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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/259145
Ver los metadatos del registro completo
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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 |
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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 |
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publishedVersion |
format |
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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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eng |
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eng |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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Internacional |
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Flux Society |
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