Abstract：
The human brain can detect statistical regularities in the environment across a wide variety of contexts. The importance of this process is well-established not just in language acquisition but across different modalities; in addition, several neural correlates of statistical learning have been identified. A current technique for tracking the emergence of regularity learning and localizing its neural background is frequency tagging (FT). FT can detect neural entrainment not only to the frequency of stimulus presentation but also to that of a hidden structure. Auditory learning paradigms with linguistic and nonlinguistic stimuli, along with a visual paradigm using nonlinguistic stimuli, have already been tested with FT. To complete the picture, we conducted an FT experiment using written syllables as stimuli and a hidden triplet structure. Both behavioral and neural entrainment data showed evidence of structure learning. In addition, we localized two electrode clusters related to the process, which spread across the frontal and parieto-occipital areas, similar to previous findings. Accordingly, we conclude that fast-paced visual linguistic regularities can be acquired and are traceable through neural entrainment. In comparison with the literature, our findings support the view that statistical learning involves a domain-general network.
摘要：
人类大脑可以在各种各样的背景下检测环境中的统计规律。这个过程的重要性不仅在语言习得中，而且在不同的模式中都得到了确立；此外，已经确定了几种统计学习的神经相关因素。用于跟踪规律性学习的出现并定位其神经背景的当前技术是频率标记（FT）。FT不仅可以检测刺激呈现的频率，还可以检测到隐藏结构的神经夹带。具有语言和非语言刺激的听觉学习范式，以及使用非语言刺激的视觉范式，已经用FT进行了测试。为了完成图片，我们使用书面音节作为刺激和隐藏的三元组结构进行了FT实验。行为和神经夹带数据均显示出结构学习的证据。此外,我们定位了与该过程相关的两个电极簇，分布在额叶和顶枕区，类似于以前的发现。因此，我们得出的结论是，可以获得快节奏的视觉语言规律性，并且可以通过神经夹带进行追踪。与文献相比，我们的研究结果支持统计学习涉及领域-一般网络的观点.
