Abstract
Learning new words and, subsequently, a lexicon, is a time-extended process requiring encoding of word-referent pairs, retention of that information, and generalization to other exemplars of the category. Some children, however, fail in one or more of these processes resulting in language delays. The present study examines the abilities of children who vary in vocabulary size (including both children with normal language (NL) and late talking (LT) children) across multiple timescales/processes - known and novel word mapping, novel word retention, and novel noun generalization. Results indicate that children with lower language skills suffer from deficits in quick in-the-moment mapping of known words compared to their NL peers, but age and vocabulary size rather than normative vocabulary ranking or NL/LT status better predicts performance on retention and generalization processes. Implications for understanding language development as a holistic process with multiple interacting variables are discussed.
Highlights
Word learning is a core part of early development, but requires multiple, interactive and related cognitive processes to be successful
In order to further explore the impact of specific vocabulary size and structure on performance, children who completed the MacArthur-Bates Communicative Development Inventory (MCDI)-WS were entered into a second model that included a fixed factor of total vocabulary size and shape residual score
The results suggest that approaches to studying word learning that cross multiple timescales may offer a holistic view for capturing word learning potential in both normal language (NL) and LT children
Summary
Word learning is a core part of early development, but requires multiple, interactive and related cognitive processes to be successful. To learn just a single new word a child must engage in fast in-the-moment mapping and encoding of a word and its referent, strengthen those initial word-referent associations such that they can be retained over time, and be able to generalize this information to new examples in future moments. Each process builds on each other and has cascading effects on the others – successful mapping lays a foundation for more robust retention, and robust retention strengthens the network that supports generalization (Kucker, McMurray & Samuelson, 2015; McMurray, Horst & Samuelson, 2012).
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