Testing the limits of statistical learning for word segmentation.

Testing the limits of statistical learning for word segmentation.
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DOI:
10.1111/j.1467-7687.2009.00886.x
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发表时间:
2010-03
影响因子:
3.7
通讯作者:
Tyler MD
Tyler MD
中科院分区:
心理学1区
文献类型:
--
作者:
Johnson EK;Tyler MD

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过去的研究表明,婴儿可以快速地从人工语言中提取音节分布信息,并利用这些知识来推断语音中可能的单词边界。然而,人工语言相对于自然语言来说是极其简化的。在这项研究中,我们询问婴儿在人工语言中跟踪音节之间过渡概率的能力是否可以扩展到自然语言的挑战。我们通过测试5.5个月和8个月大的婴儿对包含四个统一长度的单词(所有CVCV)或四个不同长度的单词(两个CVCV,两个CVCVCV)的人工语言进行分割的能力来做到这一点。两种语言的词边界的过渡概率线索是相等的。这两个年龄组都对包含相同长度单词的语言进行了分割,这表明即使是5.5个月大的孩子也对他们环境中的条件概率非常敏感。然而,尽管定义单词边界的过渡概率线索在两种语言中同样强大,但两个年龄组都没有成功分割包含不同长度单词的语言。我们的结论是,婴儿的统计学习能力可能不像早期研究所表明的那样强大。
Past research has demonstrated that infants can rapidly extract syllable distribution information from an artificial language and use this knowledge to infer likely word boundaries in speech. However, artificial languages are extremely simplified with respect to natural language. In this study, we ask whether infants’ ability to track transitional probabilities between syllables in an artificial language can scale up to the challenge of natural language. We do so by testing both 5.5- and 8-month-olds’ ability to segment an artificial language containing four words of uniform length (all CVCV) or four words of varying length (two CVCV, two CVCVCV). The transitional probability cues to word boundaries were held equal across the two languages. Both age groups segmented the language containing words of uniform length, demonstrating that even 5.5-month-olds are extremely sensitive to the conditional probabilities in their environment. However, neither age group succeeded in segmenting the language containing words of varying length, despite the fact that the transitional probability cues defining word boundaries were equally strong in the two languages. We conclude that infants’ statistical learning abilities may not be as robust as earlier studies have suggested.
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