Infant-Directed Speech Is Consistent With Teaching
Infant-Directed Speech Is Consistent With Teaching
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DOI：
10.1037/rev0000031
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发表时间：
2016-11-01
影响因子：
5.4
通讯作者：
Shafto, Patrick
中科院分区：
文献类型：
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作者：
Eaves, Baxter S., Jr.;Feldman, Naomi H.;Shafto, Patrick
婴儿定向语音 (IDS) 具有与成人定向语音 (ADS) 不同的独特属性。为什么它具有这些特性——以及它们是否旨在促进语言学习——是一个有争议的问题。我们认为，这种分歧很大程度上源于缺乏正式的指导理论，即如何最好地向婴儿学习者教授语音类别。在缺乏这样的理论的情况下，研究人员依靠有关学习的直觉来指导论证。我们使用通过其他领域的实验验证的正式教学理论作为详细分析 IDS 是否适合语音类别教学的基础。利用该理论，我们生成了用于英语语音类别教学的理想数据。我们将模拟教学数据与人类IDS进行定性比较，发现教学数据表现出IDS的许多特征，包括一些已被视为IDS不用于教学的证据。模拟数据揭示了实验者探索 IDS 在语言学习中的作用时可能遇到的陷阱。关注不同的共振峰和音素集会导致不同的结论，并且在提供足够数量的示例之前，教学数据对学习者的好处并不明显。最后，我们研究了 IDS 到学习 ADS 的迁移。教学数据改进了 ADS 数据的分类，但仅限于生成这些数据来教学的学习者，而不是普遍适用于所有类别的学习者。这项研究提供了一个有理论基础的框架，使实验者能够系统地评估 IDS 是否用于教学。
Infant-directed speech (IDS) has distinctive properties that differ from adult-directed speech (ADS). Why it has these properties-and whether they are intended to facilitate language learning-is a matter of contention. We argue that much of this disagreement stems from lack of a formal, guiding theory of how phonetic categories should best be taught to infantlike learners. In the absence of such a theory, researchers have relied on intuitions about learning to guide the argument. We use a formal theory of teaching, validated through experiments in other domains, as the basis for a detailed analysis of whether IDS is well designed for teaching phonetic categories. Using the theory, we generate ideal data for teaching phonetic categories in English. We qualitatively compare the simulated teaching data with human IDS, finding that the teaching data exhibit many features of IDS, including some that have been taken as evidence IDS is not for teaching. The simulated data reveal potential pitfalls for experimentalists exploring the role of IDS in language learning. Focusing on different formants and phoneme sets leads to different conclusions, and the benefit of the teaching data to learners is not apparent until a sufficient number of examples have been provided. Finally, we investigate transfer of IDS to learning ADS. The teaching data improve classification of ADS data but only for the learner they were generated to teach, not universally across all classes of learners. This research offers a theoretically grounded framework that empowers experimentalists to systematically evaluate whether IDS is for teaching.