Semantic Learning Deficits in School Age Children With Developmental Language Disorder

School age children with developmental language disorder (DLD) have known semantic learning deficits but what is less well understood is why semantic learning is difficult for these children. This project will combine behavioral and brain methods to investigate the cognitive and linguistic processes underlying semantic learning in children with DLD compared to typically developing peers. The outcomes will have implications for semantic learning intervention approaches in DLD.

Study Overview

• Status: Enrolling by invitation
• Conditions: Developmental Language Disorder
• Intervention / Treatment: Other: semantic learning

Detailed Description

This project will elucidate deficits in learning semantic information in developmental language disorder (DLD, formerly referred to as specific language impairment) by combining behavioral and neural measures to examine differences in the semantic learning process between school-age children with and without DLD. Vocabulary knowledge, particularly semantic knowledge, has a critical influence on reading comprehension and academic success. Despite the strong association between vocabulary knowledge and academic success, vocabulary is an under-recognized area of deficit in school-age children with DLD. Younger children with DLD have well-established deficits in vocabulary and word learning and weaknesses in semantic knowledge. Additionally, the rate of vocabulary growth in children with DLD decreases compared to typically developing peers around age 10 and semantic representations of known vocabulary items are sparse. Even with this knowledge, the field's ability to make progress toward improved semantic learning in school age DLD is hindered by the lack of basic information on the underlying nature of the semantic learning deficits in this population. This project establishes how and why semantic learning differs between school-age children with and without DLD, providing a much-needed theoretical foundation for clinical research.

Storkel, expanding on an adult word learning model by Leach and Samuel, provides a clearly testable account of word learning that has been used with children with DLD. This account involves three processes: 1) triggering, in which a new lexical encounter is compared to existing lexical representations, 2) configuration, which adds information to the expanding lexical representation, and 3) engagement, which examines how the new lexical representation behaves dynamically with existing representations. The configuration process is arguably the most critical for semantic development. Successful configuration requires the simultaneous engagement of cognitive and linguistic processes, such as attention, inhibition, working memory, and semantic and syntactic processing. While it is widely accepted that configuration is the most affected word learning process in DLD, what is unknown is what underlies deficits in configuration and whether these deficits vary across the DLD profile. These questions are further compounded by difficulty measuring configuration and associated processes, given that they are largely internal, and therefore invisible. Electroencephalography (EEG) addresses this invisibility problem by allowing for a real-time examination of unconscious levels of semantic learning and cognitive and linguistic processes. A combined EEG-behavioral methods approach can illustrate how children with DLD are approaching configuration in terms of the relative contribution of these processes. The central hypothesis of this research is that children with DLD engage cognitive and linguistic processes at different points during configuration compared to their typical peers, resulting in poorer semantic learning outcomes.

To test the central hypothesis, the investigators will record behavioral and EEG data from 10-12 year old children with DLD and typical-language peers as they complete a semantic learning task. This age aligns with the point where vocabulary growth rates in DLD further diverge from typical peers [6]. In the semantic learning task, children listen to sets of three sentences that all end with the same nonword: half of the sentence triplets support learning meaning of the nonword, half do not. The investigators will analyze EEG data for event-related potentials (ERPs) as well as changes in neural oscillations (time frequency analysis). The investigators will combine EEG and behavioral measures to examine the following aims:

Aim 1. To investigate the cognitive and linguistic processes underlying configuration in children with DLD and typical language (TL) peers. This aim will include data from the semantic learning task. Based on the assessment of behavioral outcomes, the investigators predict that the TL group will be more accurate in semantic learning than the DLD group. ERP analyses will focus on the N400 component, associated with semantic processing. Time frequency analysis will focus on changes in the theta (4-8 Hz) and alpha (8-12 Hz) frequency bands, typically associated with lexical retrieval and attention/inhibition, respectively. For both neural measures, the investigators predict engagement of the same components (N400, theta, alpha) across groups but different patterns of change in those components during configuration between groups.

Aim 2. To investigate individual differences in configuration in children with DLD and TL peers. This aim will include data from the semantic learning task and a behavioral assessment battery. Assessment of behavioral data will focus the types of errors children make during semantic learning. The investigators expect that children with DLD will provide incorrect meanings for the nonword that best fit with the first sentence in the triplet and that TL children will provide incorrect meanings that best fit with the last sentence. The investigators will also examine individual differences related to semantic learning outcomes and fine-grained differences in N400 learning effects across groups. Here, the investigators expect that individual differences in general language ability and semantic knowledge, measured via the behavioral assessment battery, will be most predictive of both behavioral semantic learning and N400 change during learning.

• Study Type: Interventional
• Enrollment (Anticipated): 50
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • California
    • San Diego, California, United States, 92182
      • San Diego State University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 10 years to 12 years (Child)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• history of typical language development or history of language or literacy difficulties
• must be willing to wear EEG cap
• must be able to sit still for 1.5 hours to complete experimental tasks
• must be literate

Exclusion Criteria:

• neurological disorders (i.e., ASD, ADHD)
• significant neurological history (i.e., head injury, epilepsy)
• left handedness
• primary language other than English
• medication other than over-the-counter allergy medications
• and/or nonverbal IQ less than 70

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Developmental language disorder; Children with language impairment but in the absence of cognitive deficits	Other: semantic learning; Experimental semantic learning from linguistic context task
Active Comparator: Typical language; Children with typical language development and typical cognitive development	Other: semantic learning; Experimental semantic learning from linguistic context task

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
mean percent correct semantic meaning identification	accuracy on the semantic learning task (did they identify when there was a meaning and when there was not)	immediately following treatment, on the same day as treatment
change in the N400 mean amplitude at the word being learned across presentations of the new word	analysis of the N400 erp component related to attaching semantic meaning	immediately following treatment, on the same day as treatment
change in the alpha and theta band power at the word being learned across presentations of the new word	analysis of the theta and alpha frequency bands related to attaching semantic meaning	immediately following treatment, on the same day as treatment

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Clinical Evaluation of Language Fundamentals - 5th edition	standardized language omnibus measure	baseline
Test of Integrated Language and Literacy Skills, Vocabulary awareness subtest	standardized semantic knowledge measures	baseline
Wechsler Intelligence Scale for Children - 5th edition, nonverbal index	standardized nonverbal cognition assessment	baseline
Nonword repetition task	experimental task gauging phonological memory, participants are asked to repeat made up words	baseline
Flanker inhibitory control and attention task	Experimental measure of inhibition and attention	baseline

Collaborators and Investigators

• Sponsor: San Diego State University
• Investigators: Principal Investigator: Alyson Abel, PhD, San Diego State University

Study record dates

Study Major Dates

• Study Start (Anticipated): October 31, 2022
• Primary Completion (Anticipated): July 31, 2023
• Study Completion (Anticipated): July 31, 2023

Study Registration Dates

• First Submitted: August 6, 2020
• First Submitted That Met QC Criteria: August 7, 2020
• First Posted (Actual): August 11, 2020

Study Record Updates

• Last Update Posted (Actual): September 28, 2022
• Last Update Submitted That Met QC Criteria: September 27, 2022
• Last Verified: September 1, 2022

More Information

Terms related to this study

• Keywords: ERP; Semantic Learning; N400; ERSP
• Additional Relevant MeSH Terms: Nervous System Diseases; Neurologic Manifestations; Neurobehavioral Manifestations; Communication Disorders; Language Disorders; Language Development Disorders
• Other Study ID Numbers: R21DC018865 (U.S. NIH Grant/Contract)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No