Department of Psychology and Waisman Center, University of Wisconsin-Madison, WI 53705-2280, USA.
Dev Sci. 2011 Nov;14(6):1323-9. doi: 10.1111/j.1467-7687.2011.01079.x. Epub 2011 Aug 2.
Infants are adept at tracking statistical regularities to identify word boundaries in pause-free speech. However, researchers have questioned the relevance of statistical learning mechanisms to language acquisition, since previous studies have used simplified artificial languages that ignore the variability of real language input. The experiments reported here embraced a key dimension of variability in infant-directed speech. English-learning infants (8-10 months) listened briefly to natural Italian speech that contained either fluent speech only or a combination of fluent speech and single-word utterances. Listening times revealed successful learning of the statistical properties of target words only when words appeared both in fluent speech and in isolation; brief exposure to fluent speech alone was not sufficient to facilitate detection of the words' statistical properties. This investigation suggests that statistical learning mechanisms actually benefit from variability in utterance length, and provides the first evidence that isolated words and longer utterances act in concert to support infant word segmentation.
婴儿善于通过跟踪统计规律来识别停顿式言语中的单词边界。然而,研究人员对统计学习机制与语言习得的相关性提出了质疑,因为之前的研究使用了简化的人工语言,忽略了真实语言输入的可变性。本文报告的实验涵盖了婴儿指向性言语中可变性的一个关键维度。学习英语的婴儿(8-10 个月大)短暂地聆听了自然的意大利语,其中只包含流畅的言语或流畅的言语和单个单词的组合。聆听时间显示,只有当单词同时出现在流畅的言语中和孤立状态时,才能成功学习目标单词的统计属性;仅仅短暂地接触流畅的言语不足以促进对单词统计属性的检测。这项研究表明,统计学习机制实际上受益于话语长度的可变性,并首次提供了证据表明孤立的单词和更长的话语协同作用,以支持婴儿的单词分割。