# Temporal Pattern Perception Mechanisms for Acoustic Communication

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $335,750

## Abstract

Project Summary/Abstract:
Processing acoustic communication signals is among the most difficult yet vital abilities of the
auditory system. These abilities lie at the heart of language and speech processing, and their
success or failure has profound impacts on quality of life across the lifespan. Understanding the
neurobiological mechanisms that support these basic abilities holds promise for advancing
assistive listening devices, and for improving diagnoses and treatments for learning disabilities
and communication disorders, such as auditory processing disorder, dyslexia, and specific
language impairment. Non-invasive neuroscience techniques in humans reveal the loci of
language-related processing but do not answer how individual neurons and neural circuits
implement language-relevant computations. Thus, circuit-level neuro-computational mechanisms
that support acoustic communication signal processing remain poorly understood. Multiple lines
of research suggest that songbirds can provide an excellent model for investigating shared
auditory processing abilities relevant to language. This proposal investigates neural mechanisms
of auditory temporal pattern processing abilities shared between songbirds and humans. In Aim
1, we test the cellular-level predictions of a powerful modelling framework, called predictive
coding, proposed as a general computational mechanism to support the learned recognition of
complex temporally patterned signals at multiple timescales. We combine state-of-the-art
machine learning methods with multi-electrode electrophysiology, to test explicit models for
natural stimulus representation, prediction, and error coding in single cortical neurons and neural
populations. One aspect of auditory perception integral to speech is the discretization of the signal
into learned categorically perceived sounds (phonemes). In Aim 2, we use the predictive coding
framework to investigate the learned categorical perception of natural auditory categories in
populations of cortical neurons. In humans, the transition statistics between adjacent phonemes
can aid or alter phoneme categorization, providing cues for language learners and listeners to
disambiguate perceptually similar sounds. Aim2 also examines how categorical neural
representations are affected by temporal context. In addition to which phonemes occur in a
sequence, speech processing also requires knowing where those elements occur. Sensitivities to
the statistical regularities of speech sequences are established long before infants learn to speak,
and continue to affect both recognition and comprehension throughout adulthood. Songbirds also
attend to the statistical regularities in their vocal communication signals. In Aim 3, we focus on
how sequence-specific information is encoded by single neurons and neural populations in
auditory cortex. The proposed approach permits progress in the near term towards establishing
the basic neurobiological substrates of foundational language...

## Key facts

- **NIH application ID:** 10624335
- **Project number:** 5R01DC018055-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** TIMOTHY Q GENTNER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $335,750
- **Award type:** 5
- **Project period:** 2019-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10624335

## Citation

> US National Institutes of Health, RePORTER application 10624335, Temporal Pattern Perception Mechanisms for Acoustic Communication (5R01DC018055-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10624335. Licensed CC0.

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