Identification of commonalities across different languages

Kieran Green
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Abstract

This article fulfills the need for quantifiable, physical, common characteristics across different languages, which is needed to support the theory that humans use domain-general neurocognitive machinery to acquire, process, and produce language. It is shown that four different languages—English, German, Slovak and Japanese—contain linguistic chunks characterized by at least one redundancy, degeneracy, pluripotentiality, or modularity (R, D, P, or M, respectively) trait, following precedent from other fields of signal investigation. It is first established that language can be regarded as a set of signals between individuals within a complex adaptive system framework and that all signals in all signaling systems exhibit R, D, P, and/or M traits. It is then shown that linguistic chunks can be regarded as individual signals and that the chunks examined from the aforementioned languages express at least one R, D, P, and/or M trait. The present contribution thereby indicates the potential provision of a new source of data for quantifying some of the pressures involved in language production and processing, and the work concludes by assessing the value of the present work for further investigation in related fields.
识别不同语言的共性
这篇文章满足了对不同语言之间可量化的物理共性特征的需求,而这正是支持人类使用领域通用神经认知机制来获取、处理和生成语言这一理论所需要的。研究表明,四种不同的语言--英语、德语、斯洛伐克语和日语--都包含至少一种具有冗余性、退化性、多能性或模块性(分别为 R、D、P 或 M)特征的语言块,这也是其他信号研究领域的先例。首先确定的是,语言可被视为复杂适应系统框架内个体间的一组信号,所有信号系统中的所有信号都表现出 R、D、P 和/或 M 特性。随后的研究表明,语言块可被视为个体信号,上述语言中的语言块至少表现出一种 R、D、P 和/或 M 特性。因此,本研究成果为量化语言生产和处理过程中涉及的某些压力提供了新的数据来源,最后对本研究成果在相关领域的进一步研究价值进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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