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Diversity not quantity in caregiver speech: Using computational modeling to isolate the effects of the quantity and the diversity of the input on vocabulary growth

2017/08/10 by Gary Jones, Caroline F. Rowland · 205 citations
Computer Science · Psychology · #Cognitive psychology #Communication #Diversity (politics) #Language Development and Disorders #Lexical diversity #Linguistics #Phonetics and Phonology Research #Psychology #Sociology #Speech and dialogue systems #Vocabulary #Vocabulary development

paper · pdf · doi:10.1016/j.cogpsych.2017.07.002

published in Cognitive Psychology 98, 1-21 (Elsevier BV)

openalex publication_date 2017/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

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.

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