Various aspects of semantic features drive early vocabulary development, but less is known about how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network represented feature distances between words (i.e., Manhattan distances of probability distributions of features elicited for each pair of words). A word's global feature distinctiveness is measured with respect to all other words in the network and a word's local feature distinctiveness is measured relative to words in sub-networks derived from clustering analyses. This paper investigates how feature distinctiveness of individual words at local and global scales of the network influences language acquisition. Regression analyses indicate that global feature distinctiveness was associated with earlier age of acquisition ratings, and was a stronger predictor of age of acquisition than local feature distinctiveness. These results suggest that the global structure of the semantic feature network could play an important role in language acquisition, whereby globally distinctive concepts help to structure vocabulary development over the lifespan.

中文翻译：

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语言习得中的全局和局部特征差异效应

语义特征的各个方面推动了早期词汇的发展，但对整体语义特征空间的全局和局部结构如何影响语言习得知之甚少。英语单词的特征网络是从成人特征生产规范的大型数据库构建的，网络中的边缘表示单词之间的特征距离（即，每对单词引出的特征概率分布的曼哈顿距离）。一个词的全局特征显着性是相对于网络中的所有其他词来衡量的，而一个词的局部特征显着性是相对于从聚类分析得出的子网络中的词来衡量的。本文研究了网络局部和全球范围内单个单词的特征独特性如何影响语言习得。回归分析表明，全局特征独特性与较早的获得评级年龄相关，并且是比局部特征独特性更强的获得年龄预测因子。这些结果表明，语义特征网络的全局结构可以在语言习得中发挥重要作用，而全局独特的概念有助于构建词汇在整个生命周期中的发展。