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Proceedings of the Linguistic Society of America
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Adults regularize variation when linguistic cues suggest low input reliability

Authors: Yiran Chen; Kathryn Schuler;

Adults regularize variation when linguistic cues suggest low input reliability

Abstract

Children regularize inconsistent probabilistic patterns in linguistic input, yet they also acquire and match probabilistic sociolinguistic variation. What factors in the language input contribute to whether children will regularize or match the probabilistic patterns they are exposed to? Here, we test the hypothesis that low input reliability facilitates regularization. As a first step, we asked adult participants to acquire a variable plural marking pattern from a written (Exp 1) and a spoken (Exp 2) artificial language under different conditions, where they were led to believe input was more, or less, reliable. In both experiments, input reliability was manipulated through both information about the speaker (e.g., whether the speaker was likely to make mistakes) and linguistic cues (e.g., typos or pronunciation errors). Results showed that adults regularized the written language more only when they were told the speaker would make mistakes and the plural variants resembled typos (Exp 1), whereas they regularized the spoken language more when the plural variants resembled pronunciation errors regardless of the speaker’s said reliability in the spoken language. We conclude that input reliability is an important factor that can modulate learners’ regularization of probabilistic linguistic input, and that linguistic cues may play a more critical role than top-down knowledge about the speaker. The current study lays down an important foundation for future work exploring whether children are able to incorporate input reliability cues when learning probabilistic linguistic variation.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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