Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach

Autores
Musso, Mariel Fernanda; Moyano, Sebastián; Rico Picó, Josué; Conejero, Ángela; Ballesteros Duperón, María Ángeles; Cascallar, Eduardo; Rueda, María Rosario
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child’s EC at 36 months. A machine learning algorithm was implemented to identify children with low EC scores at 36 months of age. An “attention only” model showed greater predictive sensitivity than the “environmental only” model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socioeconomic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC.
Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
Fil: Moyano, Sebastián. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Rico Picó, Josué. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Conejero, Ángela. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Ballesteros Duperón, María Ángeles. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Rueda, María Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Materia
Effortful control
Self-regulation
Attention
Artificial neural networks
Prediction
Machine learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/234177
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/234177
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning ApproachMusso, Mariel FernandaMoyano, SebastiánRico Picó, JosuéConejero, ÁngelaBallesteros Duperón, María ÁngelesCascallar, EduardoRueda, María RosarioEffortful controlSelf-regulationAttentionArtificial neural networksPredictionMachine learninghttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child’s EC at 36 months. A machine learning algorithm was implemented to identify children with low EC scores at 36 months of age. An “attention only” model showed greater predictive sensitivity than the “environmental only” model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socioeconomic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC.Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Moyano, Sebastián. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Rico Picó, Josué. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Conejero, Ángela. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Ballesteros Duperón, María Ángeles. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Cascallar, Eduardo. Katholikie Universiteit Leuven; BélgicaFil: Rueda, María Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaMDPI2023-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/234177Musso, Mariel Fernanda; Moyano, Sebastián; Rico Picó, Josué; Conejero, Ángela; Ballesteros Duperón, María Ángeles; et al.; Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach; MDPI; Children; 10; 6; 5-2023; 1-202227-9067CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/children10060982info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2227-9067/10/6/982info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:10:56Zoai:ri.conicet.gov.ar:11336/234177instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:10:57.257CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
title Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
spellingShingle Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
Musso, Mariel Fernanda
Effortful control
Self-regulation
Attention
Artificial neural networks
Prediction
Machine learning
title_short Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
title_full Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
title_fullStr Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
title_full_unstemmed Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
title_sort Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
dc.creator.none.fl_str_mv Musso, Mariel Fernanda
Moyano, Sebastián
Rico Picó, Josué
Conejero, Ángela
Ballesteros Duperón, María Ángeles
Cascallar, Eduardo
Rueda, María Rosario
author Musso, Mariel Fernanda
author_facet Musso, Mariel Fernanda
Moyano, Sebastián
Rico Picó, Josué
Conejero, Ángela
Ballesteros Duperón, María Ángeles
Cascallar, Eduardo
Rueda, María Rosario
author_role author
author2 Moyano, Sebastián
Rico Picó, Josué
Conejero, Ángela
Ballesteros Duperón, María Ángeles
Cascallar, Eduardo
Rueda, María Rosario
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Effortful control
Self-regulation
Attention
Artificial neural networks
Prediction
Machine learning
topic Effortful control
Self-regulation
Attention
Artificial neural networks
Prediction
Machine learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child’s EC at 36 months. A machine learning algorithm was implemented to identify children with low EC scores at 36 months of age. An “attention only” model showed greater predictive sensitivity than the “environmental only” model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socioeconomic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC.
Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
Fil: Moyano, Sebastián. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Rico Picó, Josué. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Conejero, Ángela. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Ballesteros Duperón, María Ángeles. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Rueda, María Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
description Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child’s EC at 36 months. A machine learning algorithm was implemented to identify children with low EC scores at 36 months of age. An “attention only” model showed greater predictive sensitivity than the “environmental only” model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socioeconomic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC.
publishDate 2023
dc.date.none.fl_str_mv 2023-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/234177
Musso, Mariel Fernanda; Moyano, Sebastián; Rico Picó, Josué; Conejero, Ángela; Ballesteros Duperón, María Ángeles; et al.; Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach; MDPI; Children; 10; 6; 5-2023; 1-20
2227-9067
CONICET Digital
CONICET
url http://hdl.handle.net/11336/234177
identifier_str_mv Musso, Mariel Fernanda; Moyano, Sebastián; Rico Picó, Josué; Conejero, Ángela; Ballesteros Duperón, María Ángeles; et al.; Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach; MDPI; Children; 10; 6; 5-2023; 1-20
2227-9067
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/children10060982
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2227-9067/10/6/982
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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