# Prediction and Early Language Development in Young Children with ASD

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2021 · $596,971

## Abstract

Abstract
Language delay is often one of the first concerns of parents of toddlers with autism spectrum disorder (ASD),
and early language abilities predict broader outcomes for children on the autism spectrum. Yet, mechanisms
underlying language deficits in children with ASD remain unspecified. One prominent component of linguistic
behavior is the use of predictions or expectations during learning and processing. Several researchers have
recently posited prediction-deficit accounts of ASD. The basic assumption of the prediction accounts is that
information is processed by making predictions and testing violations against expectations (prediction errors).
Flexible (neurotypical) brains attribute differential weights to prediction errors to determine when new learning is
appropriate, while individuals with ASD are thought to assign disproportionate weight to prediction errors. These
prediction deficits are hypothesized to lead to higher levels of perceived novelty, resulting in `hyperplasticity' of
learning based on the most recent input. The proposed project will be the first study to examine whether language
deficits in young children with ASD are linked to atypical domain-general prediction processes. Seven studies
are proposed to address the following Specific Aims: (1) to determine the ability of toddlers with ASD to generate
predictions compared to typically developing (TD) peers; (2) to establish whether toddlers with ASD exhibit
hyperplasticity of learning relative to TD peers; (3) to examine the extent to which certain child characteristics
predict individual variability in predictive behavior and hyperplasticity in toddlers with ASD; and (4) establish
whether predictive behavior or hyperplasticity of learning on verbal/visual tasks predicts vocabulary and/or
syntactic ability one year later in young children with ASD. The sample will be comprised of 75 ASD toddlers
(three cohorts of 25 toddlers) and 75 TD controls distribution-matched on cognition (raw scores), SES, and sex.
In this project we will utilize anticipatory eye movements (AEMs) and looking-while-listening (LWL) methods to
investigate prediction in visual and verbal tasks in toddlers with and without ASD. Studies 1 (auditory) and 2
(visual) examine prediction as indexed by AEMs under conditions in which probabilities of the occurrence of
events are relatively stable. Studies 3 (verbal) and 4 (nonverbal) will investigate hyperplasticity of learning in
AEM tasks in which probabilities of the events change and predictions must be updated. Study 5 will explore
hyperplasticity within the context of novel word learning using a LWL task. Study 6 will examine the relationship
between child characteristics (cognition, language, autism symptom severity) and prediction. In Study 7,
performance on Studies 1-5 will be used to predict language abilities in the same sample of toddlers one year
later (from 18-35 mo to 30-47 mo) using both standardized tasks and an online incremental language p...

## Key facts

- **NIH application ID:** 10220940
- **Project number:** 5R01DC017974-03
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Susan Ellis-Weismer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $596,971
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10220940

## Citation

> US National Institutes of Health, RePORTER application 10220940, Prediction and Early Language Development in Young Children with ASD (5R01DC017974-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10220940. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*