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  Measuring prosodic predictability in children’s home language environments

MacDonald, K., Räsänen, O., Casillas, M., & Warlaumont, A. S. (2020). Measuring prosodic predictability in children’s home language environments. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 695-701). Montreal, QB: Cognitive Science Society.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0006-CCC1-A Version Permalink: https://hdl.handle.net/21.11116/0000-0006-EE21-9
Genre: Conference Paper

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Macdonald_etal_2020.pdf (Publisher version), 752KB
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©2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY).
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 Creators:
MacDonald, Kyle1, Author
Räsänen, Okko2, Author
Casillas, Marisa3, Author           
Warlaumont, Anne S.4, Author
Affiliations:
1McD Tech Labs, Mountain View, CA, USA, ou_persistent22              
2Aalto University, Espoo, Finland, ou_persistent22              
3Language Development Department, MPI for Psycholinguistics, Max Planck Society, ou_2340691              
4University of California Los Angeles, Los Angeles, CA,USA, ou_persistent22              

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 Abstract: Children learn language from the speech in their home environment. Recent work shows that more infant-directed speech
(IDS) leads to stronger lexical development. But what makes IDS a particularly useful learning signal? Here, we expand on an attention-based account first proposed by Räsänen et al. (2018): that prosodic modifications make IDS less predictable, and thus more interesting. First, we reproduce the critical finding from Räsänen et al.: that lab-recorded IDS pitch is less predictable compared to adult-directed speech (ADS). Next, we show that this result generalizes to the home language environment, finding that IDS in daylong recordings is also less predictable than ADS but that this pattern is much less robust than for IDS recorded in the lab. These results link experimental work on attention and prosodic modifications of IDS to real-world language-learning environments, highlighting some challenges of scaling up analyses of IDS to larger datasets that better capture children’s actual input.

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Language(s): eng - English
 Dates: 2020-07
 Publication Status: Published online
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Title: the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020)
Place of Event: Toronto, Canada
Start-/End Date: 2020-07-29 - 2020-08-01

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Title: Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020)
Source Genre: Proceedings
 Creator(s):
Denison, Stephanie, Editor
Mack, Michael, Editor
Xu, Yang, Editor
Armstrong, Blair C., Editor
Affiliations:
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Publ. Info: Montreal, QB : Cognitive Science Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 695 - 701 Identifier: -