# Neural Systems in Auditory and Speech Categorization

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $286,345

## Abstract

Using complementary multi-modal neuroimaging methods (functional magnetic resonance imaging (fMRI) and
electrocorticography (ECoG)) in conjunction with rigorous behavioral approaches, we will examine the role of
multiple cortico-striatal and sensory cortical networks in the acquisition and automatization of novel non-
speech and speech categories in the mature adult brain. We test the scientific premise of a dual-learning
systems (DLS) model by probing neural function using fMRI or ECoG during the process of feedback-dependent
category learning. In contrast to popular single-learning system (SLS) approaches, DLS posits that two neurally-
dissociable cortico-striatal systems are critical to speech learning: an explicit, sound-to-rule cortico-striatal
system, that maps sounds onto rules, and an implicit, sound-to-reward cortico-striatal system that implicitly
associates sounds with actions that lead to immediate reward. Per DLS, the two systems contribute to the
emerging expertise of the learner. Via closed loops, the highly plastic cortico-striatal systems ‘train’ key less
labile temporal lobe networks to categorize information by validated rules or rewards. Once categories are
learned to the point of automaticity, cortico-striatal networks are no longer required to mediate behavior.
Instead, abstract categorical information within the temporal cortex drives highly accurate speech categorization.
In Aim 1.1, we use fMRI to examine the relative dominance of the two cortico-striatal networks in learning
multidimensional non-speech category structures that are experimenter-constrained to either rely on rules (rule-
based, RB), or on implicit integration of multidimensional cues (information-integration, II). We predict that key
regions of the sound-to-rule network, the prefrontal cortex (PFC), hippocampus, and caudate nucleus show
greater activation during RB, relative to II learning; in contrast, key regions within the sound-to-reward network,
the putamen and the ventral striatum show greater activation during II, relative to RB learning. In Aims 1.2 and
1.3, we leverage the temporal precision of ECoG measurements from high-density grids in temporal, PFC, and
Hippocampal regions to examine the extent to which temporal lobe representational changes during RB learning
are an outcome of error-monitoring processes within the PFC and hippocampus. In Aim 2, we probe neural
function using fMRI or ECoG to assess network and representational changes during the acquisition of non-
native supra-segmental and segmental categories to native-like performance levels. We predict that early
‘novice’ speech acquisition involves sound-to-rule mapping; later ‘experienced’ involves sound-to-reward
mapping. In contrast, only cortical networks are active at the point of ‘native-like automaticity’ in categorization.
Using innovative single-trial classification and network-level decoding analyses on ECoG data, we examine
learning-induced changes in speech representation wit...

## Key facts

- **NIH application ID:** 10194447
- **Project number:** 5R01DC015504-06
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Bharath Chandrasekaran
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $286,345
- **Award type:** 5
- **Project period:** 2017-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10194447

## Citation

> US National Institutes of Health, RePORTER application 10194447, Neural Systems in Auditory and Speech Categorization (5R01DC015504-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10194447. Licensed CC0.

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