用复杂网络模拟儿童学词，揭示词汇增长的动态规律。
Early Language Learning via Spreading Activation and Category Exploration in Complex Networks
- 构建基于语义网络的激活扩散模型，模拟儿童学词过程。
- 在四种语言中，激活扩散比最短路径更贴近真实词汇习得顺序。
- 发现词汇学习存在突发性与类别停留时间特征，适合认知科学研究者。
儿童词汇习得是否在语义和词类上呈现不均衡？我们以图结构的心理词典为框架，将早期语言学习建模为两个相互作用过程驱动的搜索：激活扩散与强制探索（而非重复利用）词类。我们在德语、英语、荷兰语和里奥普拉塔内斯西班牙语四种语言上评估模型表现，使用词汇发育量表（CDIs）作为词类的真实标注，借助Wordbank库获取规范年龄数据，并采用最先进的词义相似度图谱重构方法。结果表明，激活扩散优于最短路径基线，在模拟正常词汇习得方面更具优势。在词类层面，我们揭示了CDIs之间的复杂转换模式。通过分析其序列中的突发性和同一词类内的平均持续时间，发现激活扩散能更好捕捉实际观察到的探索动态。总体而言，我们的研究指出词汇发展可通过复杂网络中激活动态与词类访问约束之间的非平凡互动来理解。
原文摘要 · Abstract (English)
Is word acquisition in children uneven with respect to semantic and lexical categories? To answer this question, we model early language learning as a search on a graph-based mental lexicon, driven by two interacting processes: spreading activation and an enforced exploration (rather than exploitation) of lexical categories. We evaluate model performance on four languages (German, English, Dutch, and Rioplatense Spanish), using CDIs as ground-truth data for lexical categories, normative ages derived from the Wordbank repository, and state-of-the-art resources for reconstructing graphs of word similarities. We find that spreading activation outperforms a shortest path baseline in simulating normative word acquisition. At the category level, we highlight complex transitions between CDIs. By studying their sequences in terms of burstiness and average persistence time within the same CDI, we find that spreading activation better captures the exploration dynamics observed empirically. Overall, our findings suggest that vocabulary development can be understood through the non-trivial interplay between activation dynamics and some degree of constraints regulating the visiting of lexical categories in complex networks.
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