Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach

Autores
Musso, Mariel Fernanda; Daly, Alana; Combita, Lina; Moyano, Sebastián; Rico Pico, Josué; Conejero, Ángela; Ballesteros Duperon, María Ángeles; Cascallar, Eduardo; Rosario Rueda, María Rosario
Año de publicación
2023
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This study presents four attentional profiles in early development using a novel attention task. This study also aims to develop machine learning models to classify children into these profiles from a broad range of data acquired in infancy. A total of 76 toddlers participated as part of a longitudinal project.
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: Daly, Alana. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Combita, Lina. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Moyano, Sebastián. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Rico Pico, 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 Duperon, María Ángeles. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Rosario Rueda, María Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
International Congress for Psychological Sciences
Bruselas
Bélgica
Association for Psychological Sciences
Materia
Prediction
Attentional profiles
Development
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:11336/263122
Acceder
id CONICETDig_7fe456929d4bdcea1884d1ddb485fca7
oai_identifier_str oai:ri.conicet.gov.ar:11336/263122
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predictive classification of attentional profiles at age 3-years from early development using a machine learning approachMusso, Mariel FernandaDaly, AlanaCombita, LinaMoyano, SebastiánRico Pico, JosuéConejero, ÁngelaBallesteros Duperon, María ÁngelesCascallar, EduardoRosario Rueda, María RosarioPredictionAttentional profilesDevelopmentMachine learninghttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5This study presents four attentional profiles in early development using a novel attention task. This study also aims to develop machine learning models to classify children into these profiles from a broad range of data acquired in infancy. A total of 76 toddlers participated as part of a longitudinal project.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: Daly, Alana. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Combita, Lina. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Moyano, Sebastián. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Rico Pico, 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 Duperon, María Ángeles. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Cascallar, Eduardo. Katholikie Universiteit Leuven; BélgicaFil: Rosario Rueda, María Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaInternational Congress for Psychological SciencesBruselasBélgicaAssociation for Psychological SciencesAssociation for Psychological Science2023info: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/11336/263122Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach; International Congress for Psychological Sciences; Bruselas; Bélgica; 2023; 30-30CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.psychologicalscience.org/conventions/icps-2023/full-convention-programinfo:eu-repo/semantics/altIdentifier/url/https://www.psychologicalscience.org/redesign/wp-content/uploads/2023/03/Poster_Brochure_ICPS_2023.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 Técnicas2025-09-10T13:13:58Zoai:ri.conicet.gov.ar:11336/263122instacron: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 13:13:58.463CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
title Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
spellingShingle Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
Musso, Mariel Fernanda
Prediction
Attentional profiles
Development
Machine learning
title_short Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
title_full Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
title_fullStr Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
title_full_unstemmed Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
title_sort Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach
dc.creator.none.fl_str_mv Musso, Mariel Fernanda
Daly, Alana
Combita, Lina
Moyano, Sebastián
Rico Pico, Josué
Conejero, Ángela
Ballesteros Duperon, María Ángeles
Cascallar, Eduardo
Rosario Rueda, María Rosario
author Musso, Mariel Fernanda
author_facet Musso, Mariel Fernanda
Daly, Alana
Combita, Lina
Moyano, Sebastián
Rico Pico, Josué
Conejero, Ángela
Ballesteros Duperon, María Ángeles
Cascallar, Eduardo
Rosario Rueda, María Rosario
author_role author
author2 Daly, Alana
Combita, Lina
Moyano, Sebastián
Rico Pico, Josué
Conejero, Ángela
Ballesteros Duperon, María Ángeles
Cascallar, Eduardo
Rosario Rueda, María Rosario
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Prediction
Attentional profiles
Development
Machine learning
topic Prediction
Attentional profiles
Development
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 This study presents four attentional profiles in early development using a novel attention task. This study also aims to develop machine learning models to classify children into these profiles from a broad range of data acquired in infancy. A total of 76 toddlers participated as part of a longitudinal project.
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: Daly, Alana. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Combita, Lina. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Moyano, Sebastián. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Rico Pico, 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 Duperon, María Ángeles. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Rosario Rueda, María Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
International Congress for Psychological Sciences
Bruselas
Bélgica
Association for Psychological Sciences
description This study presents four attentional profiles in early development using a novel attention task. This study also aims to develop machine learning models to classify children into these profiles from a broad range of data acquired in infancy. A total of 76 toddlers participated as part of a longitudinal project.
publishDate 2023
dc.date.none.fl_str_mv 2023
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/11336/263122
Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach; International Congress for Psychological Sciences; Bruselas; Bélgica; 2023; 30-30
CONICET Digital
CONICET
url http://hdl.handle.net/11336/263122
identifier_str_mv Predictive classification of attentional profiles at age 3-years from early development using a machine learning approach; International Congress for Psychological Sciences; Bruselas; Bélgica; 2023; 30-30
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.psychologicalscience.org/conventions/icps-2023/full-convention-program
info:eu-repo/semantics/altIdentifier/url/https://www.psychologicalscience.org/redesign/wp-content/uploads/2023/03/Poster_Brochure_ICPS_2023.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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 Association for Psychological Science
publisher.none.fl_str_mv Association for Psychological Science
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
_version_ 1842980742953435136
score 12.993085

Publicaciones similares