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Journal Article

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

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Bosker,  Hans R.
Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

Van Os,  Marjolein
Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Radboud University;

Does,  Rik
Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Radboud University;

/persons/resource/persons136580

Van Bergen,  Geertje
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;

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(Supplementary material)

Fulltext (public)

Bosker_etal_2019_counting uhm's.pdf
(Publisher version), 3MB

Supplementary Material (public)

1-s2.0-S0749596X19300208-mmc2.docx
(Supplementary material), 152KB

Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0003-0B0E-3
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.