Iterated Learning Models of Language Change: A Case Study of Sino‐Korean Accent
Iterated Learning Models of Language Change: A Case Study of Sino‐Korean Accent
复制标题
语言变化的迭代学习模型：中韩口音案例研究
DOI：
10.1111/cogs.13115
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发表时间：
2022
影响因子：
2.5
通讯作者：
Feldman Naomi H.
中科院分区：
文献类型：
--
作者：
Ito Chiyuki;Feldman Naomi H.
语言进化的迭代学习模型通常被用来研究语言的出现，而不是历史上的语言变化。我们使用迭代学习模型来考察两个朝鲜语方言口音类别的历史变化。模拟显示，许多历史变化模式可以解释为连续几代语音定向学习的结果。不同迭代学习模型的比较也表明，韩国学习者的音向性概括是通过存储整个音节大小的单位来指导的，并为不同形式之间的知觉混淆对历史变化产生了实质性的影响提供了证据。这表明，除了解释语言广泛的一般特征的演变，迭代学习模型还可以提供对历史语言变化的更详细模式的洞察。
Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as resulting from successive generations of phonotactic learning. Comparisons between different iterated learning models also suggest that Korean learners’ phonotactic generalizations are guided by storage of entire syllable‐sized units, and provide evidence that perceptual confusions between different forms substantially impacted historical change. This suggests that in addition to accounting for the evolution of broad general characteristics of language, iterated learning models can also provide insight into more detailed patterns of historical language change.