@misc{10481/84005,
year = {2023},
month = {5},
url = {https://hdl.handle.net/10481/84005},
abstract = {Effortful control (EC) is a dimension of temperament that encompass individual differences
in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation
on the development of executive attention, but increasing evidence also shows a significant
contribution of the rearing environment to individual differences in EC. The aim of the current study
was to predict the development of EC at 36 months of age from early attentional and environmental
measures taken in infancy using a machine learning approach. A sample of 78 infants participated in
a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional
tasks were administered at 6 months of age, with two additional measures (i.e., one attentional
measure and another self-restraint measure) being collected at 9 months of age. Parents reported
household environment variables during wave 1, and their child’s EC at 36 months. A machinelearning
algorithm was implemented to identify children with low EC scores at 36 months of age. An
“attention only” model showed greater predictive sensitivity than the “environmental only” model.
However, a model including both attentional and environmental variables was able to classify the
groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socioeconomic
variables together with attention control processes at 6 months, and self-restraint capacity
at 9 months, are the most important predictors of EC. Results suggest a foundational role of executive
attention processes in the development of EC in complex interactions with household environments
and provide a new tool to identify early markers of socio-emotional regulation development.},
organization = {Spanish State Research Agency (Ref: PSI2017-82670-P},
organization = {PID2020-113996GB-I00)},
organization = {PRE2018-083592},
organization = {Maria Zambrano},
organization = {The Spanish Government through the European Union NextGeneration EU funds},
publisher = {MDPI},
keywords = {Effortful control},
keywords = {Self-regulation},
keywords = {Attention},
keywords = {Artificial neural network},
keywords = {Prediction},
keywords = {Machine learning},
title = {Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach},
doi = {10.3390/children10060982},
author = {Musso, Mariel F. and Moyano Flores, Pablo Sebastián and Rico Picó, Josué and Conejero Barbero, Ángela and Ballesteros Duperon, María Ángeles and Rueda Cuerva, María Del Rosario},
}