Authors:

Kathryn McGregor

Date Created:

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About Paper:

Children are introduced to millions of words in their first few years of life, and as they become toddlers, their vocabulary growth rate significantly increases. Although we have information on the normative properties of this growth, the language acquisition timeline can vary between children. This study explores how children build on their existing vocabulary knowledge to learn new words. We use a graph theoretic approach to model children's vocabulary knowledge where the words they say are connected by their shared features to form a semantic network. These shared features, or semantic feature norms (McRae, Cree, Seidenberg, & Mcnorgan, 2005; Borovsky, Peters, Cox, & McRae, in revision), can be grouped into categories like perceptual (e.g. has four legs) and functional (e.g. is used by children). To understand the impact of perceptual versus functional features, we first measure each child's existing vocabulary knowledge, then model their semantic network and produce an individually-tailored list of novel words related to the child's network via perceptual or functional features. Parents are given these words to teach their child, then assess whether their child learned them after two weeks and how this impacted vocabulary growth one month later. Prior research has shown perceptual features are most strongly correlated with normative age of acquisition (Peters & Borovsky, 2019), so we expect the novel words related by perceptual features to be learned better and improve vocabulary growth above the normative rate for the month. We anticipate this effect will not be seen in words related by functional features.

Source:

Purdue University / 2023

Topics:

No topics listed

Co-authors:

Kathryn McGregor