Predicting language outcomes at 3 years using individual differences in morphological segmentation in infancy
Year: 2024
Type: article
Study design: Observational Study
Abstract: In previous research, infants’ performance on speech perception tasks has been shown to predict later language outcomes, typically vocabulary size. We used Bayesian analyses to model trial-level looking time behavior of individual infants on morphological segmentation experiments. We compared the usefulness of Bayesian estimates and the raw looking time difference measures used in previous studies to predict (a) vocabulary size at 30 months and (b) outcome measures obtained from language samples elicited via a picture description task at 36 months. We found that both estimates of morphological segmentation reliably predicted expressive vocabulary at 30 months. The Bayesian estimate also credibly predicted the correct use of verb tense morphemes obtained from the language sample. We therefore conclude that the Bayesian estimate is better for indexing individual differences in segmentation tasks and more useful for predicting clinically relevant language outcomes. • We used Bayesian analyses to model looking time behavior of individual infants on segmentation experiments. • We compared the usefulness of the Bayesian estimate and the raw looking time difference to predict later language outcomes. • Only the Bayesian estimate predicted the correct use of verb tense morphemes at 36 months. • The Bayesian estimate is more useful for predicting clinically relevant language outcomes than the looking time difference.
Source: Infant Behavior and Development
Authors: Jinyoung Jo, Megha Sundara, Canaan Breiss
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,320
Funder: National Science Foundation
OpenAlex ID: W4403927902