# A Novel Approach to Measuring Neural Tuning to Written Words

> **NIH NIH R21** · UNIV OF MARYLAND, COLLEGE PARK · 2022 · $247,089

## Abstract

Project Description
The written word is not represented in a uniform manner in the brain. Instead, different features
of the written word are thought to be differentially represented in the same or potentially
neighboring, but distinct cortical regions. For instance, cortical areas predominantly in, but not
limited to left ventral occipital-temporal cortex (vOTC) are thought to represent different features
such as entire word units (e.g. [MINT]), bigrams ([MI], [IN], [NT], or the sub-lexical mappings of
letters to sounds ([M]-/m/, [I]-/ɪ/, [N]-/n/, [T]-/t/). In order to develop a more mechanistic view of
what aspects of the written word are effectively processed in skilled and impaired readers, it is
important to be able to parse apart experience-dependent neural tuning of these different features
of printed words. For instance, individuals may have poorly tuned letter-sound units, yet well-
tuned bigram units or lexical units. This is a central question in the study of developmental dyslexia
and reading impairments. The Specific Aims of this project are to address this question by
systematically investigating the multivariate nature of representation in cortex and the
tuning of cortical responses to these features concurrently across individuals with a range
of reading skill. In doing so we will address a) how features of word forms are distributed across
the “reading network”; b) how different neuronal populations become attuned to these different
features of the written word in an experience-dependent manner; and c) how the tuning of these
different orthographic features predicts reading performance across individuals. To accomplish
this, we employ a set of innovative methods for quantifying neural response heterogeneity across
voxels with the assumption, based on sparse coding theory, that highly tuned feature
representations have more heterogeneous (i.e., unique) neural responses across voxels. The
main focus of Aim 1 will be to validate the use of Representational Similarity Analysis (RSA) in
combination with a novel multivariate analytic method in fMRI termed Heterogeneity Regression
(Hreg) to form a metric of Tuning Similarity Analysis (TSA). Aim 2 is to determine whether this
novel set of metrics is predictive of reading performance across the range of typically achieving
readers as well as indicative of poor or impaired reading ability. This would confirm that this
heterogeneity based approach can index the content and quality of representations in skilled word
reading as well as the potential source of reading failure. If successful, the Broader Impacts of
this approach could be used to determine representational integrity not only to difficulties
in visual word recognition, but also for spoken language processing and other domains of
multidimensional perceptual learning complementing behavioral diagnostics.

## Key facts

- **NIH application ID:** 10528136
- **Project number:** 1R21HD107511-01A1
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** Donald J Bolger
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $247,089
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10528136

## Citation

> US National Institutes of Health, RePORTER application 10528136, A Novel Approach to Measuring Neural Tuning to Written Words (1R21HD107511-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10528136. Licensed CC0.

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