Using Known Words to Learn More Words: A Distributional Analysis of Child Vocabulary Development

AbstractWhy do children learn some words before others? Understanding individual variability across children and also variability across words, may be informative of the learning processes that underlie language learning. We investigated item-based variability in vocabulary development using lexical properties of distributional statistics derived from a large corpus of child-directed speech. Unlike previous analyses, we predicted word trajectories cross-sectionally, shedding light on trends in vocabulary development that may not have been evident at a single time point. We also show that whether one looks at a single age group or across ages as a whole, the best distributional predictor is whether a child knows a word is the number of other known words with which that that word tends to co-occur.

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