title: Not Only Size Matters: Early-Talker and Late-Talker Vocabularies Support Different Word-Learning Biases in Babies and Networks.
abstract: In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds seem to intuit the whole range of things in a category from hearing a single instance named-they have word-learning biases. This is not the case for children with relatively small vocabularies (late talkers). We present a computational model that accounts for the emergence of word-learning biases in children at both ends of the vocabulary spectrum based solely on vocabulary structure. The results of Experiment 1 show that late-talkers' and early-talkers' noun vocabularies have different structures and that neural networks trained on the vocabularies of individual late talkers acquire different word-learning biases than those trained on early-talker vocabularies. These models make novel predictions about the word-learning biases in these two populations. Experiment 2 tests these predictions on late- and early-talking toddlers in a novel noun generalization task.
authors.affiliation: Department of Psychology and Neuroscience, University of Colorado, Boulder.
authors.forename: Eliana
authors.initials: E
authors.lastname: Colunga
authors.affiliation: Department of Psychology and Neuroscience, University of Colorado, Boulder.
authors.forename: Clare E
authors.initials: CE
authors.lastname: Sims
mesh_terms: D002663:Child Language; D006801:Humans; D007223:Infant; D007804:Language Development; D016571:Neural Networks, Computer; D013060:Speech / Q000502:physiology*; D014706:Verbal Learning / Q000502:physiology*; D014825:Vocabulary
keywords: Computational models; Early talkers; Late talkers; Neural networks; Word learning