Predicting language outcomes at 3 years using individual differences in morphological segmentation in infancy

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.