165 | Measuring Executive Functions with a computerized software: results for unsupervised interventions

Cognition, Behavior, and Memory

Author: Melina Vladisauskas | Email: m.vladisauskas@gmail.com

Melina Vladisauskas , Laouen Belloli , Martin A. Miguel , Daniela Macario Cabral , Verónica Nin , Diego E. Shalom , Diego Fernández Slezak , Andrea P. Goldin

1° Centro de Inteligencia Artificial y Neurociencias (CIAN) – Universidad Torcuato di Tella
2° Consejo de Investigaciones Científicas y Tecnológicas – CONICET
3° Laboratorio de Inteligencia Artificial Aplicada (LIAA) – Universidad de Buenos Aires
4° Centro de Investigaciones Básicas en Psicología, UDELAR – ANII

Mate Marote is an open source cognitive-training software aimed at children between 4 and 8 years old. It consists of a set of computerized games specifically tailored to train and evaluate executive functions (EF): a class of processes critical for purposeful, goal-directed behavior, including working memory, planning, flexibility, and cognitive control.
During the last ten years several studies were performed using this software to measure and train children EF at their own schools in supervised interventions. Aiming to scale our interventions, since 2015, we have started to conduct unsupervised, but controlled, studies with children’s own teachers’ help. In this poster we show that children’s EF performance obtained from a battery of standardized tests resulted from unsupervised interventions is comparable to the results reported in the literature. Divided into “time constraint tasks” and “unconstrained” tasks, we were able to replicate expected difficulty effects and an age effect with most of the analyzed variables. We also found important discrepancies between the expected and the observed response time effects, specifically for the time constraint tasks. We implemented a modification for the latter and hereby discuss the benefits and setbacks of this new possible strategy for unsupervised setting testings.
Our results indicate that our battery can be used to measure this EFs in unsupervised settings in the future, allowing us to scale the software use in schools.