V-037 | EXECUTIVE FUNCTION TRAINING IN ARGENTINIAN CHILDREN: ANALYSIS OF IMPROVEMENT PROFILES USING CLUSTERING METHODS

Cognition, Behavior, and Memory
Author: Martina Boscolo | Email: martiboscolo@gmail.com

Martina Boscolo^1° , Gabriel O. Paz^1°, Melina Vladisauskas^{1°, 2°, 3°}, Diego E. Shalom^{4°, 5°}, María Julia Hermida^6°, Andrea P. Goldin^1°

^1° Universidad Torcuato Di Tella. Escuela de Negocios. Centro de Inteligencia Artificial y Neurociencia (CIAN). Laboratorio de Neurociencia. CONICET
^2° Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
^3° Collège de France, Université Paris Sciences Lettres (PSL), 11 Place Marcelin Berthelot, 75005 Paris, France
^4° Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires 1428, Argentina
^5° CONICET—Universidad de Buenos Aires, Instituto de Física Interdisciplinaria y Aplicada (INFINA), Buenos Aires 1428, Argentina
^6° Universidad Nacional de Hurlingham – Consejo Nacional de Investigaciones Científicas y Técnicas (UNAHUR-CONICET), Villa Tesei, Provincia de Buenos Aires, Argentina

Executive functions (EF) are cognitive functions that allow us to control actions and thoughts and adapt to changing environments. They are important for educational and life success and can be improved through cognitive training. For more than fifteen years, our team has implemented Mate Marote, a free access gaming software to train and assess EF in 4-to-8 year-olds. Interventions last from 1 to 4 months and take place within schools, with successful results. Current efforts are directly aimed at personalized cognitive training. Here, we present a clustering analysis of an experiment in which 66 6-year-olds’ EF were evaluated before and after an intervention of about 27 sessions of 15 minutes each, distributed over 3 months. The aim of this work was to determine how participants are grouped according to their improvement on EF tests after receiving cognitive training. We used k-means method to group data from the experimental group. In addition, the groups were characterized and compared in terms of sociodemographic and academic variables. We discuss the interpretation of the results in relation to the implications for the analysis of performance profiles in cognitive training, as well as the relevance and advantages of applying clustering methods for data analysis in the field.