基于语言计算方法的语言认知实验综述

人脑对语言的理解过程十分复杂，涉及多个脑网络和加工机制。以往的工作大多采用严格控制的实验设计，针对特定的语言现象展开研究，导致了研究结论趋于碎片化，无法形成关于大脑语言理解的总体结论。另一方面，深度学习的出现引发了语言计算领域的技术变革，语言计算模型在多个任务上达到甚至超越了人类的水平。这为进行全局性、高生态效度的人脑语言理解实验带来可能性，促进了语言认知实验中引入语言计算模型方法的快速发展。那么，利用新兴的语言计算方法可以为大脑语言认知机理的研究带来哪些新的机遇和启发呢?该文归纳总结了利用语言计算方法进行语言认知实验的相关工作，并对未来发展趋势予以展望。

The language understanding processes in human brain is very complicated, involving multiple brain networks and processing mechanisms. Most previous work used strictly controlled experimental designs to investigate specific language phenomena. As a result, the research conclusions tend to be fragmented, hardly forming a picture about the brain language understanding. Recently, the emergence of deep learning has triggered technological changes in the field of language computation, and computational language models have reached or even surpassed human levels in multiple tasks. This brings the possibility of conducting global and highly ecologically valid language comprehension experiments, which will promote the development of computational language methods in language cognition experiments. This article summarizes the related work of language cognition experiments using computational language methods, and anticipates the future development trends.