论文标题
语言熟悉效果是逐渐的吗？计算建模方法
Is the Language Familiarity Effect gradual? A computational modelling approach
论文作者
论文摘要
根据语言熟悉效应（LFE），人们更好地区分母语的人。尽管这种认知效应在文献中很大程度上进行了研究，但仅在有限的语言对上进行了实验，其结果仅显示出效果的存在而不会产生逐渐的措施，而逐渐的措施可能会随着语言对而变化。在这项工作中，我们表明Thorburn，Feldmand和Schatz（2019）引入的LFE计算模型可以解决这两个局限性。在第一个实验中，我们证明了该模型通过在天然和强调语音上复制行为发现来获得LFE的能力。在第二个实验中，我们通过大量语言对评估LFE，其中包括许多从未在人类上进行过测试的语言。我们表明，这种效果在各种各样的语言中得到了复制，从而进一步证明了其普遍性。以LFE的逐步度量为基础，我们还表明，属于同一家庭的语言产生了较小的分数，从而支持语言距离对LFE产生影响的想法。
According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studied in the literature, experiments have only been conducted on a limited number of language pairs and their results only show the presence of the effect without yielding a gradual measure that may vary across language pairs. In this work, we show that the computational model of LFE introduced by Thorburn, Feldmand and Schatz (2019) can address these two limitations. In a first experiment, we attest to this model's capacity to obtain a gradual measure of the LFE by replicating behavioural findings on native and accented speech. In a second experiment, we evaluate LFE on a large number of language pairs, including many which have never been tested on humans. We show that the effect is replicated across a wide array of languages, providing further evidence of its universality. Building on the gradual measure of LFE, we also show that languages belonging to the same family yield smaller scores, supporting the idea of an effect of language distance on LFE.
