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Improved statistical learning abilities in adult bilinguals

Published online by Cambridge University Press:  11 October 2017

LUCA ONNIS ,
WIN EE CHUN  and
MATTHEW LOU-MAGNUSON
LUCA ONNIS*
Affiliation:
Nanyang Technological University, Singapore
WIN EE CHUN
Affiliation:
Nanyang Technological University, Singapore
MATTHEW LOU-MAGNUSON
Affiliation:
Nanyang Technological University, Singapore
*
Address for correspondence: Luca Onnis, Nanyang Technological University, 14 Nanyang Drive, Singapore637332lucao@ntu.edu.sg
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Abstract

Using multiple languages may confer distinct advantages in cognitive control, yet it is unclear whether bilingualism is associated with better implicit statistical learning, a core cognitive ability underlying language. We tested bilingual adults on a challenging task requiring simultaneous learning of two miniature grammars characterized by different statistics. We found that participants learned each grammar significantly better than chance and both grammars equally well. Crucially, a validated continuous measure of bilingual dominance predicted accuracy scores for both artificial grammars in a generalized linear model. The study thus demonstrates the first graded advantage in learning novel statistical relations in adult bilinguals.

Keywords

Artificial grammar learningbilingualismimplicit learninglanguage dominancemixed-effects modelsstatistical learning

Information

Type
Research Notes
Information
Bilingualism: Language and Cognition , Volume 21 , Issue 2 , March 2018 , pp. 427 - 433
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Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*Support comes from Singapore Ministry of Education Tier 1 grant RG81/14 to L.O., and NTU-URECA fund to L.O. and W.E.C. Shimon Edelman, Beth O'Brien, and Kathleen Ang for commenting on earlier versions of this manuscript. Author contributions: L.O. developed the study concept and design, performed the data analysis and interpretation, and drafted the manuscript. W.E.C. performed recruitment and data collection, calculated Language dominance scores, and assisted L.O. in data analysis and manuscript revision. M. L.M. wrote the PsychoPy scripts for the experiment and assisted in data analysis and manuscript revision. All authors approved the final version of the manuscript for submission.

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