# Predictive coding in typical speech perception and dyslexia

> **NIH NIH F31** · HARVARD MEDICAL SCHOOL · 2021 · $33,743

## Abstract

PROJECT SUMMARY/ABSTRACT
The most common and best understood cause of the reading difficulty that defines dyslexia is an alteration in
the processing of spoken language. While speech perception deficits in dyslexia have been reported for
decades, a burgeoning literature describes behavioral deficits that rely on the exploitation of regularities in the
sensory environment, as well as reduced neural adaptation to consistent stimulation. Because efficient speech
processing relies on rapid plasticity for acoustic features characteristic of particular voices, coupled to semantic
predictions constrained by context, a rapid plasticity impairment in the auditory cortical hierarchy is a candidate
core deficit in dyslexia. Here we explore whether reduced plasticity due to short-term experience and/or top-
down expectation characterizes speech perception in dyslexia. By recording magnetoencephalography (MEG)
while individuals listen to pairs of words, we will determine how predictability differentially modulates neural
responses in dyslexia. Aim 1 is to characterize the spatiotemporal patterns of auditory repetition suppression
deficits in dyslexia. It is not known whether reduced neural adaptation is due to bottom-up or top-down
mechanisms. We will assess bottom-up repetition suppression by measuring responses to pairs of speech
stimuli in which the word, voice, or both are repeated unexpectedly, revealing with high spatiotemporal detail
how neural populations encode these features. Attenuated repetition suppression suggests that the auditory
system changes less due to short-term experience with word forms and voices, which may be a core
neurobiological difference in dyslexia. Aim 2 is to characterize expectation suppression and prediction error
deficits for speech in dyslexia. We will assess top-down expectation suppression by measuring responses to
pairs of speech stimuli in which listeners have high expectation that stimuli will repeat. Consistent with a
predictive coding account, we expect that fulfilled expectations will generate little response, while violated
expectations will evoke large prediction error responses, signaling a need to update the prediction. In dyslexia,
abnormalities in these phenomena suggest inadequate prediction of voice phonetics and/or word phonology,
implicating a higher-order deficit. Aim 3 is an exploratory quantification of the emergence of expected stimulus
feature encoding in neural signals. We will train a neural pattern classifier to distinguish words and voices from
the MEG data, investigating whether features emerge earlier and more robustly when they are predicted vs.
unpredicted, as would be explained by top-down influences. We will investigate whether individual differences
in classifier accuracy correlate with the magnitude of neural prediction error and with language abilities. These
aims advance a mechanistic understanding of speech processing differences that can lead to dyslexia.
Reduced plasticity due to sho...

## Key facts

- **NIH application ID:** 10005028
- **Project number:** 5F31HD100101-02
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Sara Dawley Beach
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $33,743
- **Award type:** 5
- **Project period:** 2019-12-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005028

## Citation

> US National Institutes of Health, RePORTER application 10005028, Predictive coding in typical speech perception and dyslexia (5F31HD100101-02). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10005028. Licensed CC0.

---

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