Nonword repetition, phonological storage, and multiple determinations
Nonword repetition, phonological storage, and multiple determinations
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非单词重复、语音存储和多重判定
DOI：
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发表时间：
2006
影响因子：
2.1
通讯作者：
Prahlad Gupta
中科院分区：
文献类型：
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作者：
Prahlad Gupta
(A)非单词重复和单词学习都依赖于语音存储和(B)两者都是乘法决定的建议是Gathercole(2006)主旨文章的两个主要焦点，该文章收集了大量的证据来支持这两个观点。在我看来，这些建议的重要性怎么强调都不为过：这两个概念切中了非单词重复和单词学习之间关系的核心。事实上，它们在我和我的同事们研究这种关系的方法中占据了突出的地位(例如，Gupta，2006；Gupta，Lipinski，Abbs，&Lin，2005；Gupta&MacWhinney，1997)。我们方法的一个重要方面是试图构建一个计算模型，可以模拟在非单词重复任务和单词学习任务中的表现，其基本原理是，实现这一点的计算模型将构成关于可能支撑关系的加工机制的建议。在这篇评论中，我描述了我们的计算工作如何提供了一种具体的方式来思考非单词重复和单词学习可能如何依赖于语音存储，以及这些能力如何可能被乘法确定。我认为，这样的计算工作是进一步研究Gathercole有影响力的工作所揭示的重要关系的宝贵工具，这一点在主旨文章中进行了分析。
The proposals that (a) nonword repetition and word learning both rely on phonological storage and (b) both are multiply determined are two of the major foci of Gathercole's (2006) Keynote Article, which marshals considerable evidence in support of each. In my view, the importance of these proposals cannot be overstated: these two notions go to the heart of the relationship between nonword repetition and word learning. Indeed, they figure prominently in the approach that my colleagues and I have taken to studying that relationship (e.g., Gupta, 2006; Gupta, Lipinski, Abbs, & Lin, 2005; Gupta & MacWhinney, 1997). An important aspect of our approach has been the attempt to construct a computational model that can simulate performance in a nonword repetition task and in a word learning task, the rationale being that a computational model that achieved this would constitute a proposal about the processing mechanisms that may underlie the relationship. In this Commentary, I describe how our computational work offers a concrete way of thinking about how nonword repetition and word learning may rely on phonological storage, and about how these abilities may be multiply determined. Such computational work is, I suggest, a valuable tool in further investigating the important relationship that has been revealed by Gathercole's influential work, and that is analyzed in the Keynote Article.