Feldman et al. criticize the MacArthur Communicative Development Inventories (CDIs) as having too much variability, too little stability, and insufficient ability to predict early language delay. We present data showing that these characteristics of the CDI are authentic reflections of individual differences in early language development rather than measurement deficiencies. We also respond to their critical assertions concerning sociodemographic influences on the CDI scores.

In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds are so skilled at learning noun categories that they seem to intuit the whole range of things in the category from hearing a single instance named – they are biased learners. This is not the case for children below the 20th percentile on productive vocabulary (late talkers). This paper looks at the vocabulary composition of age-matched 18-30-month-old late- and early-talking children. The results of Experiment 1 show that late talkers ’ vocabularies are more variable than early talker’s vocabularies. Crucially, Experiment 2 shows that neural networks trained on the vocabularies of individual late talkers learn qualitatively different biases than those trained on early talker vocabularies. These simulations make testable predictions for world learning biases of late- vs. early-talking children. The implications for diagnosis and intervention are discussed.

Purpose: The primary objectives of this study were to determine the prevalence of late language emergence (LLE) and to investigate the predictive status of maternal, family, and child variables. Method: This is a prospective cohort study of 1,766 epidemiologically ascertained 24-month-old singleton children. The framework was an ecological model of child development encompassing a wide range of maternal, family, and child variables. Data were obtained using a postal questionnaire. Item analyses of the 6-item Communication scale of the Ages and Stages Questionnaire (ASQ; D. Bricker & J.

Late talkers (LTs)—children who show a marked delay in vocabulary learning—are at risk for Specific Language Impairment (SLI), and much research has focused on identifying factors contributing to this phenomenon. We use a computational model of word learning to further shed light on these factors. In particular, we show that variations in the attentional abilities of the computational learner can be used to model various identified differences in LTs compared to normally-developing children: delayed and slower vocabulary growth, greater difficulty in novel word learning, and decreased semantic connectedness among learned words.