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.
翻译:根据语言熟悉效果(LFE),人们更擅长区分母语使用者,虽然这种认知效应在文献中研究得较多,但只对数量有限的对语进行了实验,实验结果只显示效果的存在,而没有产生不同语言对应体的渐进衡量标准。在这项工作中,我们证明索尔本、费尔德曼德和沙茨(2019年)引入的LFE计算模型可以解决这两个限制。在第一次实验中,我们证明这一模型有能力通过复制关于本地和重音语言的行为发现逐步衡量LFE。在第二次实验中,我们评估大量语言对应体的LFE,包括许多从未对人进行过测试的语言。我们表明,这种效果在广泛的各种语言中复制,提供了其普遍性的进一步证据。在逐渐测量LFE的基础上,我们还表明属于同一家庭的语言的分数较小,支持语言距离对LFEFE的效果。