Word Learning as Network Growth: A Cross-linguistic Analysis
- Abdellah Fourtassi, Psychology Department, Stanford University, Stanford, California, United States
- Yuan Bian, Department of Psychology, University of Illinois, Urbana–Champaign, Illinois, United States
- Michael Frank, Psychology, Stanford University, Stanford, California, United States
AbstractChildren tend to produce words earlier when they are connected to a variety of other words along both the phonological and semantic dimensions. Though this connectivity effect has been extensively documented, little is known about the underlying developmental mechanism. One view suggests that learning is primarily driven by a network growth model where highly connected words in the child’s early lexicon attract similar words. Another view suggests that learning is driven by highly connected words in the external learning environment instead of highly connected words in the early internal lexicon. The present study tests both scenarios systematically in both the phonological and semantic domains, and across 8 languages. We show that external connectivity in the learning environment drives growth in both the semantic and the phonological networks, and that this pattern is consistent cross-linguistically. The findings suggest a word learning mechanism where children harness their statistical learning abilities to (indirectly) detect and learn highly connected words in the learning environment.
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