In:Usage-based Perspectives on Language and Language Acquisition: In honour of Heike Behrens
Edited by Karin Madlener-Charpentier, Marjolijn H. Verspoor, Mirjam Weder and Annelies Häcki Buhofer
[Trends in Language Acquisition Research 35] 2026
► pp. 111–140
Chapter 4Explaining individual differences in children’s vocabulary
growth
Insights from the Language 0–5 Project
Authors
Caroline F. Rowland | Max Planck Institute for
Psycholinguistics | Donders Institute for Brain, Cognition &
Behaviour
This content is being prepared for publication; it may be subject to changes.
Abstract
Our goal in this chapter was to test a usage-based
computational model of vocabulary learning (CLASSIC). CLASSIC
simulates variation in vocabulary growth as a product of
interactions between linguistic input quantity and the child’s
current knowledge state, using an associative learning mechanism
with a fixed capacity limited processing window. We directly
compared the model’s results to those from children taking part in a
number of empirical tasks and showed that, simply by varying the
amount of input received by the model, we can successfully simulate
a number of effects that we see in the children’s performance:
individual differences in the rate of vocabulary growth between 19
and 30 months of age; correlations between vocabulary size, familiar
word processing speed (as measured by Looking-While-Listening tasks)
and phonological working memory capacity (as measured by non-word
repetition tasks), and the effect of processing speed and non-word
repetition performance on subsequent vocabulary growth. The results
suggest that exposure to linguistic input, read through a fixed
capacity processing constraint, leads learners to store phonological
knowledge in chunks of information of varying size, and it is this
stored knowledge that, at least partially, determines familiar word
processing speed, non-word repetition performance, and vocabulary
growth.
Article outline
- 1.Introduction
- 1.1Why do children differ in the speed with which they learn words?
- 1.2Introducing CLASSIC
- 1.3The present study
- 2.Method
- 2.1Participants
- 2.2Stimuli and procedure
- 2.3CLASSIC training
- 2.4Comparing children and models
- 2.5Coding
- 3.Results
- 3.1Vocabulary growth
- 3.2NWR tasks and vocabulary size
- 3.3Processing speed task (LWL) and vocabulary size
- 3.4Processing speed (LWL) and NWR tasks
- 3.5Predicting vocabulary growth from NWR performance
- 3.6Predicting vocabulary growth from speed of processing
- 4.Discussion
- 5.Conclusion
Acknowledgments Notes References
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