# Multi-feature predictive processing of stochastic sounds in the human auditory system

> **NIH NIH F31** · JOHNS HOPKINS UNIVERSITY · 2020 · $40,386

## Abstract

Project Summary/Abstract
Normal-hearing listeners are able to effortlessly interpret their acoustic surroundings, parsing ongoing
sound into distinct sources and tracking these sources through time. To perform this task, the brain
represents relevant sensory information in memory to be compared to future inputs, but the nature of
this memory representation is not well understood. Typically, this mechanism is studied using predictable
patterns in sound sequences, where listeners are asked to detect deviants from established patterns.
Previous work has demonstrated the brain is sensitive to a wide variety of patterns in sound along
multiple acoustic dimensions. These patterns, however, do not probe how the brain represents sound in
natural listening environments, where relevant information is often not predictable and cannot be
represented explicitly and with certainty. This project uses stochastic sound sequences—which exhibit
statistical properties rather than deterministic patterns—to investigate the extent to which the brain
represents statistical information from sequences of sounds in the presence of uncertainty. Our central
hypothesis is that the brain collects high-dimensional statistical information (beyond mean and variance)
to capture uncertainty across time and across perceptual features to interpret ongoing sound. In a series
of change detection experiments, listeners will be asked to detect changes in the entropy of sound
sequences varying along multiple perceptual features: pitch, timbre, and spatial location. A
computational model for predictive processing will be developed to compare alternative representations
of statistical information in the brain. Perceptual constraints in the model will be fit to individual
behavior, and the fitted model will be used to predict deviance responses in Electroencephalography
(EEG) data. Additionally, individual differences in perceptual abilities will be measured using a separate
task in the same listeners, and these measures will be compared to findings from the model to add
interpretative heft and improve the model. A computational model for how the brain processes complex
sounds will open the possibility of investigating more natural, “messy” stimuli in the laboratory, and a
better understanding of the individual differences in perception of stochastic sounds could lead to better
diagnostic tools for assessing temporal processing abilities.

## Key facts

- **NIH application ID:** 9844411
- **Project number:** 5F31DC017629-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Benjamin Skerritt-Davis
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $40,386
- **Award type:** 5
- **Project period:** 2018-12-18 → 2020-10-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9844411

## Citation

> US National Institutes of Health, RePORTER application 9844411, Multi-feature predictive processing of stochastic sounds in the human auditory system (5F31DC017629-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9844411. Licensed CC0.

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