Social media use bears no measurable relationship to adolescents' cognitive skills; negative effects of social media use may have been overstated in past research

Associations between social media use and cognitive abilities: Results from a large-scale study of adolescents. Stefan Stieger, Sabine Wunderl. Computers in Human Behavior, June 16 2022, 107358. https://doi.org/10.1016/j.chb.2022.107358

Highlights

• Social media use is not substantially associated with results from tests of cognitive abilities and skills.

• Results from random-forest models suggest low importance of all test results compared to known sex- and age-differences.

• Negative effects of social media use may have been overstated in past research.

Abstract: In adolescence, smartphone use in general and social media use in particular has often been associated with negative effects, such as higher anxiety levels and body dissatisfaction. Other outcomes – such as fundamental cognitive abilities and skills (e.g., intelligence, information processing, spatial perception) – have rarely been the focus of research. Here, we analysed data from a large sample of adolescents (12–16 years; N > 12,000) who performed a series of psychometric tests ranging from intelligence, spatial perception, and information processing, to practical numeracy, and compared their test results with their social media usage (average active and passive time per day, problematic social media use). We additionally applied a random-forest model approach, useful for designs with many predictors and expected small effect sizes. Almost all associations did not outperform known age- and sex-differences on social media use; that is, effect sizes were small-to-tiny and had low importance in the random-forest analyses compared to dominant demographic effects. Negative effects of social media use may have been overstated in past research, at least in samples with adolescents.

Keywords: Cognitive abilitySocial media useAdolescentsIntelligenceRandom-forest model

4. Discussion

The results of the present study can be summarised and discussed as follows. The PSMU scale using a Likert-type response option revealed very good reliability, whereas the reliability was very low for the binary response format (see also Andreassen et al., 2013; Wartberg et al., 2017). Therefore, Likert-type scales should be given preference for the PSMU scale, at least when used with adolescent samples.

The correlation between PSMU scores and the average time using social media per day was low (∼0.27; see also Wartberg et al., 2017, who also used adolescent samples and found r = 0.34 for pathological Internet use in general). This supports the assumption that adolescents of that age might miss an objective reference frame of which social media use behaviour is acceptable. As long as the core family (e.g., parents) does not provide negative feedback about adolescents’ possible social media overuse (or even show similar social media behaviour themselves), adolescents will probably not state problems in the PSMU scale although they may use social media for a substantial amount of time on average each day. Additionally, using a parent-form of the PSMU scale or non-parametric measures of social media use (e.g., objectively assessed social media usage behaviour by smartphone apps; e.g., Ellis et al., 2019) might be a good approach for future research.

Past research has found evidence that it makes a difference whether one uses social media actively (e.g., chatting, sharing photos) or passively (e.g., browsing, reposting messages, looking at content; Escobar-Viera et al., 2018; Thorisdottir et al., 2019). In the present study, some support for this assumption was found, but differences were of very low effect size (see correlation differences in Table 1, sixth column). Girls were significantly more likely to be actively using social media compared to boys, whereas boys were significantly more likely to be passively using social media compared to girls. Furthermore, active social media users had slightly lower verbal intelligence, whereas passive social media users had slightly higher scores, which is rather counterintuitive. If adolescents’ active use of social media by writing texts and so forth is associated with positive aspects, then we should not expect a negative association with verbal intelligence. Further, although past research found that social media use reduces working memory short-term (e.g., Aharony & Zion, 2019), it does not seem to have long-term effects because the association between active social media use and the long-term memory was, although in the expected direction, of tiny effect size (−0.021; see Table 1) and of marginal importance (third least important predictor in the RF model; see Fig. 1).

Although we cannot draw conclusions about the causal directions of the found effects, in general, the effects themselves were of very weak effect sizes and, compared with each other, the importance of effects mostly did not outperform known demographic differences, such as sex- or age-differences in social media usage (e.g., Coyne et al., 2020). For example, when it comes to the time social media is used actively per day, the association with fluid intelligence (figural) had only about a quarter of importance compared with the sex-difference between boys and girls (girls using social media more than boys). Or put differently, being a boy or a girl is by far more impactful on differences in active social media use than the effect found for figural fluid intelligence.

Furthermore, we found no evidence of any substantial association with adolescents’ intelligence, spatial perception, information processing, technical understanding, creativity, spelling skills, and vocabulary. The only exception was practical numeracy, where we at least found effects similar in effect size to demographics, such as sex-differences (see Fig. 1). Adolescents with higher social media use or higher PSMU scores had lower practical numeracy ability and vice versa. Because of the cross-sectional design, it remains unclear whether adolescents with lower practical numeracy skills more actively search for social media communication or the other (more alarming) way round; that is, more social media usage leads to reduced numeracy abilities, i.e., reduced ability to solve simple text-based mathematical problems (e.g., calculation of areas). Although we also found an association between social media passive use and information processing outperforming demographics, the overall explained variance was very low (1.6%); therefore, we did not interpret this result in detail.

However, the results are interesting from another point-of-view, namely the impact of social media use on cognitions in general, such as the ability to concentrate, hold attention, keep information in memory, and executive functioning (for a review, see Wilmer et al., 2017). Previous research has found evidence that even short-term interaction with smartphones can impact ongoing cognitions by impairing the ability to concentrate or distort attention (Wilmer et al., 2017). For example, one oft-described aspect of smartphone usage in everyday life is multitasking (Judd, 2014), which can have negative effects, such as delayed completion of primary tasks (e.g., Leiva, Böhmer, Gehring, & Krüger, 2012, September) but also positive ones, such as better task-switching abilities (Alzahabi & Becker, 2013) or better multisensory integration (Lui & Wong, 2012). Furthermore, past research has found that even the mere presence of a smartphone can reduce cognitive capacity, resulting in lower scores on intelligence tests (e.g., working memory, fluid intelligence; Ward et al., 2017) or reduced task performance, especially for tasks with high cognitive demands (Thornton et al., 2014).5 Similar studies exist about children doing a school test (Beland & Murphy, 2016; Levine et al., 2007).

Looking at the pattern of effects in Table 1, the directions of effects largely correspond to these earlier results. Negative significant associations were predominantly found on tests with high cognitive demand (intelligence test, spatial perception test, technical understanding, practical numeracy, spelling skills [although less spelling errors for highly active social media users]), which were all speeded tests with time restrictions. In contrast, significant positive associations were found on the speeded test with low cognitive demand, namely the information processing test (i.e., more correct answers, fewer errors), which uses simple reaction time tasks. Therefore, the effect pattern could also mean that adolescents do not have lower abilities on the tested concepts (e.g., intelligence, spatial perception), but instead have difficulties with the test procedures themselves because they needed to concentrate and focus their cognitions on a specific task under time constraints. This would also explain why these adolescents performed better in the information perception task. Here, multitasking is beneficial: coordinating information from different senses (seeing, hearing) to perform different hand/foot coordination tasks by pressing buttons. Although this might be a possible reason why we found detrimental effects on low vs. high cognitive demand tests, we do not have direct evidence for that based on the current data, though this would be a fruitful approach for future research.

The present study has also limitations. First, some of the measures (2.2.6 to 2.2.10) were developed and validated in-house at VIC and are not published in any peer-reviewed journal. Nevertheless, all measures were developed over several years under the premisses of being valid, reliable, practical, easy-to-administer, and short. Most of them have a clear face validity (e.g., technical understanding, practical numeracy) and a clear and objective test score calculation (e.g., sum of correct answers). Tests are frequently re-standardised as suggested by the DIN 33430 norm.6 Furthermore, in the present study we assessed the time of social media usage subjectively. Because past research found that subjectively assessed usage time does not necessarily correspond to objectively assessed usage time (e.g., through software-based accurate time assessments; Ellis et al., 2019; Sewall et al., 2020), future research should try to replicate the present findings by using an objective measure of social media usage. Because past research found lower associations between objective social media use with, for example, well-being (Sewall et al., 2020), it could be that when focusing on the present results, the uncovered low effect sizes could drop. Another limitation comes from the conceptual distinguishing between active and passive social media use, which has frequently been questioned (for a thoughtful discussion, see Meier & Krause, 2022, March 22), as well as the rather unspecific focus of the measure without explicitly differentiating between the broad range of possible behaviours from texting to watching videos. Because all measures were self-assessed by adolescents, we also cannot rule out a possible shared method-specific variance of the used psychometric tests with social media use. Using objective measures of social media use in future research would resolve that issue.

In conclusion, we did not find any substantial negative associations between social media use and the tested concepts. The effects found did not substantially outperform other known effects, such as sex- or age-differences (except a slightly higher value for practical numeracy on PSMU) if at all. To conclude, although past research found negative effects of social media use in early adolescents (<11 years of age; e.g., Charmaraman, Lynch, Richer, & Grossman, 2021) or children (e.g., 4 and 8 years; Skalická et al., 2019; for a review, see Wiederhold, 2019), cognitive abilities and skills of adolescents between 12 and 16 years of age do not seem to be overly affected by social media use.