual differences in learning-outcome profiles derived from four language components. With machine-learning predictive modeling, we identified four neural learning networks, including the Perisylvian, frontoparietal, salience, and default-mode networks, that contribute to predicting individual learning-outcome profiles and revealed language-component-general and component-specific prediction patterns across training sessions. These findings provide significant insights in understanding training-dependent neural dynamics underlying individual differences in learning success across language components.

人类语言学习的过程和最终成就因人而异。尽管数十年来探索语言学习的神经基础的研究已经确定了支持不同组件学习的不同和重叠的神经网络，但驱动个体间巨大差异的神经机制仍远未得到理解。在这里，我们研究了在训练期间男性和女性的多个大脑网络的神经动力学在多大程度上有助于解释在学习人工语言的多种语言成分（即词汇、形态、短语和句子结构）时的个体差异。具有功能性 MRI 的 7 d 训练和成像范例。通过机器学习和预测建模，整个培训课程中的神经激活模式高度预测来自四个组成部分的个人学习成功情况。我们确定了四个神经学习网络（即 Perisylvian、frontoparietal、salience 和 default-mode 网络）并检查了它们对学习成功预测的动态贡献。此外，预测的鲁棒性会根据特定的训练阶段和学习组件系统地跨网络发生变化。我们进一步证明，下额叶、岛叶和额顶叶区域中的网络节点子集越来越多地表示新获得的语言知识，而这些表示区域之间的多元连接在学习过程中得到了增强，以帮助更成功的学习者。

重要性声明学习一门语言的个体差异不仅在语言的同一组成部分内而且在不同组成部分之间都被广泛观察到。这项研究表明，在 7 天人工语言训练的四个成像会话中，多个大脑网络的动态会导致源自四种语言成分的学习结果概况的个体差异。通过机器学习预测建模，我们确定了四个神经学习网络，包括 Perisylvian、frontoparietal、salience 和 default-mode 网络，它们有助于预测个人学习结果概况并揭示语言组件的一般和组件特定的预测模式在整个培训课程中。