# Prefrontal contributions to contextual representation

> **NIH NIH F32** · UNIVERSITY OF PENNSYLVANIA · 2022 · $69,802

## Abstract

Project Abstract/Summary
This application describes a 3-year training plan that will enable me, a cognitive neuroscientist with prior
training in electroencephalography (EEG), to conduct research on contextual memory representation using
neuroimaging (fMRI) and computational modeling. EEG is useful for examining the timing properties of neural
activity, but cannot localize activity to specific regions of the brain. In this proposal, I will receive training on a
high-spatial resolution neuroimaging technique (fMRI), which will allow me to develop theories of neural
function that are constrained by both space and time. I will also build on my prior degree in applied statistics
and receive additional training in computational neuroscience, which will enable me to develop computational
theories at the macro-circuit level. I will be supervised by Dr. Sharon Thompson-Schill, an expert fMRI
experimentalist and theorist of lateral prefrontal cortex function, who has extensive experience researching
the context-dependent nature of semantic memory. I will be co-supervised by Dr. Anna Schapiro, an expert on
statistical learning and computational modeling of the brain. I propose to examine how prefrontal cortex (PFC)
represents statistical dependencies among sequentially presented visual and auditory input. I will examine
how the temporal extent and level of abstraction of sequential representations changes across ventral PFC.
This will connect findings from several literatures, ranging from decision-making to emotion processing and
language comprehension, within a single unifying framework. In addition, I will explore whether ‘deep’ or
‘shallow’ recurrent neural networks better capture the sensitivity profile of ventral PFC, informing the
question of whether the brain conducts ‘deep’ learning. In Aim 1, I will conduct behavioral piloting and collect
data for two neuroimaging experiments on hierarchical sequential processing. I will have participants learn the
statistical properties of hierarchically organized sequences of abstract visual (Aim 1a&b) and auditory (Aim 1b)
images. I then test for neural sensitivity to statistical learning at each hierarchical level using pattern similarity
analysis, comparing the neural response to the sequences before and after learning. In Aim 2, I will conduct
computational modeling of the neuroimaging data in Aim 1, with held out data to ensure robustness and
reproducibility. I compare the neuroimaging data to internal model representations derived from single-layer
(‘shallow’) and multi-layer (‘deep’) recurrent neural networks trained on the same sequences as the humans in
Aim 1. By modeling the neural representation of context itself, the current proposal will help fill a critical gap
in our understanding of how the brain predicts upcoming sensory input, enabling rapid processing of the
world around us. It will also inform our understanding of several psychiatric disorders that involve prefrontal
cortex disfunction and d...

## Key facts

- **NIH application ID:** 10377341
- **Project number:** 5F32MH123002-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Cybelle Marguerite Smith
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $69,802
- **Award type:** 5
- **Project period:** 2020-04-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10377341

## Citation

> US National Institutes of Health, RePORTER application 10377341, Prefrontal contributions to contextual representation (5F32MH123002-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10377341. Licensed CC0.

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