Prediction of attention profiles at age 3 and 4 years using a machine learning approach

Autores
Musso, Mariel Fernanda; Cascallar, Eduardo; Rueda Cuerva, María del Rosario
Año de publicación
2022
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Attentional development involves complex interactions between multiple cognitive processes and other systems. Individual differences in attentional tasks depend not only on age-related changes, but also on nonlinear relationships among genetic, temperament, cognitive and physical conditions, environment, and motivation. Classical statistical methods have serious constrains to address this complex nature. Therefore, this study proposes to use several machine learning algorithms (ML) to predict characteristic results of normal development and deviations in the development of executive attention at 3 and 4 years old, considering cognitive, behavioral and EEG data, and parent reported measures, collected in a longitudinal study. This approach is expected to accurately classify the attentional profiles based on the task performance, and to identify very early markers of executive attention development. This study is part of one funded longitudinal project involving an initial sample of 151 babies and their families who participated on three waves of data collection (at 6, 9, and 16-18 months-old). Two waves of data collection are added: at 36 and 48 months old. Several measures were taken, involving behavioral tasks, eye-tracking tasks, EEG/ERPs protocols, parent-reported measures of child temperament and home environment. Other measures are included in the last two waves: WPPSI-IV, spatial conflict task, sustained attention task, visual sequence learning task, delay of gratification task, EEG resting protocol, BeeAT Task, child's temperament, family SES, parenting styles, parents' mental health, and ASD/ADHD symptomatology. ML methods (e.g., artificial neural networks, fast large margin, decision trees, etc.) and time series analyses will be developed through training and cross-validation phases to study the attentional trajectories across ages. Sensitivity analyses will be carried out to provide measures of therelative importance of each predictor.
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: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Rueda Cuerva, María del Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
10th International Congress for Integrative Developmental Cognitive Neuroscience
Paris
Francia
Flux Society
Materia
Prediction
Attention profiles
Children
Machine Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/259145

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spelling Prediction of attention profiles at age 3 and 4 years using a machine learning approachMusso, Mariel FernandaCascallar, EduardoRueda Cuerva, María del RosarioPredictionAttention profilesChildrenMachine Learninghttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Attentional development involves complex interactions between multiple cognitive processes and other systems. Individual differences in attentional tasks depend not only on age-related changes, but also on nonlinear relationships among genetic, temperament, cognitive and physical conditions, environment, and motivation. Classical statistical methods have serious constrains to address this complex nature. Therefore, this study proposes to use several machine learning algorithms (ML) to predict characteristic results of normal development and deviations in the development of executive attention at 3 and 4 years old, considering cognitive, behavioral and EEG data, and parent reported measures, collected in a longitudinal study. This approach is expected to accurately classify the attentional profiles based on the task performance, and to identify very early markers of executive attention development. This study is part of one funded longitudinal project involving an initial sample of 151 babies and their families who participated on three waves of data collection (at 6, 9, and 16-18 months-old). Two waves of data collection are added: at 36 and 48 months old. Several measures were taken, involving behavioral tasks, eye-tracking tasks, EEG/ERPs protocols, parent-reported measures of child temperament and home environment. Other measures are included in the last two waves: WPPSI-IV, spatial conflict task, sustained attention task, visual sequence learning task, delay of gratification task, EEG resting protocol, BeeAT Task, child's temperament, family SES, parenting styles, parents' mental health, and ASD/ADHD symptomatology. ML methods (e.g., artificial neural networks, fast large margin, decision trees, etc.) and time series analyses will be developed through training and cross-validation phases to study the attentional trajectories across ages. Sensitivity analyses will be carried out to provide measures of therelative importance of each predictor.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: Cascallar, Eduardo. Katholikie Universiteit Leuven; BélgicaFil: Rueda Cuerva, María del Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España10th International Congress for Integrative Developmental Cognitive NeuroscienceParisFranciaFlux SocietyFlux Society2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/259145Prediction of attention profiles at age 3 and 4 years using a machine learning approach; 10th International Congress for Integrative Developmental Cognitive Neuroscience; Paris; Francia; 2022; 200-201CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://fluxsociety.org/2022-paris/info:eu-repo/semantics/altIdentifier/url/https://fluxsociety.org/wp-content/uploads/2022/08/2022-Flux-Abstract-Book.pdfInternacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:43:43Zoai:ri.conicet.gov.ar:11336/259145instacron: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:34982025-09-29 09:43:43.56CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Prediction of attention profiles at age 3 and 4 years using a machine learning approach
title Prediction of attention profiles at age 3 and 4 years using a machine learning approach
spellingShingle Prediction of attention profiles at age 3 and 4 years using a machine learning approach
Musso, Mariel Fernanda
Prediction
Attention profiles
Children
Machine Learning
title_short Prediction of attention profiles at age 3 and 4 years using a machine learning approach
title_full Prediction of attention profiles at age 3 and 4 years using a machine learning approach
title_fullStr Prediction of attention profiles at age 3 and 4 years using a machine learning approach
title_full_unstemmed Prediction of attention profiles at age 3 and 4 years using a machine learning approach
title_sort Prediction of attention profiles at age 3 and 4 years using a machine learning approach
dc.creator.none.fl_str_mv Musso, Mariel Fernanda
Cascallar, Eduardo
Rueda Cuerva, María del Rosario
author Musso, Mariel Fernanda
author_facet Musso, Mariel Fernanda
Cascallar, Eduardo
Rueda Cuerva, María del Rosario
author_role author
author2 Cascallar, Eduardo
Rueda Cuerva, María del Rosario
author2_role author
author
dc.subject.none.fl_str_mv Prediction
Attention profiles
Children
Machine Learning
topic Prediction
Attention profiles
Children
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 Attentional development involves complex interactions between multiple cognitive processes and other systems. Individual differences in attentional tasks depend not only on age-related changes, but also on nonlinear relationships among genetic, temperament, cognitive and physical conditions, environment, and motivation. Classical statistical methods have serious constrains to address this complex nature. Therefore, this study proposes to use several machine learning algorithms (ML) to predict characteristic results of normal development and deviations in the development of executive attention at 3 and 4 years old, considering cognitive, behavioral and EEG data, and parent reported measures, collected in a longitudinal study. This approach is expected to accurately classify the attentional profiles based on the task performance, and to identify very early markers of executive attention development. This study is part of one funded longitudinal project involving an initial sample of 151 babies and their families who participated on three waves of data collection (at 6, 9, and 16-18 months-old). Two waves of data collection are added: at 36 and 48 months old. Several measures were taken, involving behavioral tasks, eye-tracking tasks, EEG/ERPs protocols, parent-reported measures of child temperament and home environment. Other measures are included in the last two waves: WPPSI-IV, spatial conflict task, sustained attention task, visual sequence learning task, delay of gratification task, EEG resting protocol, BeeAT Task, child's temperament, family SES, parenting styles, parents' mental health, and ASD/ADHD symptomatology. ML methods (e.g., artificial neural networks, fast large margin, decision trees, etc.) and time series analyses will be developed through training and cross-validation phases to study the attentional trajectories across ages. Sensitivity analyses will be carried out to provide measures of therelative importance of each predictor.
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: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Rueda Cuerva, María del Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
10th International Congress for Integrative Developmental Cognitive Neuroscience
Paris
Francia
Flux Society
description Attentional development involves complex interactions between multiple cognitive processes and other systems. Individual differences in attentional tasks depend not only on age-related changes, but also on nonlinear relationships among genetic, temperament, cognitive and physical conditions, environment, and motivation. Classical statistical methods have serious constrains to address this complex nature. Therefore, this study proposes to use several machine learning algorithms (ML) to predict characteristic results of normal development and deviations in the development of executive attention at 3 and 4 years old, considering cognitive, behavioral and EEG data, and parent reported measures, collected in a longitudinal study. This approach is expected to accurately classify the attentional profiles based on the task performance, and to identify very early markers of executive attention development. This study is part of one funded longitudinal project involving an initial sample of 151 babies and their families who participated on three waves of data collection (at 6, 9, and 16-18 months-old). Two waves of data collection are added: at 36 and 48 months old. Several measures were taken, involving behavioral tasks, eye-tracking tasks, EEG/ERPs protocols, parent-reported measures of child temperament and home environment. Other measures are included in the last two waves: WPPSI-IV, spatial conflict task, sustained attention task, visual sequence learning task, delay of gratification task, EEG resting protocol, BeeAT Task, child's temperament, family SES, parenting styles, parents' mental health, and ASD/ADHD symptomatology. ML methods (e.g., artificial neural networks, fast large margin, decision trees, etc.) and time series analyses will be developed through training and cross-validation phases to study the attentional trajectories across ages. Sensitivity analyses will be carried out to provide measures of therelative importance of each predictor.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Book
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/259145
Prediction of attention profiles at age 3 and 4 years using a machine learning approach; 10th International Congress for Integrative Developmental Cognitive Neuroscience; Paris; Francia; 2022; 200-201
CONICET Digital
CONICET
url http://hdl.handle.net/11336/259145
identifier_str_mv Prediction of attention profiles at age 3 and 4 years using a machine learning approach; 10th International Congress for Integrative Developmental Cognitive Neuroscience; Paris; Francia; 2022; 200-201
CONICET Digital
CONICET
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language eng
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Flux Society
publisher.none.fl_str_mv Flux Society
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