# Adaptive visual representation in human posterior parietal cortex

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $418,750

## Abstract

PROJECT SUMMARY
Central to human cognition is the ability to interact adaptively with our visual
environment by collecting and accumulating pertinent information to guide thoughts and
behavior. Recent work has pinpointed the significant role of posterior parietal cortex
(PPC) in supporting adaptive visual processing. Building upon two recent reviews on
PPC and two sets of exciting preliminary findings on visual representation in human PPC
involving attention and visual working memory (VWM), the present proposal aims to
document the nature of visual processing in PPC with the same rigor and precision as
those used to study visual representation in OTC. The overarching hypothesis here is
that task-related factors play a more prominent role in shaping visual representation in
PPC than OTC and the key to understand the intricate details of visual representation in
PPC is through its interactions with task-related factors. Using fMRI pattern decoding
and representational similarity analyses to document PPC representational structure in
humans, the present proposal examines two fundamental aspects of adaptive visual
processing: The contributions of attention and task to adaptive visual representation in
PPC in Aim 1, and the nature of VWM representation in PPC in Aim 2. To understand
the neural underpinnings of PPC visual processing, using voxel overlap analysis, in all
the studies proposed, we will examine whether the same or distinct PPC neuronal
populations contribute to different aspect of PPC visual processing. The proposed
studies will provide for the first time an in-depth and systematic understanding of the
nature of PPC visual representation in the human brain and its interaction with important
task-related factors. They shall provide foundational knowledge regarding how adaptive
visual processing is accomplished in the human brain.

## Key facts

- **NIH application ID:** 9981063
- **Project number:** 1R01EY030854-01A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** YAODA XU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $418,750
- **Award type:** 1
- **Project period:** 2020-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9981063

## Citation

> US National Institutes of Health, RePORTER application 9981063, Adaptive visual representation in human posterior parietal cortex (1R01EY030854-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9981063. Licensed CC0.

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