Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach

Autores
Musso, Mariel Fernanda; Cómbita, Lina M.; Cascallar, Eduardo C.; Rueda, M. Rosario
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Previous studies have demonstrated the psychological impact of stressful events related to an infectious disease outbreak. This impact may be moderated by the perception of risk and individual differences in personality. The aim of this study was to analyze the effects of the personality profiles and mental health on the perceived risk (being infected, getting hospitalized, and dying from COVID-19) and on preventive behaviors (wash your hands, stay at home, maintain social distance, touch your face, and mask use). A total sample of 126 Argentine adults, both genders, participated and filled in the Revised NEO Personality Inventory (NEO PI-R), the Symptom Checklist-90 (SCL-90) scale, a sociodemographic questionnaire, and COVID-19 estimates regarding risk perception and preventive behaviors. Results show that people with undercontrolled personality profile and high interpersonal sensitivity overestimated their probability of getting infected, hospitalization, and dying from COVID-19. In addition, the resilient profile group with high anxiety overestimated the probability of hospitalization and dying; the undercontrolled profile group with high anxiety, phobic anxiety, or psychoticism, also overestimated their probability of dying; the undercontrolled profile people with high interpersonal sensitivity, or high anxiety reported higher probabilities of maintaining social distance. Anxiety and depression symptoms explain a low percentage of the perceived risk variance; while conscientiousness, together with mental health were able to explain the estimated probability of engaging in protective behaviors. These findings could be useful to implement more effective and realistic strategies to promote the adoption of preventive behaviors.
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. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento; España. Universidad Argentina de la Empresa; Argentina
Fil: Cómbita, Lina M.. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Cascallar, Eduardo C.. Katholikie Universiteit Leuven; Bélgica
Fil: Rueda, M. Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Materia
COGNITIVE GAINS
MACHINE LEARNING
GENETIC VARIATION
PREDICTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/232216
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning ApproachMusso, Mariel FernandaCómbita, Lina M.Cascallar, Eduardo C.Rueda, M. RosarioCOGNITIVE GAINSMACHINE LEARNINGGENETIC VARIATIONPREDICTIONhttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Previous studies have demonstrated the psychological impact of stressful events related to an infectious disease outbreak. This impact may be moderated by the perception of risk and individual differences in personality. The aim of this study was to analyze the effects of the personality profiles and mental health on the perceived risk (being infected, getting hospitalized, and dying from COVID-19) and on preventive behaviors (wash your hands, stay at home, maintain social distance, touch your face, and mask use). A total sample of 126 Argentine adults, both genders, participated and filled in the Revised NEO Personality Inventory (NEO PI-R), the Symptom Checklist-90 (SCL-90) scale, a sociodemographic questionnaire, and COVID-19 estimates regarding risk perception and preventive behaviors. Results show that people with undercontrolled personality profile and high interpersonal sensitivity overestimated their probability of getting infected, hospitalization, and dying from COVID-19. In addition, the resilient profile group with high anxiety overestimated the probability of hospitalization and dying; the undercontrolled profile group with high anxiety, phobic anxiety, or psychoticism, also overestimated their probability of dying; the undercontrolled profile people with high interpersonal sensitivity, or high anxiety reported higher probabilities of maintaining social distance. Anxiety and depression symptoms explain a low percentage of the perceived risk variance; while conscientiousness, together with mental health were able to explain the estimated probability of engaging in protective behaviors. These findings could be useful to implement more effective and realistic strategies to promote the adoption of preventive behaviors.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. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento; España. Universidad Argentina de la Empresa; ArgentinaFil: Cómbita, Lina M.. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaFil: Cascallar, Eduardo C.. Katholikie Universiteit Leuven; BélgicaFil: Rueda, M. Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; EspañaJohn Wiley & Sons2022-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/232216Musso, Mariel Fernanda; Cómbita, Lina M.; Cascallar, Eduardo C.; Rueda, M. Rosario; Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach; John Wiley & Sons; Mind, Brain, and Education; 16; 4; 3-2022; 300-3171751-228XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/mbe.12336info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1111/mbe.12336info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:51:36Zoai:ri.conicet.gov.ar:11336/232216instacron: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 09:51:36.448CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
title Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
spellingShingle Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
Musso, Mariel Fernanda
COGNITIVE GAINS
MACHINE LEARNING
GENETIC VARIATION
PREDICTION
title_short Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
title_full Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
title_fullStr Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
title_full_unstemmed Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
title_sort Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
dc.creator.none.fl_str_mv Musso, Mariel Fernanda
Cómbita, Lina M.
Cascallar, Eduardo C.
Rueda, M. Rosario
author Musso, Mariel Fernanda
author_facet Musso, Mariel Fernanda
Cómbita, Lina M.
Cascallar, Eduardo C.
Rueda, M. Rosario
author_role author
author2 Cómbita, Lina M.
Cascallar, Eduardo C.
Rueda, M. Rosario
author2_role author
author
author
dc.subject.none.fl_str_mv COGNITIVE GAINS
MACHINE LEARNING
GENETIC VARIATION
PREDICTION
topic COGNITIVE GAINS
MACHINE LEARNING
GENETIC VARIATION
PREDICTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Previous studies have demonstrated the psychological impact of stressful events related to an infectious disease outbreak. This impact may be moderated by the perception of risk and individual differences in personality. The aim of this study was to analyze the effects of the personality profiles and mental health on the perceived risk (being infected, getting hospitalized, and dying from COVID-19) and on preventive behaviors (wash your hands, stay at home, maintain social distance, touch your face, and mask use). A total sample of 126 Argentine adults, both genders, participated and filled in the Revised NEO Personality Inventory (NEO PI-R), the Symptom Checklist-90 (SCL-90) scale, a sociodemographic questionnaire, and COVID-19 estimates regarding risk perception and preventive behaviors. Results show that people with undercontrolled personality profile and high interpersonal sensitivity overestimated their probability of getting infected, hospitalization, and dying from COVID-19. In addition, the resilient profile group with high anxiety overestimated the probability of hospitalization and dying; the undercontrolled profile group with high anxiety, phobic anxiety, or psychoticism, also overestimated their probability of dying; the undercontrolled profile people with high interpersonal sensitivity, or high anxiety reported higher probabilities of maintaining social distance. Anxiety and depression symptoms explain a low percentage of the perceived risk variance; while conscientiousness, together with mental health were able to explain the estimated probability of engaging in protective behaviors. These findings could be useful to implement more effective and realistic strategies to promote the adoption of preventive behaviors.
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. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento; España. Universidad Argentina de la Empresa; Argentina
Fil: Cómbita, Lina M.. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
Fil: Cascallar, Eduardo C.. Katholikie Universiteit Leuven; Bélgica
Fil: Rueda, M. Rosario. Universidad de Granada. Centro de Investigación Mente, Cerebro y Comportamiento.; España
description Previous studies have demonstrated the psychological impact of stressful events related to an infectious disease outbreak. This impact may be moderated by the perception of risk and individual differences in personality. The aim of this study was to analyze the effects of the personality profiles and mental health on the perceived risk (being infected, getting hospitalized, and dying from COVID-19) and on preventive behaviors (wash your hands, stay at home, maintain social distance, touch your face, and mask use). A total sample of 126 Argentine adults, both genders, participated and filled in the Revised NEO Personality Inventory (NEO PI-R), the Symptom Checklist-90 (SCL-90) scale, a sociodemographic questionnaire, and COVID-19 estimates regarding risk perception and preventive behaviors. Results show that people with undercontrolled personality profile and high interpersonal sensitivity overestimated their probability of getting infected, hospitalization, and dying from COVID-19. In addition, the resilient profile group with high anxiety overestimated the probability of hospitalization and dying; the undercontrolled profile group with high anxiety, phobic anxiety, or psychoticism, also overestimated their probability of dying; the undercontrolled profile people with high interpersonal sensitivity, or high anxiety reported higher probabilities of maintaining social distance. Anxiety and depression symptoms explain a low percentage of the perceived risk variance; while conscientiousness, together with mental health were able to explain the estimated probability of engaging in protective behaviors. These findings could be useful to implement more effective and realistic strategies to promote the adoption of preventive behaviors.
publishDate 2022
dc.date.none.fl_str_mv 2022-03
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/11336/232216
Musso, Mariel Fernanda; Cómbita, Lina M.; Cascallar, Eduardo C.; Rueda, M. Rosario; Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach; John Wiley & Sons; Mind, Brain, and Education; 16; 4; 3-2022; 300-317
1751-228X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/232216
identifier_str_mv Musso, Mariel Fernanda; Cómbita, Lina M.; Cascallar, Eduardo C.; Rueda, M. Rosario; Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach; John Wiley & Sons; Mind, Brain, and Education; 16; 4; 3-2022; 300-317
1751-228X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1111/mbe.12336
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1111/mbe.12336
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
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 13.070432

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