The impact of digital media on children's intelligence while controlling for genetic differences in cognition and socioeconomic background

Abstract

Digital media defines modern childhood, but its cognitive effects are unclear and hotly debated. We believe that studies with genetic data could clarify causal claims and correct for the typically unaccounted role of genetic predispositions. Here, we estimated the impact of different types of screen time (watching, socializing, or gaming) on children's intelligence while controlling for the confounding effects of genetic differences in cognition and socioeconomic status. We analyzed 9855 children from the USA who were part of the ABCD dataset with measures of intelligence at baseline (ages 9-10) and after two years. At baseline, time watching (r = - 0.12) and socializing (r = - 0.10) were negatively correlated with intelligence, while gaming did not correlate. After two years, gaming positively impacted intelligence (standardized β = + 0.17), but socializing had no effect. This is consistent with cognitive benefits documented in experimental studies on video gaming. Unexpectedly, watching videos also benefited intelligence (standardized β = + 0.12), contrary to prior research on the effect of watching TV. Although, in a posthoc analysis, this was not significant if parental education (instead of SES) was controlled for. Broadly, our results are in line with research on the malleability of cognitive abilities from environmental factors, such as cognitive training and the Flynn effect.

Figure 1

Path diagram of a strict measurement invariant Latent Change Score model with the change in intelligence from ages 9–10 to 11–12. Screen time Watching, Screen time Socializing, Screen time Gaming, cogPGS (Polygenic scores for cognitive performance), and SES (socioeconomic status) are exogenous variables, each already accounting for the effect of the others on baseline intelligence and on the change in intelligence after two years. All variables are standardized. (Which is why the loadings differ between time points—the constraining of loadings and intercepts must be done for the unstandardized estimates.) Non-significant values are marked with “n.s.”. Following convention, rectangles represent observed or exogenous variables and circles represent latent variables (screen time types are shown with only one rectangle for aesthetic reasons—they are actually three distinct rectangles). Single-headed arrows denote regression weights, while double-headed arrows represent variances, covariances, or errors. Standardized betas.

Figure 2

Path diagram of a strict measurement invariant Latent Change Score model with the change in intelligence from ages 9–10 to 11–12. Screen time Gaming, cogPGS (Polygenic scores for cognitive performance), and SES (socioeconomic status) are exogenous variables, each already accounting for the effect of the others on baseline intelligence and on the change in intelligence after two years. All variables are standardized. Non-significant values are marked with “n.s.”. Following convention, rectangles represent observed or exogenous variables and circles represent latent variables. Single-headed arrows denote regression weights, while double-headed arrows represent variances, covariances, or errors. Standardized betas.

Figure 3

Density plot of time spent Gaming (raw values) between boys and girls at ages 9–10.