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Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning

Rhythm is key to language acquisition. Across languages, rhythmic features highlight fundamental linguistic elements of the sound stream and structural relations among them. A sensitivity to rhythmic features, which begins in utero, is evident at birth. What is less clear is whether rhythm supports...

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Autores principales: Lau, Joseph C. Y., Fyshe, Alona, Waxman, Sandra R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178268/
https://www.ncbi.nlm.nih.gov/pubmed/35693512
http://dx.doi.org/10.3389/fpsyg.2022.894405
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author Lau, Joseph C. Y.
Fyshe, Alona
Waxman, Sandra R.
author_facet Lau, Joseph C. Y.
Fyshe, Alona
Waxman, Sandra R.
author_sort Lau, Joseph C. Y.
collection PubMed
description Rhythm is key to language acquisition. Across languages, rhythmic features highlight fundamental linguistic elements of the sound stream and structural relations among them. A sensitivity to rhythmic features, which begins in utero, is evident at birth. What is less clear is whether rhythm supports infants' earliest links between language and cognition. Prior evidence has documented that for infants as young as 3 and 4 months, listening to their native language (English) supports the core cognitive capacity of object categorization. This precocious link is initially part of a broader template: listening to a non-native language from the same rhythmic class as (e.g., German, but not Cantonese) and to vocalizations of non-human primates (e.g., lemur, Eulemur macaco flavifrons, but not birds e.g., zebra-finches, Taeniopygia guttata) provide English-acquiring infants the same cognitive advantage as does listening to their native language. Here, we implement a machine-learning (ML) approach to ask whether there are acoustic properties, available on the surface of these vocalizations, that permit infants' to identify which vocalizations are candidate links to cognition. We provided the model with a robust sample of vocalizations that, from the vantage point of English-acquiring 4-month-olds, either support object categorization (English, German, lemur vocalizations) or fail to do so (Cantonese, zebra-finch vocalizations). We assess (a) whether supervised ML classification models can distinguish those vocalizations that support cognition from those that do not, and (b) which class(es) of acoustic features (including rhythmic, spectral envelope, and pitch features) best support that classification. Our analysis reveals that principal components derived from rhythm-relevant acoustic features were among the most robust in supporting the classification. Classifications performed using temporal envelope components were also robust. These new findings provide in principle evidence that infants' earliest links between vocalizations and cognition may be subserved by their perceptual sensitivity to rhythmic and spectral elements available on the surface of these vocalizations, and that these may guide infants' identification of candidate links to cognition.
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spelling pubmed-91782682022-06-10 Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning Lau, Joseph C. Y. Fyshe, Alona Waxman, Sandra R. Front Psychol Psychology Rhythm is key to language acquisition. Across languages, rhythmic features highlight fundamental linguistic elements of the sound stream and structural relations among them. A sensitivity to rhythmic features, which begins in utero, is evident at birth. What is less clear is whether rhythm supports infants' earliest links between language and cognition. Prior evidence has documented that for infants as young as 3 and 4 months, listening to their native language (English) supports the core cognitive capacity of object categorization. This precocious link is initially part of a broader template: listening to a non-native language from the same rhythmic class as (e.g., German, but not Cantonese) and to vocalizations of non-human primates (e.g., lemur, Eulemur macaco flavifrons, but not birds e.g., zebra-finches, Taeniopygia guttata) provide English-acquiring infants the same cognitive advantage as does listening to their native language. Here, we implement a machine-learning (ML) approach to ask whether there are acoustic properties, available on the surface of these vocalizations, that permit infants' to identify which vocalizations are candidate links to cognition. We provided the model with a robust sample of vocalizations that, from the vantage point of English-acquiring 4-month-olds, either support object categorization (English, German, lemur vocalizations) or fail to do so (Cantonese, zebra-finch vocalizations). We assess (a) whether supervised ML classification models can distinguish those vocalizations that support cognition from those that do not, and (b) which class(es) of acoustic features (including rhythmic, spectral envelope, and pitch features) best support that classification. Our analysis reveals that principal components derived from rhythm-relevant acoustic features were among the most robust in supporting the classification. Classifications performed using temporal envelope components were also robust. These new findings provide in principle evidence that infants' earliest links between vocalizations and cognition may be subserved by their perceptual sensitivity to rhythmic and spectral elements available on the surface of these vocalizations, and that these may guide infants' identification of candidate links to cognition. Frontiers Media S.A. 2022-05-26 /pmc/articles/PMC9178268/ /pubmed/35693512 http://dx.doi.org/10.3389/fpsyg.2022.894405 Text en Copyright © 2022 Lau, Fyshe and Waxman. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Lau, Joseph C. Y.
Fyshe, Alona
Waxman, Sandra R.
Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning
title Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning
title_full Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning
title_fullStr Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning
title_full_unstemmed Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning
title_short Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning
title_sort rhythm may be key to linking language and cognition in young infants: evidence from machine learning
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178268/
https://www.ncbi.nlm.nih.gov/pubmed/35693512
http://dx.doi.org/10.3389/fpsyg.2022.894405
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