Structural priming in artificial languages and the regularisation of unpredictable variation
Structural priming in artificial languages and the regularisation of unpredictable variation
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DOI：
10.1016/j.jml.2016.06.002
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发表时间：
2016-12-01
影响因子：
4.3
通讯作者：
Smith, Kenny
中科院分区：
文献类型：
--
作者：
Feher, Olga;Wonnacott, Elizabeth;Smith, Kenny
我们提出了一种利用人工语言学习的新颖实验技术来研究交际互动过程中的结构启动与语言规律之间的关系。我们使用不可预测的变化作为测试用例，因为它是研究学习者在习得、传播和交互过程中的偏见的成熟范式。我们对参与者进行了人工语言的培训，这些语言在词序上表现出不可预测的变化，随后让他们使用这些人工语言进行交流。我们在两种不同的语法结构以及人与人以及人与计算机的交互中发现了结构启动的证据。无论行为趋同如何，启动都会发生：交流仅在人类互动中导致共享词序使用，但在所有条件下都观察到启动。此外，交互导致所有条件下不可预测的变化的减少，这表明交流交互在消除不可预测的变化中发挥着作用。正则化在人与人的交互中最强，并且在参与者认为他们正在与人交互但实际上是在与计算机交互的情况下。我们建议参与者认识到不可预测的变化的反功能性质，从而在沟通过程中采取行动消除这种变化。此外，人与人互动中发生的相互启动促使一些参与者趋向于最规则、高度可预测的语言系统。我们的方法为人工语言学习和结构启动领域提供了潜在的好处，并提供了一个有用的工具来研究导致语言变化和最终语言设计的交流过程。 (C) 2016 作者。由爱思唯尔公司出版
We present a novel experimental technique using artificial language learning to investigate the relationship between structural priming during communicative interaction, and linguistic regularity. We use unpredictable variation as a test-case, because it is a well established paradigm to study learners' biases during acquisition, transmission and interaction. We trained participants on artificial languages exhibiting unpredictable variation in word order, and subsequently had them communicate using these artificial languages. We found evidence for structural priming in two different grammatical constructions and across human-human and human-computer interaction. Priming occurred regardless of behavioral convergence: communication led to shared word order use only in human human interaction, but priming was observed in all conditions. Furthermore, interaction resulted in the reduction of unpredictable variation in all conditions, suggesting a role for communicative interaction in eliminating unpredictable variation. Regularisation was strongest in human-human interaction and in a condition where participants believed they were interacting with a human but were in fact interacting with a computer. We suggest that participants recognize the counter-functional nature of unpredictable variation and thus act to eliminate this variability during communication. Furthermore, reciprocal priming occurring in human-human interaction drove some pairs of participants to converge on maximally regular, highly predictable linguistic systems. Our method offers potential benefits to both the artificial language learning and the structural priming fields, and provides a useful tool to investigate communicative processes that lead to language change and ultimately language design. (C) 2016 The Author(s). Published by Elsevier Inc.