Not Only Size Matters: Early-Talker and Late-Talker Vocabularies Support Different Word-Learning Biases in Babies and Networks.

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.

Beyond modeling abstractions: learning nouns over developmental time in atypical populations and individuals.

Connectionist models that capture developmental change over time have much to offer in the field of language development research. Several models in the literature have made good contact with developmental data, effectively captured behavioral tasks, and accurately represented linguistic input available to young children. However, fewer models of language development have truly captured the process of developmental change over time. In this review paper, we discuss several prominent connectionist models of early word learning, focusing on semantic development, as well as our recent work modeling the emergence of word learning biases in different populations. We also discuss the potential of these kinds of models to capture children's language development at the individual level. We argue that a modeling approach that truly captures change over time has the potential to inform theory, guide research, and lead to innovations in early language intervention.

Diabetic ketoacidosis and memory dysfunction in children with type 1 diabetes.

OBJECTIVE: We tested the hypothesis that diabetic ketoacidosis (DKA) results in memory deficits typical of hypoxic/ischemic injury because recent studies suggest that cerebral metabolic changes similar to those observed in hypoxic/ischemic cerebral injury are observed in children with DKA, even without symptoms suggesting cerebral injury. STUDY DESIGN: Thirty-three children with type 1 diabetes mellitus (T1DM) and a history of DKA and 29 children with T1DM without a history of DKA were enrolled from an academic hospital pediatric endocrinology clinic. These groups were comparable on demographic and disease-related variables. These groups' ability to recall events in association with specific details, the memory function most directly affected by mild hypoxia/ischemia, was compared on 2 tasks (ie, event-color associations and event-spatial position associations). RESULTS: In multivariate analyses controlling for other critical variables, children with DKA history had significantly lower rates of accurate memory on both tasks (mean, 0.34 +/- 0.13 on the color task and 0.57 +/- 0.15 on the spatial task) than did children without DKA history (mean, 0.44 +/- 0.11 and 0.65 +/- 0.18, P < .01). CONCLUSIONS: DKA disrupts memory function, underscoring the importance of DKA prevention when T1DM is known and prompt diagnosis of children with new onset of T1DM.