Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach Musso, Mariel F. Moyano Flores, Pablo Sebastián Rico Picó, Josué Conejero Barbero, Ángela Ballesteros Duperon, María Ángeles Rueda Cuerva, María Del Rosario Effortful control Self-regulation Attention Artificial neural network Prediction Machine learning 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. 2023-07-26T08:29:09Z 2023-07-26T08:29:09Z 2023-05-31 journal article Musso, M.F.; Moyano, S.; Rico-Picó, J.; Conejero, Á.; Ballesteros-Duperón, M.Á.; Cascallar, E.C.; Rueda, M.R. Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach. Children 2023, 10, 982. [https://doi.org/10.3390/ children10060982] https://hdl.handle.net/10481/84005 10.3390/children10060982 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI