# The Nature of Working Memory Representations

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2024 · $394,030

## Abstract

PROJECT SUMMARY
Because most high-level cognition depends on working memory (WM) and its dysfunction causes a host of
cognitive impairments, researchers have spent decades trying to understand the neural mechanisms that
support WM. Recently, using sophisticated computational neuroimaging approaches researchers have
repeatedly decoded the contents of WM from patterns of neural activity in a widely distributed number of brain
areas. The format of the WM representation in early visual cortex, for instance, might have the same “sensory-
like” properties as the visual stimulus. Although sensory-like representations are less likely in higher-order brain
areas, the nature of these alternative representations has yet remained impenetrable. This gap in our knowledge
is a critical problem because a host of psychiatric and neurologic disorders stems from a primary WM
dysfunction. Our long-term goal is to understand the mechanisms by which neural populations across the brain
encode WM representations, and how we might develop strategies to mitigate WM problems that impact the
quality of cognition. Our overall hypothesis is that what one sees and what one stores in WM can be distinct and
that distinction differs across the cortical hierarchy. The central aim of the project is to develop incisive data
analytic approaches that will reveal the nature of what is actually being encoded in the neural population
dynamics underlying WM storage. The rationale for the proposed research is that as we better understand the
neural mechanisms of WM, a strong theoretical framework will emerge within which strategies for understanding
and treating cognitive dysfunction will emerge. We test our central hypothesis by pursuing two specific aims. 1)
We will model the neural population dynamics that code for distinct formats of WM representations. 2) We will
identify when and where neural populations encode WM representations that are abstractions of sensory
features. Strong preliminary data demonstrate the feasibility of proposed work as well as initial support for the
hypotheses. Under Aim 1, using novel dimensionality reduction techniques suggest that neural populations code
for both a representation of the memorized stimulus and a representation of the specific stimulus feature that is
task relevant. Under Aim 2, using novel means to model and visualize WM representations revealed that neural
populations that are traditionally thought to encode visual stimulus features in WM also store abstractions that
can bear little resemblance to the original visual stimulus. Overall, the proposed work will generate the data
needed to unmask the representational format of WM across the cortical hierarchy. The approach is innovative
because it uses direct and unbiased computational approaches to model and visualize the representational
format of WM in ways that have not been applied in neuroimaging. The proposed research is significant because
it will provide key insights into the nature o...

## Key facts

- **NIH application ID:** 10923886
- **Project number:** 5R01EY033925-03
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** CLAYTON E CURTIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $394,030
- **Award type:** 5
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923886

## Citation

> US National Institutes of Health, RePORTER application 10923886, The Nature of Working Memory Representations (5R01EY033925-03). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10923886. Licensed CC0.

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