Statistical Learning in Infant Language Acquisition

NIH RePORTER · NIH · R01 · $361,393 · view on reporter.nih.gov ↗

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
UNIVERSITY OF WISCONSIN-MADISON
Principal Investigator
JENNY R. SAFFRAN
Activity code
R01
Funding institute
NIH
Fiscal year
2022
Award amount
$361,393
Award type
1
Project period
2022-08-01 → 2027-05-31