# Studying crowding as a window into object recognition and development and health of visual cortex

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2020 · $388,626

## Abstract

ABSTRACT
 Our long-term goal is to understand how the human brain recognizes objects. This 3-year
project will characterize the computational kernel (computation that is applied independently to
many parts of the image data) that is isolated by crowding experiments. We present the discovery
that recognition of simple objects is performed by recognition units implementing the same
computation at every eccentricity. These units are dense in the fovea and thus hard to isolate there,
but they are sparse in the periphery, and easily isolated. Our fMRI & psychophysics pilot data show
that each of these units, at every eccentricity, has a circular receptive field with a radius of 2.6±1.5
mm (mean±SD) in human cortical area hV4. Because of cortical magnification, that 2.6 mm
corresponds to a tiny 0.05 deg in the fovea, but grows linearly with eccentricity, to a comfortable 3
deg at 10 deg eccentricity. We test this idea by pursuing its implications physiologically (Aim 1),
clinically (Aim 2), and psychophysically and computationally (Aim 3).
 Aim 1. Better noninvasive measures for the health and development of visual cortex are
needed. Conservation of crowding distance (in mm) in a particular cortical area (hV4) would validate
crowding distance as a quick, noninvasive measure of that area's condition. Aim 2. Huge public
interventions seek to help dyslexic children read faster and identify amblyopic children sooner. It
would be valuable to know whether crowding contributes to reading problems and provides a basis
for effective screening for dyslexia and amblyopia, as it can be measured before children learn to
read. Aim 3. Documenting conservation of efficiency gives evidence that the same universal
computation recognizes objects at every eccentricity. We are testing the first computational model of
object recognition that accounts for many human characteristics of simple-object recognition. The
new work extends to effect of receptive field size and learning.

## Key facts

- **NIH application ID:** 9884770
- **Project number:** 5R01EY027964-03
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** DENIS G PELLI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $388,626
- **Award type:** 5
- **Project period:** 2018-02-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9884770

## Citation

> US National Institutes of Health, RePORTER application 9884770, Studying crowding as a window into object recognition and development and health of visual cortex (5R01EY027964-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9884770. Licensed CC0.

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