Distributional Effects and Individual Differences in L2 Morphology Learning. Issue 1 (12th September 2016)

• Record Type: Journal Article
• Authors: Brooks, Patricia J.; Kwoka, Nicole; Kempe, Vera
• Is Part Of: Language learning. Volume 67:Issue 1(2017)
• Page Start: 171
• Page End: 207
• Publication Date: 2016-09-12
• Subjects: miniature natural language learning -- inflectional morphology -- statistical learning -- nonverbal intelligence -- entropy -- second language learning; Language and languages -- Periodicals; Language and languages -- Study and teaching -- Periodicals; Linguistics; 407
• DOI: 10.1111/lang.12204
• Languages: English
• ISSNs: 0023-8333

Abstract

Abstract: Second language (L2) learning outcomes may depend on the structure of the input and learners' cognitive abilities. This study tested whether less predictable input might facilitate learning and generalization of L2 morphology while evaluating contributions of statistical learning ability, nonverbal intelligence, phonological short‐term memory, and verbal working memory. Over three sessions, 54 adults were exposed to a Russian case‐marking paradigm with a balanced or skewed item distribution in the input. Whereas statistical learning ability and nonverbal intelligence predicted learning of trained items, only nonverbal intelligence also predicted generalization of case‐marking inflections to new vocabulary. Neither measure of temporary storage capacity predicted learning. Balanced, less predictable input was associated with higher accuracy in generalization but only in the initial test session. These results suggest that individual differences in pattern extraction play a more sustained role in L2 acquisition than instructional manipulations that vary the predictability of lexical items in the input. Open Practices: This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework athttps://osf.io/n8spz . Learn more about the Open Practices badges from the Center for Open Science:https://osf.io/tvyxz/wiki .