# Statistical Learning in Infant Language Acquisition

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $361,393

## Abstract

Project Summary/Abstract
First language acquisition is a hallmark of typical human development. A substantial body of research suggests
that infants’ ability to detect statistical regularities in language input facilitates language learning. However, the
impact of this literature has been limited by its failure to connect statistical learning with the burgeoning body of
research and theories focused on infants’ real-time language processing. The current application is motivated
by the premise that statistical regularities facilitate infants’ attempts to efficiently encode and process language
input. To this end, infants generate predictions about likely downstream input. These predictions are often
incorrect, rendering prediction errors – a potentially potent source of data for subsequent learning. Input that is
probabilistic and/or that has previously led to prediction errors provides information-rich data, guiding infants’
subsequent attention and learning. We hypothesize that statistical regularities are an important source of
information influencing this process, along with the other contextual cues available in both the linguistic and
nonlinguistic environment. To date, no prior studies have manipulated statistical regularities during infant
language processing tasks; research is necessary to adjudicate amongst the possible relationships between
statistical regularities in the input and real-time language behaviors during development. In the proposed
experiments, we will measure infants’ use of sequential statistical regularities during predictive language
processing tasks (Aim 1), assess the impact of statistical regularities on prediction error-based learning (Aim 2),
and examine the role of uncertainty due to statistical structure and prediction error in motivating infants’ active
exploratory behavior during language learning (Aim 3). The results of the proposed research will promote
positive developmental outcomes by expanding our understanding of the relationship between the statistical
structure of language input and real-time processes that are unfolding during language development. As in our
previous statistical learning research, the outcomes of these studies with typically-developing infants will
motivate future investigations that include infants and young children at risk for atypical language development
trajectories.

## Key facts

- **NIH application ID:** 10387382
- **Project number:** 1R01HD105313-01A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** JENNY R. SAFFRAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $361,393
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10387382

## Citation

> US National Institutes of Health, RePORTER application 10387382, Statistical Learning in Infant Language Acquisition (1R01HD105313-01A1). Retrieved via AI Analytics 2026-05-31 from https://api.ai-analytics.org/grant/nih/10387382. Licensed CC0.

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