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

Autor/inn/en	Musso, Mariel F.; Cómbita, Lina M.; Cascallar, Eduardo C.; Rueda, M. Rosario
Titel	Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
Quelle	In: Mind, Brain, and Education, 16 (2022) 4, S.300-317 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Musso, Mariel F.) ORCID (Cómbita, Lina M.) ORCID (Cascallar, Eduardo C.) ORCID (Rueda, M. Rosario)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1751-2271
DOI	10.1111/mbe.12336
Schlagwörter	Genetics; Artificial Intelligence; Gender Differences; Age Differences; Intervention; Individual Differences; Training Methods; Biochemistry; Physiology; Intelligence Tests; Models; Accuracy; Classification; Executive Function; Outcomes of Education; Kaufman Brief Intelligence Test + Suchen Sie Ihr Suchwort? Humangenetik; Künstliche Intelligenz; Geschlechterkonflikt; Age; Difference; Age difference; Altersunterschied; Individueller Unterschied; Didaktik; Trainingsmaßnahme; Biochemie; Physiologie; Intelligence test; Intelligenztest; Analogiemodell; Classification system; Klassifikation; Klassifikationssystem; Lernleistung; Schulerfolg
Abstract	The objective of this research was to develop robust predictive models of the gains in working memory (WM) and fluid intelligence (Gf) following executive attention training in children, using genetic markers, gender, and age variables. We explore the influence of genetic variables on individual differences in susceptibility to intervention. Sixty-six children (males: 54.2%) aged 50.9-75.9 months participated in a four-weeks computerized training program. Information on genes involved in the regulation of dopamine, serotonin, norepinephrine, and acetylcholine was collected. The standardized pre- to post-training gains of two dependent measures were considered: WM Span backwards condition (WISC-III) and the IQ-f factor from the Kaufman Brief Intelligence Test (K-BIT). A machine-learning methodology was implemented utilizing multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm. Both ANN models reached high overall accuracy in their predictive classification. Variations in genes involved in dopamine and norepinephrine neurotransmission affect children's susceptibility to benefit from executive attention training, a pattern that is consistent with previous studies. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01