Over the past decades, research employing artificial grammar, sequence learning, and statistical learning paradigms has flourished, not least because these methods appear to offer a window, albeit with a restricted view, on implicit learning processes underlying natural language learning. But these paradigms usually provide relatively little exposure, use meaningless stimuli, and do not even necessarily target natural language structures. So the question arises whether they engage the same brain regions as natural language. The aim of this review is to use data from brain imaging, brain stimulation, and the effects of brain damage to identify the main brain regions that show sensitivity to structural regularities in implicit learning paradigms and to consider their relationship to natural language processing and learning.

中文翻译：

---

内隐学习的神经科学

在过去的几十年中，采用人工语法，序列学习和统计学习范式的研究蓬勃发展，这不仅是因为这些方法似乎为自然语言学习的隐式学习过程提供了一个窗口，尽管视野有限。但是这些范例通常提供相对较少的曝光，使用无意义的刺激，甚至不一定以自然语言结构为目标。因此，出现了一个问题，即它们是否参与与自然语言相同的大脑区域。这篇综述的目的是使用来自脑成像，脑刺激和脑损伤影响的数据来识别对隐性学习范式中的结构规律敏感的主要大脑区域，并考虑它们与自然语言处理和学习的关系。