Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 May 31;10(6):982.
doi: 10.3390/children10060982.

Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach

Mariel F Musso  1   2   3   4 , Sebastián Moyano  1   4 , Josué Rico-Picó  1   4 , Ángela Conejero  4   5 , M Ángeles Ballesteros-Duperón  4   6 , Eduardo C Cascallar  7 , M Rosario Rueda  1   4
Affiliations

Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach

Mariel F Musso et al. Children (Basel). .

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 machine-learning 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 socio-economic 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.

Keywords: artificial neural networks; attention; effortful control; machine learning; prediction; self-regulation.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Procedure for the Visual Sequence Learning task for 6-month-old infants.
Figure 2
Figure 2
Procedure for the Visual Sequence Learning task for 9-month-old infants.
Figure 3
Figure 3
Procedure for the switching task. Locations of stimulus presentation in the pre-switch block were counterbalanced between participants.
Figure 4
Figure 4
Predictive weights of the variables participating in the best model for the predictive classification of low EC.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Rueda M.R., Checa P., Rothbart M.K. Contributions of Attentional Control to Socioemotional and Academic Development. Early Educ. Dev. 2010;21:744–764. doi: 10.1080/10409289.2010.510055. - DOI
    1. Robson D.A., Allen M.S., Howard S.J. Self-Regulation in Childhood as a Predictor of Future Outcomes: A Meta-Analytic Review. Psychol. Bull. 2020;146:324–354. doi: 10.1037/bul0000227. - DOI - PubMed
    1. Miyake A., Friedman N.P., Emerson M.J., Witzki A.H., Howerter A., Wager T.D. The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cogn. Psychol. 2000;41:49–100. doi: 10.1006/cogp.1999.0734. - DOI - PubMed
    1. Blair C., Zelazo P.D., Greenberg M.T. The Measurement of Executive Function in Early Childhood. Dev. Neuropsychol. 2005;28:561–571. doi: 10.1207/s15326942dn2802_1. - DOI - PubMed
    1. Allan N.P., Hume L.E., Allan D.M., Farrington A.L., Lonigan C.J. Relations between Inhibitory Control and the Development of Academic Skills in Preschool and Kindergarten: A Meta-Analysis. Dev. Psychol. 2014;50:2368–2379. doi: 10.1037/a0037493. - DOI - PubMed

LinkOut - more resources

-