Language learning, language use and the evolution of linguistic variation.

Language learning, language use and the evolution of linguistic variation.
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DOI:
10.1098/rstb.2016.0051
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发表时间:
2017-01-05
期刊:
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
影响因子:
--
通讯作者:
Wonnacott E
Wonnacott E
中科院分区:
其他
文献类型:
--
作者:
Smith K;Perfors A;Fehér O;Samara A;Swoboda K;Wonnacott E

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语言共性产生于语言学习过程和语言使用过程的相互作用。这些因素之间的关系的一个测试案例是语言变异,它往往是以语言或社会语言学标准为条件的。我们如何解释自然语言中不可预测的变异的稀缺性,以及语言的这种属性在多大程度上直接反映了统计学习中的偏见?我们回顾了探索这些问题的三种实验工作,并介绍了语言变化的学习和传播的贝叶斯模型,沿着与成人参与者密切匹配的人工语言学习实验。我们的研究结果表明,虽然语言学习者的偏见可能会在塑造语言系统中发挥作用,但学习者的偏见与语言结构之间的关系并不简单。弱偏误会对语言结构产生强烈的影响,因为它们会在反复传播中积累。但反过来也可能是正确的:强烈的偏见可能产生微弱的影响或没有影响。此外,在互动过程中使用语言可以重塑语言系统。因此,如果我们要了解统计学习中的偏见如何与语言传播和语言使用相互作用,以塑造语言的结构特性,那么结合学习,传播和使用研究的数据和见解是必不可少的。这篇文章是“认知科学中统计学习的新前沿”主题的一部分。
Linguistic universals arise from the interaction between the processes of language learning and language use. A test case for the relationship between these factors is linguistic variation, which tends to be conditioned on linguistic or sociolinguistic criteria. How can we explain the scarcity of unpredictable variation in natural language, and to what extent is this property of language a straightforward reflection of biases in statistical learning? We review three strands of experimental work exploring these questions, and introduce a Bayesian model of the learning and transmission of linguistic variation along with a closely matched artificial language learning experiment with adult participants. Our results show that while the biases of language learners can potentially play a role in shaping linguistic systems, the relationship between biases of learners and the structure of languages is not straightforward. Weak biases can have strong effects on language structure as they accumulate over repeated transmission. But the opposite can also be true: strong biases can have weak or no effects. Furthermore, the use of language during interaction can reshape linguistic systems. Combining data and insights from studies of learning, transmission and use is therefore essential if we are to understand how biases in statistical learning interact with language transmission and language use to shape the structural properties of language. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.
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影响因子: 2.6
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