人脑如何学习新的语言规则  

How Brain Acquire New Language Rules

作　　者：耿立波 杨丽[1,2] 方娇艳 杨亦鸣[1,2] GENG Libo;YANG Li;FANG Jiaoyan;YANG Yiming(School of Linguistic Sciences and Arts,Jiangsu Normal University,Xuzhou,Jiangsu 221009,China;Collaborative Innovation Center for Language Ability,Jiangsu Normal University,Xuzhou,Jiangsu 221009,China)

机构地区：[1]江苏师范大学语言科学与艺术学院,江苏徐州221009 [2]江苏师范大学语言能力协同创新中心,江苏徐州221009

出　　处：《中文信息学报》2021年第5期27-37,62,共12页Journal of Chinese Information Processing

基　　金：国家社会科学基金(16CYY021);国家重点基础研究发展计划(973)(2014CB340502)。

摘　　要：成人大脑究竟能否掌握新的语言规则,是语言学习研究领域一直存在争议的问题。习得年龄、输入量和相似性,哪个才是影响语言规则学习的重要因素?学界始终没有统一的结论。该文以成年汉语母语者为研究对象,基于小数据的人工语法学习(artificial grammar learning, AGL)范式设计实验,采用跟踪调查和事件相关电位技术,探讨在高/低输入量条件下,人脑加工与汉语相似程度不等的三种句法结构时的神经机制。结果发现,成人可以在小数据学习范式下,运用无监督学习方法掌握新的语言规则;人脑可以基于少量的规则输入习得多种人工语法规则,并表现出趋近于母语加工的自动加工模式;人脑通过竞争的方式习得新的语言规则。该研究丰富了AGL范式下的语言学习理论,并可以对自然语言处理相关研究提供一些启示。Whether and how human brains can master new grammar rules has been hotly debated in linguistic research. There is a lack of consensus regarding what the most important factors are in grammar rule learning(e.g., age of acquisition and amount of input) and their influences. This question yielded the current study, which utilized Artificial Grammar Learning(AGL) paradigm and Event Related Potentials(ERPs) to examine longitudinal changes in the neural mechanism underlying processing artificial grammar among adult Mandarin native speakers. We manipulated the amount of the input, and created three artificial grammars, each featuring a different level of similarity to the Chinese Mandarin grammar. The results showed that(a) within the framework of small data learning, adults can use unsupervised learning to master new grammar rules;(b) different grammar rules can be acquired with a relatively small amount of input and processed to a native-like level;and(c) grammar rules are acquired through competitive interactions between brain mechanisms. These findings contribute to learning theories using AGL paradigm and inform future research on Natural Language Processing.

关 键 词：人工语法 小数据 无监督学习 输入量 事件相关电位技术 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]