# Investigating orthography-phonology and orthography-semantics pathways with implications for compensatory mechanisms in reading disorder in the context of a randomized control trial

> **NIH NIH F32** · UNIVERSITY OF CONNECTICUT STORRS · 2021 · $65,994

## Abstract

Project Summary/Abstract
Decoding-based reading disorder (RD, also known as dyslexia), a neurodevelopmental disorder with difficulties
in reading and spelling, affects approximately 3-10% of all children. Because RD leads to negative consequences
beyond academic achievement such as poor mental health outcomes, identifying effective reading interventions
is a high priority for researchers and educators. Currently, evidence-based multi-componential interventions that
explicitly and systematically target phonics (PHON, focused on letter-sound matching) are considered most
effective for remediating RD. However, up to 30% of RD children continue to struggle, warranting broader
examination of interventions that goes beyond PHON. Emerging research suggests that morphology-based
intervention (MORPH, focused on the understanding and identification of the structure of a word, such as word
bases, prefixes, and affixes) may serve as an alternative and complementary strategy. This proposed project,
leveraging a funded reading intervention program, will examine event-related potential (ERP)-based neural
signals in relation to responsiveness to interventions (RTI) by drawing insights from a computational model of
reading (i.e., the connectionist triangle model). Based on this model, we predict that PHON intervention primarily
targets the orthography-phonology [O-P] pathway, and MORPH intervention primarily targets the orthography-
semantics [O-S] pathway. We also predict that MORPH intervention promotes greater reading improvements for
individuals who primarily rely on O-S (possibly compensatory) mechanisms to overcome their weakness in
phonological processing. Specifically, this longitudinal project aims to address (1) whether children with RD who
have enhanced O-S processes at baseline are more responsive to PHON and MORPH interventions, and
whether such responsiveness will be greater if the approach emphasizes both O-S and O-P pathways as
compared to the O-P pathway alone; (2) whether the MORPH intervention strengthens the representation of O-
P and O-S pathways. We will examine ERP-based neural signals from a lexical task, collected 4 times over the
course of 5-week intensive interventions (MORPH, PHON, Executive function [EF], and Math interventions). We
will use machine learning and an individual difference approach to assess O-P and O-S neural signals and their
trajectories over the course of intervention, and to understand the relationship between these neural signals and
children’s RTI. This proposal will help us better understand factors that predict children’s RTI, and the mechanism
of different reading interventions. This fellowship will provide crucial research and professional training
opportunities to become an independent translational researcher. This includes gaining experience in designing
and conducting randomized controlled trials (RCTs), and methodological training in EEG/ERP including machine
learning and multilevel mixed modeling analyti...

## Key facts

- **NIH application ID:** 10389790
- **Project number:** 1F32HD106739-01A1
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** Siu Yin Clement-Lam
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $65,994
- **Award type:** 1
- **Project period:** 2021-11-01 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10389790

## Citation

> US National Institutes of Health, RePORTER application 10389790, Investigating orthography-phonology and orthography-semantics pathways with implications for compensatory mechanisms in reading disorder in the context of a randomized control trial (1F32HD106739-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10389790. Licensed CC0.

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