# Infants' self-generated visual statistics support object and category learning

> **NIH NIH R01** · TRUSTEES OF INDIANA UNIVERSITY · 2021 · $645,832

## Abstract

PROJECT SUMMARY
Human visual object recognition is remarkable in its ability to recognize individual objects in challenging
circumstances and to rapidly recognize even novel instances of tens of thousands of everyday categories.
Although a great deal is known about these processes at maturity, very little is known about their development
especially with respect to common everyday objects and the experiences that support robust object recognition
and categorization. This gap is critical because object recognition and categorization support early word
learning, physical problem solving, and the later learning of orthographies and mathematical symbols. This
research projects focuses on visual object learning in 1 year old toddlers, a developmental period that at the
front end of marked advances in visual object recognition and a period in which children with multiple risk factors
begin to fall behind the normative developmental trajectory. The approach focuses on the properties of real-
world visual experiences that support learning to recognize individual objects in challenging visual contexts and
generalizing that learning to same category members. The method uses head-mounted eye-trackers to capture
field-of-view images from 100 infants 17 to 22 months of age as they spontaneously interact and play with
objects. Through active interactions with objects infants generates their own packets of visual data for learning.
Multiple visual properties relevant to object perception will be algorithmically measured and quantified. Toddlers’
recognition of the actively-engaged object and a novel object from the same category will be measured in
challenging benchmark contexts including clutter, occlusion, and different views. Category generalization will
be measured in a name generalization task. Advanced statistics and machine learning will determine the visual
properties of self-generated experiences that support infants object recognition and categorization. The research
will provide the first characterization of the natural visual statistics of toddlers’ active interactions with objects
and potentially transformative evidence that the developmental foundation for human prowess in visual object
categorization lies not in experiences with many different instances of a single category, the standard
assumption, but in active visual experiences with individual objects. Moreover, infants at risk for Developmental
Language Delay and Autism show disruptions in early object name learning that have been recently linked to
disruptions in visual learning about objects. The project includes preliminary analyses of infants at risk in
preparation for the next step in the long-term research program.

## Key facts

- **NIH application ID:** 10368173
- **Project number:** 1R01HD104624-01A1
- **Recipient organization:** TRUSTEES OF INDIANA UNIVERSITY
- **Principal Investigator:** LINDA B. SMITH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $645,832
- **Award type:** 1
- **Project period:** 2021-09-21 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10368173

## Citation

> US National Institutes of Health, RePORTER application 10368173, Infants' self-generated visual statistics support object and category learning (1R01HD104624-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10368173. Licensed CC0.

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