Consistency and Variability in Children's Word Learning Across Languages

Abstract

Why do children learn some words earlier than others? The order in which words are acquired can provide clues about the mechanisms of word learning. In a large-scale corpus analysis, we use parent-report data from over 32,000 children to estimate the acquisition trajectories of around 400 words in each of 10 languages, predicting them on the basis of independently derived properties of the words' linguistic environment (from corpora) and meaning (from adult judgments). We examine the consistency and variability of these predictors across languages, by lexical category, and over development. The patterning of predictors across languages is quite similar, suggesting similar processes in operation. In contrast, the patterning of predictors across different lexical categories is distinct, in line with theories that posit different factors at play in the acquisition of content words and function words. By leveraging data at a significantly larger scale than previous work, our analyses identify candidate generalizations about the processes underlying word learning across languages.

Keywords: corpus analysis; language acquisition; word learning.

Figure 1.

Exampleproductiontrajectoriesforthewords“dog”and“jump” across languages. Points show the proportion of children producing each word for each one-month age group. Lines show the best-fitting logistic curve. Labels show the forms of the words in each language.

Figure 2.

Estimates of coefficients in predicting words’ developmental trajectories for English comprehension and production data. Larger coefficient values indicate a greater effect of the predictor on acquisition: positive main effects indicate that words with higher values of the predictor tend to be understood/produced by more children, while negative main effects indicate that words with lower values of the predictor tend to be understood/produced by more children; positive age interactions indicate that the predictor’s effect increases with age, while negative age interactions indicate the predictor’s effect decreases with age. Line ranges indicate 95% confidence intervals; filled in points indicate coefficients for which p < .05.

Figure 3.

Estimates of coefficients in predicting words’ developmental trajectories for all languages and measures. Each point represents a predictor’s coefficient in one language, with the bar showing the mean across languages. Filled in points indicate coefficients for which p < .05.

Figure 4.

Correlations of coefficient estimates between languages. Eachpoint represents the mean of one language’s coefficients’ correlation with each other language’s coefficients, with the vertical line indicating the overall mean across languages. The shaded region and line show a bootstrapped 95% confidence interval for a randomized baseline where predictor coefficients are shuffled within language.

Figure 5.

Dendrograms of the similarity structure among languages’ coefficients.

Figure 6.

Estimates of effects in predicting words’ developmental trajectories for each language, measure, and lexical category (main effect of predictor + main effect of lexical category + interaction between predictor and lexical category). Each point represents a predictor’s effect in one language, with the bar showing the mean across languages.