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  Counting 'uhm's: how tracking the distribution of native and non-native disfluencies influences online language comprehension

Bosker, H. R., Van Os, M., Does, R., & Van Bergen, G. (2019). Counting 'uhm's: how tracking the distribution of native and non-native disfluencies influences online language comprehension. Journal of Memory and Language, 106, 189-202. doi:10.1016/j.jml.2019.02.006.

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Bosker, Hans R.1, 2, Author           
Van Os, Marjolein1, 3, Author
Does, Rik1, 3, Author
Van Bergen, Geertje4, Author           
Affiliations:
1Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792545              
2Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              
3Radboud University, ou_persistent22              
4Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792551              

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Free keywords: distributional learning, pragmatic inferences, disfluencies, non-native speech, prediction, eye-tracking
 Abstract: Disfluencies, like 'uh', have been shown to help listeners anticipate reference to low-frequency words. The associative account of this 'disfluency bias' proposes that listeners learn to associate disfluency with low-frequency referents based on prior exposure to non-arbitrary disfluency distributions (i.e., greater probability of low-frequency words after disfluencies). However, there is limited evidence for listeners actually tracking disfluency distributions online. The present experiments are the first to show that adult listeners, exposed to a typical or more atypical disfluency distribution (i.e., hearing a talker unexpectedly say uh before high-frequency words), flexibly adjust their predictive strategies to the disfluency distribution at hand (e.g., learn to predict high-frequency referents after disfluency). However, when listeners were presented with the same atypical disfluency distribution but produced by a non-native speaker, no adjustment was observed. This suggests pragmatic inferences can modulate distributional learning, revealing the flexibility of, and constraints on, distributional learning in incremental language comprehension.

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Language(s): eng - English
 Dates: 2019-02-202019-03-062019
 Publication Status: Issued
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 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.jml.2019.02.006
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Title: Journal of Memory and Language
Source Genre: Journal
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Pages: - Volume / Issue: 106 Sequence Number: - Start / End Page: 189 - 202 Identifier: ISSN: 0749-596X
CoNE: https://pure.mpg.de/cone/journals/resource/954928495417