Working memory predicts word learning (Gray et al., 2022)

  • Shelley Gray (Creator)
  • Roy Levy (Creator)
  • Mary Alt (Creator)
  • Tiffany Hogan (Creator)
  • Nelson Cowan (Creator)

Dataset

Description

<b>Purpose:</b> The purpose of this study was to use an established model of working memory in children to predict an established model of word learning to determine whether working memory explained word learning variance over and above the contributions of expressive vocabulary and nonverbal IQ.<b>Method: </b>One hundred sixty-seven English-speaking second graders (7- to 8-year-olds) with typical development from two states participated. They completed a comprehensive battery of working memory assessments and six word learning tasks that assessed the creation, storage, retrieval, and production of phonological and semantic representations of novel nouns and verbs and the ability to link those representations.<b>Results:</b> A structural equation model with expressive vocabulary, nonverbal IQ, and three working memory factors predicting two word learning factors fit the data well. When working memory factors were entered as predictors after expressive vocabulary and nonverbal IQ, they explained 45% of the variance in the phonological word learning factor and 17% of the variance in the semantic word learning factor. Thus, working memory explained a significant amount of word learning variance over and above expressive vocabulary and nonverbal IQ.<b>Conclusion: </b>Results show that working memory is a significant predictor of dynamic word learning over and above the contributions of expressive vocabulary and nonverbal IQ, suggesting that a comprehensive working memory assessment has the potential to identify sources of word learning difficulties and to tailor word learning interventions to a child’s working memory strengths and weaknesses.<b>Supplemental Material S1. </b>Correlations among working memory, word learning measures, expressive vocabulary, &amp; nonverbal IQ (lower-left triangle), <i>N </i>for each measure (Diagonal), and <i>N </i>for each pair of measures (upper-right triangle).<b/><b>Supplemental Material S2. </b>Estimated factor loadings and error variances of the working memory variables from the model.<b>Supplemental Material S3.</b> Estimated factor loadings and error variances for the word learning variables for the phonological factor from the model. <b>Supplemental Material S4.</b> Estimated factor loadings and error variances for the word learning variables for the semantic factor from the model. <b>Supplemental Material S5. </b>Estimated error covariances for word learning variables from the model. <b>Supplemental Material S6. </b>Estimated covariances of the working memory factors from the model. <b>Supplemental Material S7.</b> Estimated structural coefficients for predicting the phonological and semantic word learning factors from the model.<b>Supplemental Material S8.</b> Estimated covariances of the latent disturbances for the word learning factors from the model. Gray, S. I., Levy, R., Alt, M., Hogan, T. P., &amp; Cowan, N. (2022). Working memory predicts new word learning over and above existing vocabulary and nonverbal IQ. <i>Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2021_JSLHR-21-00397
Date made available2022
PublisherASHA journals

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