Patrick Rebuschat: What can cross-situational statistical learning tell us about bilingual development and second language acquisition?
CLUL Seminars
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The next session of Seminário CLUL is scheduled for the 13th of December, from 11:00 a.m. to 12:30 p.m.

Patrick Rebuschat

Titulo: What can cross-situational statistical learning tell us about bilingual development and second language acquisition?

Resumo : Statistical learning, essentially our ability to make use of statistical information in the environment to acquire (linguistic) knowledge, plays a fundamental role in how we learn languages. Following the seminal work of Saffran et al. (1996), there is substantial empirical evidence demonstrating that infants, children, and adults can rely on statistical learning to complete a variety of linguistic tasks, from speech segmentation and phonological categorization to word learning and syntactic development (see Frost et al., 2019, for a recent re- view). Statistical computations can be applied to a range of language units, including speech sounds, syllables, lexical categories, and syntactic phrases, but they are not limited to the domain of language. Instead, as previous research has shown, statistical learning is domain-general, i.e. it enables us to acquire information from multiple cognitive domains (language, music, etc.) and across a range of modalities (auditory, visual, tactile, etc.) (e.g., Frost et al., 2015). Moreover, statistical learning is not unique to human learners, as non- human primates rely on statistical learning, too (e.g., Rey et al., 2019).

In this talk, I will review recent statistical learning research conducted collaboratively in our group, Lancaster’s Language Learning Lab. The focus will be on experimental studies using the cross-situational learning paradigm developed by Monaghan et al. (2015). In this paradigm, participants are exposed to a novel language in ambiguous contexts under incidental learning conditions. That is, participants face the challenge of having to rapidly map novel sounds or sound sequences to multiple referents in the environment without prior information of the learning target and without feedback. To accomplish this task, participants need to be able to keep track of co-occurrence statistics across multiple learning trials, hence cross-situational statistical learning. 

In a sequence of studies, we explored cross-situational learning of novel phonology, words, morphology and grammar, either separately or simultaneously, using either natural languages (Latin, Japanese) or artificial languages (based, for example, on Japanese, Portuguese, German), comparing incidental or intentional learning conditions, the effect of instructional manipulations (e.g., feedback, explicit instruction, spacing) and the role of individual differences (e.g., declarative and procedural memory, working memory). Most of our studies have focused on adult participants (e.g., Monaghan et al. 2019, 2021; Rebuschat et al., 2021; Walker et al., 2020), but we have recently completed studies testing cross-situational learning in children. I will conclude the presentation with a reflection on the implications of this research for the study of bilingual development in children and adolescents and second language acquisition in adults.



Frost, R., Armstrong, B. C., & Christiansen, M. H. (2019). Statistical learning research: A critical review and possible new directions. Psychological Bulletin, 145(12), 1128–1153.

Frost, R., Armstrong, B. C., Siegelman, N., & Christiansen, M. H. (2015). Domain generality versus modality specificity: The paradox of statistical learning. Trends in Cognitive Sciences, 19(3), 117–125.

Monaghan, P., Mattock, K., Davies, R. A. I., & Smith, A.C. (2015), Gavagai is as Gavagai does: Learning nouns and verbs from cross-situational statistics. Cognitive Science, 39, 1099-1112.

Monaghan, P ., Ruiz, S., & Rebuschat, P . (2021). The role of exposure condition on the cross-situational learning of vocabulary and morphosyntax: Linear mixed effects reveal local and global effects of acquisition. Second Language Research, 37(2) 261–289.

Monaghan, P., Schoetensack, C., & Rebuschat, P. (2019). A single paradigm for implicit and statistical learning. Topics in Cognitive Science, 11(3), 536-554.

Rebuschat, P., Monaghan, P., & Schoetensack, C. (2021). Learning vocabulary and grammar from cross- situa- tional statistics. Cognition, 206.

Walker, N., Monaghan, P., Schoetensack, C., & Rebuschat, P. (2020). Distinctions in the acquisition of vocabulary and grammar: An individual differences approach. Language Learning, 70(S2), 221-254.

Rey, A., Minier, L., Malassis, R., Bogaerts, L., & Fagot, J. (2019). Regularity extraction across species: Associative learning mechanisms shared by human and non‐human primates. Topics in Cognitive Science, 11(3), 573- 586.