Cai Jing, Hadjinicolaou Alex E, Paulk Angelique C, Soper Daniel J, Xia Tian, Williams Ziv M, Cash Sydney S
Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
bioRxiv. 2024 Apr 18:2023.03.10.531095. doi: 10.1101/2023.03.10.531095.
Through conversation, humans relay complex information through the alternation of speech production and comprehension. The neural mechanisms that underlie these complementary processes or through which information is precisely conveyed by language, however, remain poorly understood. Here, we used pretrained deep learning natural language processing models in combination with intracranial neuronal recordings to discover neural signals that reliably reflect speech production, comprehension, and their transitions during natural conversation between individuals. Our findings indicate that neural activities that encoded linguistic information were broadly distributed throughout frontotemporal areas across multiple frequency bands. We also find that these activities were specific to the words and sentences being conveyed and that they were dependent on the word's specific context and order. Finally, we demonstrate that these neural patterns partially overlapped during language production and comprehension and that listener-speaker transitions were associated with specific, time-aligned changes in neural activity. Collectively, our findings reveal a dynamical organization of neural activities that subserve language production and comprehension during natural conversation and harness the use of deep learning models in understanding the neural mechanisms underlying human language.
通过对话，人类通过言语产生和理解的交替来传递复杂信息。然而，这些互补过程背后的神经机制，或者说信息通过语言精确传达的神经机制，仍然知之甚少。在这里，我们使用预训练的深度学习自然语言处理模型结合颅内神经元记录，来发现能可靠反映言语产生、理解以及个体之间自然对话中它们的转换的神经信号。我们的研究结果表明，编码语言信息的神经活动广泛分布在多个频段的额颞叶区域。我们还发现，这些活动特定于所传达的单词和句子，并且它们依赖于单词的特定上下文和顺序。最后，我们证明这些神经模式在语言产生和理解过程中部分重叠，并且听众 - 说话者的转换与神经活动中特定的、时间对齐的变化相关。总的来说，我们的研究结果揭示了一种神经活动的动态组织，它在自然对话中支持语言产生和理解，并利用深度学习模型来理解人类语言背后的神经机制。
