# Identification of treatment parameters that maximize language treatment efficacy for children.

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2022 · $641,329

## Abstract

Abstract
Poor language skills are associated with numerous negative outcomes ranging from higher rates of tantrums
and difficulty developing friendships to school failure, contact with the justice system, and increased victimization.
Although language deficits may be noticed as early as toddlerhood, effective treatment may not begin this early
and there is relatively little time to close the language gap before these children are faced with the increased
language demands of formal education and the cumulative effects of academic struggle. For the 7-13% of
children with impaired language skills, language treatments that are faster and more effective are urgently
needed. This competing renewal addresses this need with a series of studies that translate basic research in
statistical learning to treatment contexts. The Statistical Learning Framework posits learners extract word
meaning and grammatical structure from the language input they receive, and the statistical structure of the input
accounts for rapid, implicit language learning. Six proposed studies translate statistical learning principles to a
treatment context. Theoretically-motivated treatment factors are tested in two groups of children with poor
language skills. “Late Talkers” are children (ages 2-3 years) who are identified by the very limited number of
vocabulary words that they understand and use. Preschool children with Developmental Language Disorder
(ages 4-5 years) show marked deficits in the use of grammatical morphemes. Parallel studies targeting
vocabulary treatment (for Late Talkers) and morphosyntax treatment (for children with DLD) will test whether
leveraging prior learning can improve treatment methods by making learning faster and more effective. We will
also directly address the issue of non-responders (i.e., children who make limited improvement despite treatment
that is effective for others), an unaddressed problem inherent to all treatment research. We leverage our previous
findings to predict which children are highly likely to be non-responders and propose alternative treatment
methods that might assist this subset of children. These studies represent the necessary work for principled
language treatment that is supported by evidence, and can provide insights into the nature of learning in a range
of children with poor language skills.

## Key facts

- **NIH application ID:** 10449762
- **Project number:** 2R01DC015642-06A1
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** MARY ALT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $641,329
- **Award type:** 2
- **Project period:** 2016-07-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449762

## Citation

> US National Institutes of Health, RePORTER application 10449762, Identification of treatment parameters that maximize language treatment efficacy for children. (2R01DC015642-06A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10449762. Licensed CC0.

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