# Supplement:Using population vectors to understand visual working memory for natural stimuli

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $50,721

## Abstract

This is a diversity supplement to provide training to a postbaccalaureate researcher, Mr. Sebastian Lopez,
so that he will be more competitive for getting into graduate school and more successful as a researcher. The
following is the abstract from the main grant.
 Visual working memory plays a fundamental role in visual perception and visually guided behavior, and
much has been learned about the nature of this memory system by studies using arrays of artificial but easily
controlled stimuli (e.g., arrays of colored disks or oriented Gabor patches). However, current quantitative
models of visual working memory based on these artificial stimuli cannot be readily extended to the kinds of
complex, structured scenes that humans face in daily life. The central goal of the proposed research is to
develop and test a new quantitative approach to understanding the representation of complex objects and
scenes in working memory, which will lead to a better understanding of real-world vision.
 In our model, a scene is represented in visual working memory as a noisier version of the pattern of
activation that was produced during the perception of that scene. We model this by feeding the scene into a
neural network model of the ventral object recognition pathway and using the resulting pattern of activation
across the population of units (the population vector) as a model of the working memory representation. We
then use this model to make predictions about both behavioral performance and neural activity in human
subjects. For example, the change detection task involves presenting a sample scene followed after delay by a
test scene and asking subjects to report whether the two scenes are the same or different. We can model this
task by feeding the sample and test scenes into the model and computing the distance between the population
vectors for the sample and test scenes. In our preliminary data, we find that the distance between the vectors
can predict behavioral change detection performance extremely well. Moreover, we find that the vector for the
sample scene can predict the pattern of neural activity during the delay between the sample and test scenes
(measured via event-related potentials). Note that previous quantitative models of visual working memory
cannot make any predictions at all for the natural scenes used in these preliminary studies.
 We propose testing and extending this model in several ways. First, we will conduct several experiments to
assess the ability of the model to predict behavioral performance and neural activity across a broad range of
natural stimuli. Second, we will compare the ability of population vectors from different cortical regions (as
estimated from the model and from fMRI data) to predict behavioral performance and delay-period activity,
providing new insights into the specific brain regions that underlie visual working memory. Third, we will
determine whether our model can predict performance in visually guided tasks (e....

## Key facts

- **NIH application ID:** 11076138
- **Project number:** 3R01EY033329-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** STEVEN J LUCK
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $50,721
- **Award type:** 3
- **Project period:** 2022-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11076138

## Citation

> US National Institutes of Health, RePORTER application 11076138, Supplement:Using population vectors to understand visual working memory for natural stimuli (3R01EY033329-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11076138. Licensed CC0.

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