# What you see is what you learn: Visual attention in statistical word learning

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $346,643

## Abstract

PROJECT SUMMARY
Individual differences in the quantity and quality of parent talk and individual differences in infant
visual attention predict later vocabulary development, which in turn has the cascading
consequences of later cognitive development and school achievement. The proposed research
studies how infants prior to their first birthday begin to learn object names, and does so in a unique
approach, focusing on how visual information from the infant perspective coincide with parent
naming and on how infant looking behavior selects the data to be aggregated and how that
selected data changes incrementally in statistical learning. Toward this goal, we will collect a
corpus of infant-perspective scenes from 8-12-month-old infants as they play with their parent in
a toy room and as parents naturally name objects during play. We will analyze the referential
ambiguity of the scenes that co-occur with parent naming events, by showing the scenes to infants
and tracking their gaze direction in free viewing. We will use the gaze data to quantify ambiguity
in terms of the uncertainty, correctness, and informativeness of the scenes as to the intended
object referent. We will then construct training sets for cross-situational learning experiments
from the collected scenes by manipulating the mix of high and low ambiguity trials. We will test a
series of hypotheses about how infants aggregate information to learn multiple object names. 
Moreover, we will feed the trial-by-trial gaze data of individual infants to models to predict final
learning outcomes, with the goal of specifying attentional and memory processes that support
learning. Our overarching aim of the project is to show that the infant-perspective scenes co-
occurring with early naming events have properties that guide and train infant visual attention and
in so doing support the learning of names and their referents through the aggregation of
information across multiple naming events.

## Key facts

- **NIH application ID:** 10312147
- **Project number:** 5R01HD093792-06
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Chen Yu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $346,643
- **Award type:** 5
- **Project period:** 2017-11-17 → 2024-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10312147

## Citation

> US National Institutes of Health, RePORTER application 10312147, What you see is what you learn: Visual attention in statistical word learning (5R01HD093792-06). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10312147. Licensed CC0.

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