# Computational Cognitive Neuroscience of Human Auditory Cortex

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $337,875

## Abstract

PROJECT SUMMARY
Humans with normal hearing excel at deriving information about the world from sound. Our auditory abilities
represent stunning computational feats that only recently have been replicated to any extent in machine
systems. And yet our auditory abilities are highly vulnerable, being greatly compromised in listeners with
hearing impairment, cochlear implants, and auditory neurodevelopmental disorders, particularly in the
presence of noise. Difficulties in recognition often lead to frustration and social isolation, and are not
adequately addressed by current hearing aids, implants, and remediation strategies. The long-term goal of the
proposed research is to reveal the basis of auditory recognition and to provide insights that will facilitate
improved prosthetic devices and therapeutic interventions. The development of more effective devices and
therapies is currently limited by an incomplete understanding of the factors that underlie real-world recognition
by normal-hearing listeners. In particular, although responses to sound in subcortical auditory pathways are
relatively well studied, little is known about the transformations that occur within the auditory cortex to create
representations of meaningful sound structure. We propose to enrich the understanding of auditory recognition
with three sets of experiments that examine the cortical representation of real-world sounds in human listeners,
combining functional magnetic resonance imaging (fMRI) with computational modeling of the underlying
representations. Aim 1 develops artificial neural network models of speech and music processing and
compares their representations to those in the auditory cortex, synthesizing and then measuring brain
responses to sounds that generate the same response in a model, and probing the time scale of the auditory
analysis of speech and music. Aim 2 develops and tests models of pitch perception in noise, exploring the
hypothesis that pitch perception is constrained both by the statistics of natural sounds and the frequency
selectivity of the cochlea. Aim 3 develops and tests models that jointly localize and recognize sounds, and
probes the brain representations of sound identity and location using fMRI. The results will reveal the
mechanisms underlying robust sound recognition by the healthy auditory system and will set the stage for
investigations of the cortical consequences of hearing impairment and auditory developmental disorders,
hopefully suggesting new strategies for remediation.

## Key facts

- **NIH application ID:** 10468917
- **Project number:** 5R01DC017970-04
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Josh H McDermott
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $337,875
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10468917

## Citation

> US National Institutes of Health, RePORTER application 10468917, Computational Cognitive Neuroscience of Human Auditory Cortex (5R01DC017970-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10468917. Licensed CC0.

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