# Dual language input, semantic structure and word learning in typically developing and late talking bilingual children

> **NIH NIH K23** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2024 · $193,752

## Abstract

Project Summary.
Over the last several decades, developmental language scientists have sought to understand the effects of dual
language input on children’s vocabulary outcomes. Much of this work has been correlational and has focused
on static standardized measures of vocabulary size in typically developing bilingual children, excluding late
talkers – children with atypically small vocabularies. Yet, about 25% of bilingual 2-year-olds are late talkers
compared to only 10-15% of white monolingual cohorts. Although an important predictor of later outcomes,
vocabulary size reveals little about children’s semantic structure – how word meanings are represented,
organized, and connected. Computational and empirical studies in monolinguals suggest that children’s semantic
structure 1.) reflects the statistical regularities and semantic relationships in their language environments; and
2.) predicts word learning over and above vocabulary size. Critically, it remains unclear how children’s lexicons
reflect dual language input, and whether different semantic structures yield distinct adaptations to word learning
in dual language contexts. Therefore, the primary objective of this proposal is to examine interactions between
dual language input, semantic structure, and word learning in bilingual TD and LT toddlers. We will test 80
Spanish-English matched typical- and late-talking bilingual toddlers aged 24 – 30 months. In Aim 1, we will use
semantic network approaches to model children’s lexicons in both languages and characterize the relationship
between dual language input and children’s semantic structure. In Aim 2, we will examine the relationship
between semantic structure and statistical word learning in single and dual language contexts. We will also
analyze whether children’s semantic network properties in both languages modeled in Aim 1 predict word
learning performance across semantic and dual language experimental conditions. In Aim 3, we will analyze late-
talker status as a predictor and compare 40 typical talkers to 40 late talkers to examine whether the relationships
among dual language input, semantic structure, and word learning differ between groups. This career
development proposal includes an expert team of mentors from Psychology, Computer Science, Communication
Sciences and Disorders, Education and Public Health. The candidate will receive training in observational
methodology and parent-child interactions; experimental and eye-tracking methodology for toddlers; and network
science approaches. The long-term goals of this work and specialized training are to examine language
trajectories longitudinally in bilingual learners; and to develop novel clinician- and parent-mediated interventions
for bilingual children via behavior and network science approaches. The career goals align closely with the
strategic objectives of NIDCD, including to capitalize on advances in basic research to enhance our
understanding of normal function and disorde...

## Key facts

- **NIH application ID:** 10949247
- **Project number:** 1K23DC022006-01
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Kimberly Crespo
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $193,752
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10949247

## Citation

> US National Institutes of Health, RePORTER application 10949247, Dual language input, semantic structure and word learning in typically developing and late talking bilingual children (1K23DC022006-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10949247. Licensed CC0.

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