Extended Statistical Learning as an account for slow vocabulary growth

Stephanie Stokes (1) , Sophie Kern (2) , Christophe dos Santos (3)

1 University of Canterbury [Christchurch]
2 DDL - Dynamique Du Langage
3 iBraiN - Imaging, Brain & Neuropsychiatry

Stokes (2010) compared the lexicons of English-speaking late talkers (LT) with those of their typically developing (TD) peers on neighborhood density (ND) and word frequency (WF) characteristics and suggested that LTs employed learning strategies that differed from those of their TD peers. This research sought to explore the cross-linguistic validity of this conclusion. The lexicons (production, not recognition) of 208 French-speaking two-year-old children were coded for ND and WF. Regression revealed that ND and WF together predicted 62% of the variance in vocabulary size, with ND and WF uniquely accounting for 53% and 9% of that variance respectively. Epiphenomenal findings were ruled out by comparison of simulated data sets with the actual data. A generalized Mann–Whitney test showed that children with small vocabularies had significantly higher ND values and significantly lower WF values than children with large vocabularies. An EXTENDED STATISTICAL LEARNING theory is proposed to account for the findings.

Domaines: Linguistique

Stephanie Stokes, Sophie Kern, Christophe dos Santos. Extended Statistical Learning as an account for slow vocabulary growth. Journal of Child Language, 2012, 39 (1), pp.105-129. ⟨10.1017/S0305000911000031⟩. ⟨hal-01996405⟩