# Neuronal circuits for context-driven bias in auditory categorization

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $708,605

## Abstract

NEURONAL CIRCUITS FOR CONTEXT-DRIVEN BIAS IN AUDITORY CATEGORIZATION
 In everyday life, because both sensory signals and neuronal responses are noisy, important cognitive tasks, such
as auditory categorization, are based on uncertain information. To overcome this limitation, listeners incorporate other
types of signals, such as the statistics of sounds over short and long time scales and signals from other sensory modalities
into their categorization decision processes. At the behavioral level, such contextual signals bias categorization by shifting
the listener's psychometric curve. At the neuronal level, categorization requires a transformation of sensory representation
into a representation of category membership that is modulated by these contextual signals. While categorical
representations have been found in the cortex, the cell types and neuronal mechanisms supporting the emergence of these
representations remains unknown. Furthermore, the mechanisms by which neuronal categorical representations are
modulated by contextual signals, giving rise to a behavioral bias, have not been explored. Our goal is to identify the
contribution of specific cell types to categorization and to understand the neuronal mechanisms for how contextual signals
bias auditory categorization.
 Multiple studies have demonstrated that neurons in auditory cortex (AC) and the posterior parietal cortex (PPC)
are involved in auditory categorization. Based on the well-described circuit architecture of the AC, recent studies, and our
preliminary data, we propose a series of hypotheses that delineate the role of excitatory-inhibitory circuits within AC in
creating and biasing categorical stimulus representations and for the role of PPC-AC projections in driving the source for
the bias signal. To test these hypotheses, we train mice in a two-alternative-forced choice task in which mice categorize
the
task,
associations).
frequency of a “target” sound into one of two overlapping categories (“low” or “high”). While mice participate in this
we systematically manipulate three bias signals (short-term and long-term stimulus statistics, and cross-modal
Thisdesign allows us to frame the cognitive task within a Bayesian framework, which generates formal
computational models for the function of specific neuronal cell types that are tested experimentally.
behavioral
activity.
category.
in
auditory
We will combine this
and computational framework with electrophysiological recordings and optogenetic manipulations of neuronal
First, we will test whether distinct neuronal cell types in AC differentially encode information about stimulus
Second, we will test whether and how specific inhibitory neuronal cell types in AC mediate context dependence
auditory categorization. Third, we will test whether and how cortico-cortical feedback mediates context dependence in
categorization.
 Aligned with the goals of the BRAIN initiative, our project will deliver a mechanistic framework for a cortical
ci...

## Key facts

- **NIH application ID:** 10011908
- **Project number:** 5R01NS113241-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Yale E Cohen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $708,605
- **Award type:** 5
- **Project period:** 2019-09-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10011908

## Citation

> US National Institutes of Health, RePORTER application 10011908, Neuronal circuits for context-driven bias in auditory categorization (5R01NS113241-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10011908. Licensed CC0.

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