A key omission from many accounts of children’s early word learning is the linguistic knowledge that the child has acquired up to the point when learning occurs. We simulate this knowledge using a computational model that learns phoneme and word sequence knowledge from naturalistic language corpora. We show how this simple model is able to account for effects of word length, word frequency, neighborhood density and phonotactic probability on children’s early word learning. Moreover, we show how effects of neighborhood density and phonotactic probability on word learning are largely influenced by word length, with our model being able to capture all effects. We then use predictions from the model to show how the ease by which a child learns a new word from maternal input is directly influenced by the phonological knowledge that the child has acquired from other words up to the point of encountering the new word. There are major implications of this work: models and theories of early word learning need to incorporate existing sublexical and lexical knowledge in explaining developmental change while well-established indices of word learning are rejected in favor of phonological knowledge of varying grain sizes.

许多关于儿童早期单词学习的重要遗漏是儿童在学习发生之前所获得的语言知识。我们使用从自然主义语言语料库中学习音素和单词序列知识的计算模型来模拟此知识。我们展示了这个简单的模型如何能够解释单词长度，单词频率，邻域密度和音位概率对儿童早期单词学习的影响。此外，我们展示了邻域密度和音位概率对单词学习的影响在很大程度上受单词长度的影响，我们的模型能够捕获所有影响。然后，我们使用模型的预测结果来说明，孩子从母体输入中学习新单词的难易程度如何直接受到孩子从其他单词获得的语音学知识的影响，直到遇到新单词为止。这项工作的主要含义是：早期单词学习的模型和理论需要结合现有的词法和词汇知识来解释发展变化，而公认的单词学习指标却被拒绝，而倾向于不同大小的语音学知识。