Abstract
Children who hear large amounts of diverse speech learn language more quickly than children who do not. However, high correlations between the amount and the diversity of the input in speech samples makes it difficult to isolate the influence of each. We overcame this problem by controlling the input to a computational model so that amount of exposure to linguistic input (quantity) and the quality of that input (lexical diversity) were independently manipulated. Sublexical, lexical, and multi-word knowledge were charted across development (Study 1), showing that while input quantity may be important early in learning, lexical diversity is ultimately more crucial, a prediction confirmed against children’s data (Study 2). The model trained on a lexically diverse input also performed better on nonword repetition and sentence recall tests (Study 3) and was quicker to learn new words over time (Study 4). A language input that is rich in lexical diversity outperforms equivalent richness in quantity for learned sublexical and lexical knowledge, for well-established language tests, and for acquiring words that have never been encountered before.
