Modeling cross-language structural priming in sentence production

Khoe, Yung Han; Tsoukala, Chara; Kootstra, Gerrit Jan; Frank, Stefan L.

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Modeling cross-language structural priming in sentence production

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Tsoukala, Chara
Center for Language Studies , External Organizations;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Citation

Khoe, Y. H., Tsoukala, C., Kootstra, G. J., & Frank, S. L. (2020). Modeling cross-language structural priming in sentence production. In T. C. Stewart (Ed.), Proceedings of the 18th Annual Meeting of the International Conference on Cognitive Modeling (pp. 131-137). University Park, PA, USA: The Penn State Applied Cognitive Science Lab.

Cite as: https://hdl.handle.net/21.11116/0000-0009-64E4-5

Abstract

A central question in the psycholinguistic study of multilingualism is how syntax is shared across languages. We implement a model to investigate whether error-based implicit learning can provide an account of cross-language structural priming. The model is based on the Dual-path model of
sentence-production (Chang, 2002). We implement our model using the Bilingual version of Dual-path (Tsoukala, Frank, & Broersma, 2017). We answer two main questions: (1) Can structural priming of active and passive constructions occur between English and Spanish in a bilingual version of the Dual-
path model? (2) Does cross-language priming differ quantitatively from within-language priming in this model? Our results show that cross-language priming does occur in the model. This ﬁnding adds to the viability of implicit learning as an account of structural priming in general and cross-language
structural priming speciﬁcally. Furthermore, we ﬁnd that the within-language priming effect is somewhat stronger than the cross-language effect. In the context of mixed results from
behavioral studies, we interpret the latter ﬁnding as an indication that the difference between cross-language and within-
language priming is small and difﬁcult to detect statistically.