digital media and experiment

Digital Media and Experiment

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A Cultural Evolution Approach to Digital Media

Alberto acerbi.

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Edited by: Giosué Baggio, Norwegian University of Science and Technology, Norway

Reviewed by: Monica Tamariz, University of Edinburgh, UK; Massimo Lumaca, International School for Advanced Studies, Italy

*Correspondence: Alberto Acerbi [email protected]

Received 2016 Sep 23; Accepted 2016 Nov 29; Collection date 2016.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Digital media have today an enormous diffusion, and their influence on the behavior of a vast part of the human population can hardly be underestimated. In this review I propose that cultural evolution theory, including both a sophisticated view of human behavior and a methodological attitude to modeling and quantitative analysis, provides a useful framework to study the effects and the developments of media in the digital age. I will first give a general presentation of the cultural evolution framework, and I will then introduce this more specific research program with two illustrative topics. The first topic concerns how cultural transmission biases, that is, simple heuristics such as “copy prestigious individuals” or “copy the majority,” operate in the novel context of digital media. The existence of transmission biases is generally justified with their adaptivity in small-scale societies. How do they operate in an environment where, for example, prestigious individuals possess not-relevant skills, or popularity is explicitly quantified and advertised? The second aspect relates to fidelity of cultural transmission. Digitally-mediated interactions support cheap and immediate high-fidelity transmission, in opposition, for example, to oral traditions. How does this change the content that is more likely to spread? Overall, I suggest the usefulness of a “long view” to our contemporary digital environment, contextualized in cognitive science and cultural evolution theory, and I discuss how this perspective could help us to understand what is genuinely new and what is not.

Keywords: cultural evolution, cultural transmission, transmission biases, cultural attraction, digital media, social media

1. Introduction

Digital media are media encoded in digital format, typically to be transmitted and consumed on electronic devices, such as computers and smartphones. Digital media of wide diffusion includes emails, digital audio and video recordings, ebooks, blogs, instant messaging, and more recently social media. Although, digital media started to be developed with the creation of digital computers in the 1940s, their wide cultural impact can be traced back only to two or three decades, with the widespread diffusion of personal computers and especially the internet (Briggs and Burke, 2009 ).

Social media and ubiquitous connectivity (e.g., allowed by portable digital devices) are even more recent developments. Facebook, in its early stage limited to university or high-school students and employees of a handful of companies, was open to the public 10 years ago, in September 2006 (Boyd and Ellison, 2007 ). The first version of the iPhone, which gave the initial momentum to the worldwide diffusion of smartphones, was launched shortly after, at the beginning of 2007 (West and Mace, 2010 ).

Despite that, digital media, and social media in particular, have today an enormous reach. Facebook for example counts, as of June 2016, more than 1.7 billion monthly active users 1 . The influence of digital media on the behavior of a vast part of the human population is unanimously recognized. As a consequence, academic interest for digital media has grown rapidly in different disciplines. Here, I will not attempt a review of the existing literature, but I will propose that a specific scientific field, cultural evolution, could provide a suitable framework to analyse how the massive diffusion of digital media influences human cultural behavior.

The article is structured as follows. In the next section I will provide a brief and general introduction to the field of cultural evolution, focusing on the aspects I consider more relevant for the study of contemporary digital media. These aspects are cultural evolution's naturalistic and quantitative approach and its commitment to develop hypotheses informed by cognitive science and evolutionary theory. I will then explore more in depth two areas of research where cultural evolution could give an original contribution. First, I will discuss how cultural transmission biases, i.e., simple rules such as “copy the majority” or “copy prestigious individuals,” a central topic in cultural evolutionary research, might influence cultural transmission in the digital age, and conversely how digitally-supported cultural transmission might disrupt these biases. I will explore at some length two of these biases, related to prestige and popularity. Second, I will examine how cultural evolutionary dynamics could be influenced by the fact that digitally-supported cultural transmission allows virtually error-free propagation of cultural traits. I will conclude suggesting that the cultural evolution framework places the digital age in a broader context, and I will discuss how this theoretical and historical “long view” could help us to better understand the changes we are confronted with in our society.

2. Cultural evolution

Cultural evolution is a relatively recent scientific field that studies human and, partly, non-human cultural behavior (see Mesoudi, 2015 , for a recent review). Cultural behavior is generally defined as behavior transmitted through social learning, as opposed to individual learning or genetic inheritance (Henrich and McElreath, 2003 ). The distinction between cultural and non-cultural behavior is not a sharp one (Morin, 2015 ) but it works quite well for practical purposes. Cultural evolutionists study things such as the evolution of uniquely human forms of cooperation (Boyd and Richerson, 2009 ; Turchin et al., 2013 ), indigenous knowledge of plants' properties (Reyes-Garcia et al., 2008 ), the cultural evolution of language (Tamariz et al., 2014 ; Kirby et al., 2015 ), the spread of fashions in contemporary culture, using cases like baby names (Bentley et al., 2004 ) or dog breeds (Ghirlanda et al., 2013 , 2014 ), or how ineffective medical treatments can nonetheless be successful (Tanaka et al., 2009 ; de Barra et al., 2014 ; Miton et al., 2015 ), just to give a few examples. Similarly, a wide range of methodologies are used, including simulation and mathematical models (Acerbi et al., 2009 ; Kempe et al., 2014 ; Smaldino and McElreath, 2016 ), laboratory experiments (Caldwell and Smith, 2012 ; Derex and Boyd, 2015 ; Muthukrishna et al., 2015 ; Schillinger et al., 2016 ), phylogenetic analysis (Fortunato and Jordan, 2010 ; Tehrani, 2013 ; Watts et al., 2015 ), ethnographic research (Mathew and Boyd, 2014 ; Colleran and Mace, 2015 ), and comparative studies of social learning in humans and other animals (Whiten et al., 2009 ; Dean et al., 2012 ; Reindl et al., 2016 ).

What brings together all these researches is, more than a unitary view about how culture should be considered an evolutionary process (see Claidière et al., 2014 ; Acerbi and Mesoudi, 2015 ; Lewens, 2015 , for a general discussion), a strong commitment to provide explanations that are naturalistic and quantitative, as well as grounded in cognitive science and evolutionary theory. At the minimum, all cultural evolutionists share the idea that a cultural phenomenon is a population-level aggregate of individual-level interactions and that, to explain the former, one needs to take seriously the latter. Accordingly, the works of Cavalli-Sforza and Feldman ( 1981 ) and Boyd and Richerson ( 1985 ) are considered as establishing modern cultural evolution. These works consisted in mathematical models, inspired by population genetics, developing formalisms to link micro-processes of transmission—like different “directions” of transmission, e.g., from parents to offsprings, between peers, etc. or different transmission biases, see below—to macro-processes of cultural change—like the diffusion dynamics of cultural traits. In parallel, cognitive anthropologists such as Sperber ( 1985 , 1996 ) started to consider in depth the role of individual cognition in the explanation of cultural patterns, focusing on the fact that the success of some widespread beliefs may depend on them being generally attractive to human minds (I will discuss some examples in the next sections).

The psychology of digital media, in particular online activities (sometimes described as “cyberpsychology” Attrill, 2015 ) is a growing field (see e.g., Wallace, 2001 ; Suler, 2015 ). A cultural evolution approach adds, as mentioned, an explicit interest for the micro-macro link, in other words, for how individual-level properties (e.g., psychological) influence population-level dynamics and vice versa. In addition, the naturalistic and quantitative framework provided by cultural evolution seems perfectly suited for the study of contemporary digital media. One of the opportunities that the widespread diffusion of digital media offers to social sciences is the availability of vast amounts of data on human behavior (Lazer et al., 2009 ). While the understanding offered by ethnographic (e.g., Boyd, 2014 ) or critical-theory-inspired (e.g., Fuchs, 2014 ) perspectives remain clearly important, the cultural evolution approach is in a better position to make sense also of the quantitative data that digital media usage quasi-automatically produces. On the other side, computer scientists and physicists had promptly made use of these data to study the diffusion of information in digital social networks (see Weng et al., 2012 ; Adamic et al., 2014 ; Cooney et al., 2016 ; Del Vicario et al., 2016 , for few recent examples). These works importantly include quantitative analysis and models, and they can offer valuable insights on online activity. However, the perspective of cultural evolution can complement this thread of research by providing a refined view of the micro-processes of transmission and of the psychological motivations underpinning them.

To sum up, cultural evolution may offer a privileged perspective to look at digital media, including both a sophisticated view of human behavior and a methodological attitude to modeling and quantitative analysis. In the next sections I will try to substantiate this claim with some examples of investigations that a cultural evolution approach suggests.

3. Transmission biases in the digital age

For the majority of cultural evolutionists the widespread utilization of social learning is the reason of the ecological success of the human species (Henrich, 2016 ). Social learning provides a shortcut to long and potentially dangerous individual learning and a fast and flexible alternative to genetic evolution. However, simply copying from others can be risky: to be effective, social learning needs to be selective (Laland, 2004 ). According to this view, social learning is made possible by domain-general heuristics—often referred to as “transmission biases” or “social-learning strategies”—helping us to choose what, when, and from whom to learn (Boyd and Richerson, 1985 ). To use a mundane example, imagine you find yourself in a new and unknown town, searching a restaurant for dinner. You may first decide that is worth to look to what others do, instead of trying to figure it out by yourself (“copy when asocial learning is costly”), and then that it does not make much sense to follow the first person you see in the street, but look for restaurants that seem full of customers (“copy the majority”). After few days, you might have found your favorite place, and you can stop to check where other people go (“copy when uncertain”).

Transmission biases are a good place to start as much research has been developed in cultural evolution on this topic. Theoretical models and simulations have explored the adaptive value of different biases, and predictions from the models have been tested in empirical settings (see Rendell et al., 2011 , for a review). In parallel, various works have attempted to detect the presence of transmission biases in real-life cultural dynamics (e.g., Reyes-Garcia et al., 2008 ; Henrich and Broesch, 2011 ; Kandler and Shennan, 2013 ; Acerbi and Bentley, 2014 ). Importantly, for our focus on digital media, transmission biases are considered a suite of psychological adaptations shaped by natural selection (Henrich, 2016 ), hence generally effective in the social and physical environment of small-scale societies. A question only partially explored in cultural evolution is how these biases scale in contemporary, complex, societies, and especially in the novel digital environment.

3.1. Prestige

Various heuristics are available when choosing from whom to copy. From an evolutionary point of view, for example, kin share a common genetic interest, so they will be willing to circulate useful information. Copying from parents and from other close members of the family makes thus perfect sense. Elders, especially in small-scale and slow-changing societies, have two important qualities. First, they had time to learn themselves a substantial part of the cultural repertoire of the society, and, second, they must have done it effectively, exactly because they arrived to old age. Age-biased social learning is thus another evolutionary expected strategy (Henrich, 2016 ).

However, for specialized expertises (i.e., only few people possess them), or for expertises that exhibit variability in a population (i.e., some people are very good at them and others are not), kin- and age- based strategies are not particularly effective. In these cases, an alternative is to try to assess directly the ability of others. Copying skilled or successful individuals is then another of the heuristics suggested by cultural evolutionists (see e.g., Mesoudi, 2011 , for an experimental approach). This strategy presents, in turn, another problem. Skills can be opaque, difficult to recognize, and this is especially true when one does not possess the expertise in question, which is exactly the case when there is the need to learn it. Similarly, success can be volatile, or due to luck. How many successful hunts an apprentice hunter should assess before deciding to copy from a particular individual and not from another?

A possible solution is prestige-biased social learning. Cultural evolutionist Joe Henrich defines prestige cues as a “second-order cultural learning” (Henrich, 2016 , p. 45): one can make use of signs of deference, respect, or simply check from whom other people are learning, and choose those individuals as cultural models. The risk, with prestige-biased social learning, is that prestige and skills may not correlate. What if an individual is prestigious because of his hunting abilities, but I am attempting to learn how to build harpoons? What if an individual is prestigious because he belongs to an influential family, but he does not possess any particular skill? The answer is that in small-scale societies this is a minor problem. Specialization and inequality are limited, so that respected individuals will indeed be, on average, generally skilled.

Of course, the situation is different today. Our reliance on celebrities, for example in advertisement, is generally considered a good candidate for a cultural evolutionary mismatch (Henrich, 2016 ). The acting abilities of George Clooney are unlikely to correlate with his expertise in coffee-tasting, still, the story goes, the success of a Nestlé brand of coffee depends on the presence of the actor in the advertisements. Internet and in particular social media would possibly push things even further, because the rapidity of communications and of the extension and the number of the virtual communities. The real risk for the society is not much that we end up to parrot the—alleged—favorite coffee brand of celebrities, but that social media users will attempt to copy skills that are not existent at all (such as Clooney's coffee tasting ability) or existent, but not relevant in the local environment (such as Clooney's acting ability). More worryingly, extremist groups could make use, consciously or not, of prestige-biased influence mechanisms for on-line proselytism (Barkow et al., 2012 ) 2 . These ideas could be tested empirically, but, to my knowledge, not much research has been done yet. One could examine whether usage of internet and social media correlates with higher preferential attention to “global” cues of prestige (as opposed to “local” ones), possibly taking into account confounding factors such as the exposition to traditional mass-media, like television or cinema. In addition, attention to global cues of prestige does not need to be harmful, especially in a fast-changing and deeply interconnected society. Although, it might be argued that acting abilities are not necessarily relevant, the same digital media allow to access also to prestigious surgeons, programmers, or philanthropists in a way that would not be possible in a local environment.

Research on social media “influencers” is in its infancy, and results are not conclusive (see Bakshy et al., 2011 ; Aral and Walker, 2012 , and the studies reviewed therein). Bakshy et al. ( 2011 ), for example, measured how links to webpages posted in Twitter spread in the social media itself, and found that, indeed, users with more followers and who have been already influential in the past tended to produce larger “cascades.” However, it is not clear how to distinguish the fact that the number of followers is a sign of prestige, in the cultural evolution meaning, from the fact that, at the same time, it indicates how many individuals are exposed to the link. In this sense, the effect could be simply due to a larger number of possible events of transmission. Even not considering this confounding, Bakshy et al. ( 2011 ) comment that, given that cascades-sizes are power-law distributed (i.e., there are very few large cascades, while the majority of links are never reposted), “individual-level predictions of influence nevertheless remain relatively unreliable.” They thus proceed to analyse the contribute of the actual content of the links tweeted, showing that content independently rated as more interesting and positive generated larger cascades. These findings resonate with theoretical results showing that wide-ranging events of diffusion of traits in networks are favored less by influencers than by the presence of large masses of easily influenceable individuals (Watts and Dodds, 2007 ).

The same celebrity influence is, at least in cultural evolution literature, mainly anecdotal, and marketing studies show that the effect of celebrities in advertisements is mediated by various cues, such as their relationship with the product advertised (see e.g., Kelting and Rice, 2013 ). We do not know, for every George Clooney, how many advertisements with celebrities did not succeed (Stephen Hawking, for example, was featured in the early 2000s in a high-profile campaign for an online fund platform that closed in 2004), and how many campaigns succeed without the presence of a celebrity. Moreover, as the results from Bakshy et al. ( 2011 ) suggest, there is an interaction between content and prestige. An interesting possibility is that relatively low-cost alternatives, like which coffee brand to choose or which haircut, could be celebrity-biased, but the effect would be less important for high-cost choices. This would mean that prestige-biased epidemics of extremism might not be such a realistic danger. On the other side, Clooney would not be probably able to persuade smokers to quit, for example.

In sum, although we have some convincing evidences of the effect of prestige-biased social learning in small-scale societies (Henrich and Broesch, 2011 ) and from laboratory experiments (Atkisson et al., 2012 ; Chudek et al., 2012 ), the question of how automatic is the influence of digital media's “influencers” in contemporary society remains open. Morin ( 2015 ) writes of “flexible imitators” that selectively use social—such as prestige—or asocial cues, depending on various factors, e.g., the above mentioned cost of the alternatives. Others (Heyes, 2016b ) suggest that, at least in some circumstances, human social learning strategies are explicitly metacognitive. This means that these strategies include adjustable learning targets, changing from situation to situation, such as “copy digital natives,” referring to copying knowledgeable young persons in the specific domain of technology, instead of a general rule “copy young individuals” (Heyes, 2016a ).

In this case, like in the others we will explore in the next sections, the cultural evolutionary approach suggests a perspective from which to look to digital media and a series of questions that might be addressed in further research. What is the difference between the usage of prestige cues in small-scale societies and in our contemporary digital environment? What are the differences between local prestige, as in the case of small-scale societies or in contemporary circles of friends, and global prestige, as in the case of celebrities? Is prestige modulated by content? We already mentioned a possible difference between high-cost and low-cost choices; another could be related to the presence or absence of previous knowledge: real coffee connoisseurs might be less impressed by Clooney's approval.

3.2. Popularity

A similar way of reasoning can be applied to frequency-dependent biases. In the idiom of cultural evolution, frequency-dependent biases are heuristics that make use of the estimated frequency of a cultural trait to help deciding whether to copy it or not. The usefulness of positive, i.e., preferences for popular traits, frequency-dependent biases is easy to understand. When in a new environment, or when confronted with a new technology, it makes sense to take advantage of the cumulative experience of other individuals.

When cultural evolutionists talk about positive frequency-dependent biases, they generally refers to “conformity” in a precise and quite restrictive sense, meaning a disproportionate tendency to copy from the majority (Boyd and Richerson, 1985 ). This means that, returning to our restaurants example, if 60 people are eating in restaurant A and 40 people in restaurant B, the probability to choose A should be higher than 60% in conformist-biased social learning. In fact, it has been noted that, in almost all cases, social learning imply to “follow the majority” in a loose sense (Boyd and Richerson, 1985 ). In the above case, for example, one individual would still be more likely to go to restaurant A without any particular bias, i.e., copying randomly (imagine to ask to a random person where she was for dinner and follow her advice: your probability to go to restaurant A will be 60%).

This over-response to frequency information (Efferson et al., 2008 ) has a special importance for cultural evolution. First, it has been shown to contribute to maintain culturally homogenous groups, despite certain levels of migrations and individual variations (see e.g., Boyd and Richerson, 2009 ). Second, it allows to directly “jump” to the best alternative in presence of noisy information (Henrich, 2016 ). In what follows I will thus use the more generic term “popularity bias” to indicate that the perception of something as popular makes it preferable to other—less popular—cultural traits, and I will reserve the usage of the term “conformity” for the technical sense described above. Finally, “social influence” simply means that people copy, without any bias, the choices of others.

As in the case of prestige, it is important to draw an explicit comparison between the conditions in which a psychological bias implementing a preference for popular cultural traits could have evolved and today's digital age. The first interesting aspect is that, in a small-scale and perhaps illiterate society, popularity needs to be estimated from various cues. The situation with digital media appears clearly different. Popularity is quantified and explicitly made public—the number of Facebook “likes” or “share,” the number of Twitter “retweets,” etc.—in practically all digital platforms. While one could speculate whether the success of this practice might be due to a universal sensitivity to this kind of information, as a cultural evolution perspective would suggest, it is not clear what kind of effect this could have on cultural transmission patterns. One possibility is that such low-cost availability of popularity signals would discourage individual exploration, prompting people to follow cheap social cues (Derex and Boyd, 2015 ), with digital media amplifying the effect of popularity-biased cultural transmission.

For example, success in digital media, especially regarding internet websites, has been repeatedly described as following a power-law distribution (as mentioned in the previous section for the links posted on Twitter). Power-law distributions are typical of winner-take-all markets, with very few websites monopolizing visitors whereas the vast majority remains relatively unsuccessful (Adamic and Huberman, 2000 ). However, it is useful to remind that power-law distributions are not necessarily generated by popularity-biased dynamics, as defined above. Power-law distributions naturally arise with unbiased social influence, because simply copying at random amplifies small initial differences. In fact, cultural evolutionary studies have shown that power-law distributions are present in many domains where social influence is important, such as baby names, dog breeds, scientific citations (Bentley et al., 2004 ), or even decoration styles in neolithic pottery (Neimann, 1995 ), where one can safely exclude the influence of digital media. The tell-tale of a positive-frequency-dependent bias is a distribution that is even more skewed in favor of successful items than power-laws (Mesoudi and Lycett, 2009 ).

In addition it is difficult, when not impossible without additional data, to set apart the effect of social influence and the effect of the intrinsic quality of the items in creating these skewed distributions (Aral and Walker, 2012 ; Muchnik et al., 2013 ; Morin, 2015 ). Ghirlanda et al. ( 2013 ), trying to deal with this problem, examined the case of dog breeds popularity. They showed that desired characteristics of breeds, such as trainability or good health, were not correlated with their success. This suggests that, in this specific domain, the role of popularity, or simply social influence, is more important than the intrinsic characteristics of the cultural traits, i.e., the dog breeds themselves.

Some studies manipulated directly the perceived popularity of items in digital media, trying to detect the effect on their subsequent success. In a recent experiment, Muchnik et al. ( 2013 ) assigned randomly more than 100,000 comments submitted to a website with a structure similar to Reddit to three treatment groups: up-treated (comments were artificially given a +1 rating at their creation), down-treated (comments were artificially given a −1 rating at their creation), and control. Up-treated comments were indeed more likely to be subsequently up-voted than control. Down-treated comments were, as expected, more likely to be subsequently down-voted than comments in the control group. However, they were up-voted to a greater extent, so that the net effect was slightly positive, even if not significant with respect to the control group, as if users of the website tended to counterbalance negative comments. Muchnik et al. ( 2013 ) explain their results as due to an increasing turnout (i.e., up- or down-treated comments generated overall more ratings than comments in the control group) coupled with a common preference for positive ratings.

In a previous large-scale experiment, Salganik et al. ( 2006 ) created a digital “artificial market” where subjects could listen to and download unknown songs. Participants in the social influence condition could see how many times a song was downloaded previously, and they were randomly assigned to one of eight “worlds” where the counts of download were evolving independently. Salganik et al. ( 2006 ) showed that the social influence condition created more inequality (defined as difference between successful and unsuccessful songs) and unpredictability (defined as the difference between songs' results in the different worlds) with respect to the independent condition, where participants did not have information on previous download. Interestingly, two forms of visual presentation were proposed to participants in the social influence condition: in the first, the songs were presented in the same configuration of the independent condition, simply adding the number of previous downloads, and in the second they were presented as an ordered list, with the most downloaded on the top. Social influence was noticeably stronger in the latter case (more on this below).

Unpredictability, however, was not complete: there was a significant correlation between the perceived quality of the songs, as measured in the independent condition, and their success in the social influence condition or as Salganik et al. ( 2006 ) put it: “in general, the “best” songs never do very badly, and the “worst” songs never do extremely well.” Given that choices (downloading or not a song) were extremely low-cost for the participants and the fact that the songs were previously unknown, the effect of popularity seems relatively limited in this experiment (Lewens, 2015 ; Morin, 2015 , argument more thoroughly for a similar interpretation of these results). In a follow up study, the manipulations were stronger, such as completely reversing the perceived popularity order of the songs, i.e., presenting as the most popular the “worst” song of the independent condition, and so on (Salganik and Watts, 2008 ). Again, however, the best songs tended to recover their popularity in the long run. Moreover, strong distortions of the correlation between intrinsic appeal and popularity were intuitively perceived by the participants, as showed by the fact that they resulted in fewer downloads overall. As above, the effect of popularity seems to be more nuanced that what an intuitive, clear-cut, understanding would suggest.

A more extreme version of the explicit advertisement of popularity cues is the proliferation of “top-N” lists. The spreading of top-lists predates digital media, and it is almost an hallmark of the broadcast era (in the United Kingdom the first introduction of a top-chat program in BBC radio dates back to 1957 3 ), but it reached enormous diffusion in the recent years, with on-line top-lists of virtually everything. From a cultural evolution perspective, top-lists are not only sources of cheap estimates of popularity, but they also supply a direct way to implement a variant of the above mentioned conformist-bias, giving disproportionate publicity to already popular items (Acerbi and Bentley, 2014 ). The presentation of alternatives in form of top-lists, or ranked tables, do seem to enhance popularity influence (Salganik et al., 2006 ).

Another, more elaborate, variant of popularity displays is represented by the spreading of information in form of consumer—as opposed to “expert”—reviews, whether as a part of commercial websites (such as Amazon), or through websites specifically dedicated to reviews (such as Tripadvisor, Yelp, etc.). The positive economic effect of favorable reviews has been shown in several domains, including books (Chevalier and Mayzlin, 2006 ), restaurants (Luca, 2011 ), or hotels (Ye et al., 2009 ). The where-to-go-to-dinner example I used to illustrate cultural transmission biases looks rather outdated nowadays, when people can glance at their smartphones and obtain cheap, real-time, information on all restaurants in their surroundings. Finally, a large number of websites and, in particular, almost all social media and commercial websites, provide direct personalized recommendations, e.g., “inspired by your browser history" in Amazon, “who to follow” in Twitter, etc.

Consumer reviews and recommendation systems have complex effects on users' preferences (Duan et al., 2008 ; Fleder and Hosanagar, 2009 ) that is not possible to explore in this article. Moreover, the contemporary trend might even be to replace these explicit systems with more subtle presentation cues, embedded in the layout of the user interface, or simply deciding the informations that are presented and the informations that are not, as in Facebook News Feed (Vanderbilt, 2016 ). These recent and less recent (such as top-lists diffusion) developments are stimulating material for future cultural evolutionary studies, and looking at them through the perspective of cultural transmission biases seems a promising direction.

In conclusion, the details of how popularity influences the spreading of cultural traits need further investigation. The quantitative data resulting from digital media usage may be of great significance for this endeavor. At the same time, new ways to signal and perceive popularity in the digital environment represent an important new area of research for cultural evolutionary studies.

4. Preservative and reconstructive cultural transmission

How faithful is cultural transmission? While, in the popular image, cultural “evolution” implies that ideas and behaviors spread by replicating gene-like from individual to individual, practitioners tend to be more cautious about the analogy genes-cultural traits, in particular regarding fidelity of transmission. The term “meme,” invented by Richard Dawkins, is dismissed by the majority of cultural evolutionists, even though sometimes used in social-media literature (e.g., Weng et al., 2012 ; Adamic et al., 2014 ).

The oral transmission of stories provides a case in point. Transmission chain experiments, where individuals are asked to iteratively listen to and repeat short narratives (starting from Bartlett, 1932 ), have shown that, because of memory and attention limits, or biases from previous knowledge, the original material is quickly disrupted (more on transmission chain experiments below). In fact, what is surprising is on the contrary how some orally transmitted folktales have remained relatively stable through centuries or even millennia (Graça da Silva and Tehrani, 2016 ).

There are various options to explain cultural macro-stability. Some (see e.g., Sperber, 1996 ; Sperber and Hirschfeld, 2004 ; Morin, 2015 ) prefer to concentrate on universal, or slow-changing, factors of attraction that make some cultural traits, or some features of them, particularly memorable, or more likely to be reproduced individually. The stability of a long, oral, transmission chain of a story—say Cinderella—does not depend on a series of faithful acts of copying, but on the fact that some features of the story are particularly likely to be remembered and reconstructed in successive retellings (the example of Cinderella is used in Acerbi and Mesoudi, 2015 ). The Pumpkin Coach might be one cultural attractor, as an example of a minimally counterintuitive concept (a concept that mainly fits our intuitive cognitive expectations but with few exceptions; for an analysis of the success of folktales due to the presence of minimally counterintuitive concepts see Norenzayan et al., 2006 ); another might be the relationship between Cinderella and the wicked stepmother (stepparents are considered a serious threat for stepchildren from the point of view of kin selection theory, see Daly and Wilson, 1999 ).

Others links instead macro-stability to precision of transmission at individual level (micro-stability). Some focus on the fact that, compared to other species that make nevertheless use of social learning, such as other great apes, humans are faithful copiers (Tennie et al., 2009 ; Dean et al., 2012 ). Another possibility is that the above mentioned transmission biases provide a way to repeatedly encounter the same behavior, supplying redundancy to the process of cultural transmission (Boyd and Richerson, 1985 ). Finally, another option yet is provided by epistemic technologies (Sterelny, 2006 ), i.e., modifications of the external environment that improve individuals' cognitive abilities, in this case specifically related to facilitate transmission, including extensive apprenticeship or practice.

Acerbi and Mesoudi ( 2015 ) argued that these explanations are not mutually exclusive, and that their importance varies depending, among other things, on the domain being studied. Some cultural domains, such as orally transmitted stories, can be considered mainly based on reconstructive cultural transmission, i.e., they derive their stability from the presence of features that are likely to be reconstructed each time by individuals, no matter how faithful is the process of transmission itself. Other domains, for example complex technologies, are characterized by preservative cultural transmission, implemented through faithful copying and external epistemic technologies. As might be expected, reconstruction and preservation, or attraction and faithful copying, are important, in various degrees, in all cultural domains. Rhymes are epistemic tools that make attractive stories even more transmissible (Rubin, 1995 ); recipes books contain scripts that make universally palatable dishes easier to prepare (Acerbi and Mesoudi, 2015 ).

Digital media can therefore be considered as a technology that makes cultural transmission more preservative. Cinderella does not need to be listened to, remembered, and retold, but can be “shared” in social media, and practically replicated with extremely low mutation rate. In this sense, the usage of the term “meme” for content that spreads in digital media could be possibly reconciled with its meaning in cultural evolution. An interesting question, from a cultural evolution perspective, is whether the degree of fidelity of transmission influences the kind of content that is more likely to spread.

Cultural evolutionists have investigated content effects experimentally mainly using the above mentioned transmission chain methodology. Transmission chain experiments show that the distortion of the content are consistent, that is, some kinds of content tend to survive along the chains, and others do not. A growing, if somehow unsystematic, catalog of so-called content biases is being built, including among others: a bias for social information (or gossip), involving peoples' relationships and interactions (e.g., Mesoudi et al., 2006 ); a bias for survival-relevant information, such as location of resources or predators (e.g., Stubbersfield et al., 2015 ); a bias for content that elicits emotional reactions, especially related to disgust (e.g., Eriksson and Coultas, 2014 ); a bias for the above mentioned minimally counterintuitive concepts (e.g., Barrett and Nyhof, 2001 ); a negativity bias, where negatively valenced information is preferred to positively valenced one (Bebbington et al., 2017 ); a bias for simplicity in linguistic structure (balanced by informativeness, e.g., Kirby et al., 2015 ), and so on.

However, what if information can be easily reproduced with high-fidelity, as it happens in preservative digital transmission? Promising steps in this direction have recently been made, for example, by experiments from Eriksson and Coultas ( 2014 ) and Stubbersfield et al. ( 2015 ), which considered each passage in the transmission chain as composed by three distinct phases: choose-to-receive, encode-and-retrieve, and choose-to-transmit. The choose-to-receive and the choose-to-transmit phases indicate respectively the willingness to receive and to circulate cultural information. They are comparable to social media “share,” as they do not require the memorization and the repetition of the material, which are required only in the encode-and-retrieve phase. Eriksson and Coultas ( 2014 ) found that the bias favoring disgust-related information was operating in the same way in all phases of the transmission. Stubbersfield et al. ( 2015 ) compared social and survival information biases, and they found that social information bias had an advantage on survival information bias only in the encode-and-retrieve phase (i.e., the “standard” transmission chain methodology), but not in the choose-to-receive and choose-to-transmit.

Berger and Milkman ( 2012 ), with a different approach, examined directly what people share in a 3-month “field study” conducted on New York Times articles. Among other findings, they report that the most shared articles were characterized by a preponderance of positive emotion-valenced terms with respect to negative emotion-valenced ones. This might appear surprising when compared with transmission chain studies that found, on the contrary, that a story with negative content had an advantage in terms of probability to spread and to not be distorted (Bebbington et al., 2017 ). This negative bias, in terms of favoring attention and memorization, has been confirmed in several experiments, and there are evolutionary reasons to think that negative information should be more salient than positive one (Fessler et al., 2014 ). One way to reconcile these findings with the results of Berger and Milkman ( 2012 ) might be indeed to consider that they studied a paradigmatic case of digitally-mediated preservative transmission, whereas the findings supporting the importance of a negative bias come from cases of reconstructive transmission, or simply related to recall. In this particular case, digital media would favor—because memory and reconstruction are less important than, perhaps, self-presentation motifs, and desire to share positive content with familiars and friends—different content with respect to traditional oral transmission. Other features, for example simplicity and repetitiveness, which have been shown important for the maintenance of oral traditions (Rubin, 1995 ), seem to contribute in the same way to the success of digital content (Shifman, 2012 ).

Interestingly, some social media texts, in particular Facebook updates, come with the explicit instruction to “copy-and-paste”—as opposed to share—them. It is not entirely clear why this is the case 4 , but, from the point of view we are discussing, copy-and-paste reintroduce variation in highly preservative digital transmission, allowing for modifications that could make the messages more successful (Acerbi and Mesoudi, 2015 ). Adamic et al. ( 2014 ) estimated a “mutation rate” of μ = 0.11 for Facebook status updates asked to be copy-and-pasted, i.e., 11, every 100 copies, were different from the original, which is extremely high considering the fidelity provided by the digital support.

In fact, some researchers (for example Shifman, 2013 ) have proposed that one of the main features of internet “memes” is to provide templates that individuals use to introduce personal innovations. Whereas, in oral transmission reconstruction is practically unavoidable, in digitally-supported transmission the content is actively modified by individuals. Shifman ( 2013 ) distinguishes two major ways individuals use to modify content: “remix,” involving the digital editing of pre-existent material, and “mimicry,” involving the actual creation of a new content, inspired by the source. A well-known example of remix is the “Hitler Reacts” meme 5 , where fake subtitles, often related to contemporary popular culture topics, are added to a scene of the 2004 movie “Downfall,” where an angry Hitler addresses his strict collaborators in his bunker few days before committing suicide. An example of mimicry is instead “Harlem Shake.” In the first 2 weeks of February 2013 around 40,000 videos, in which groups of people dance on the music of the song “Harlem Shake,” were uploaded to YouTube 6 . The videos are all based on the same concept: they usually start with a single person dancing, surrounded by other people apparently indifferent to the event. Suddenly, the entire group starts to dance, generally with exaggerated and spasmodic-looking movements, often using props and costumes.

More studies are needed to clarify whether there is a specific effect of digital media on the content that is transmitted, but, again, cultural evolution may provide a favorable perspective to investigate this problem. In addition, the distinction preservative/reconstructive is only one of the possible ways to look at the effects of supporting cultural transmission digitally. It has been argued, for example, that universal factors of attraction, or stable content-biases, are especially important with respect to context-based transmission biases (such as popularity and prestige, examined in the previous sections) when cultural transmission chains have two properties. First, they extend through long time-scales, and, second, they are “narrow,” that is, the connections between individuals are sparse (Morin, 2015 ). Digital media seem exactly to be the opposite case, providing fast spreading and high connectivity between individuals (Doer et al., 2012 ). On the other side, successful cultural traits that spread through digital media can reach enormous diffusion—the well known Gangnam style music video has, as of September 2016, more than two and half billions views on YouTube 7 —which may imply they can reach a very diverse audience, possibly by tapping common psychological preferences.

As above, this review of the cultural evolution literature suggests a way to frame possible questions, more than providing answers. Does the fact that digital media support cheap and high fidelity transmission have an influence on the kind of content that is more likely to spread? What is the role of mechanisms that introduce variation in digital transmission? Are universal cognitive biases more, less, or equally important in the digital age?

5. Taking the long view

Overall, very few studies in cultural evolution have dealt with these subjects. As a consequence, this review is only proposing some possible directions, and, mainly, suggesting that cultural evolution can provide a “long view” to the contemporary digital environment. When put into perspective, the new phenomena that characterize our digital age appear to have their roots in deeper psychological and historical dynamics, and, to understand what is genuinely new and what is not, we may need to take seriously these dynamics.

The spread of massive digital misinformation , for example, is considered one of the most worrying contemporary global risks by the World Economic Forum 8 . Models that explicitly address the spread of misinformation in social networks (Acemoglu et al., 2009 ; Del Vicario et al., 2016 ) could greatly benefit of the inclusion of the knowledge developed in cultural evolution. The transmission chain experiments mentioned in the previous section show that certain kinds of information, related for example to gossip or disgust, are more likely to spread than others. How these, and others, predispositions to be influenced in cultural transmission interact with the novel characteristics of digital media (such as high fidelity of transmission, speed, etc.) is material for future studies.

A similar reasoning can be applied to another allegedly worrying phenomenon associated to digital, in particular social, media, that is, the formation of echo chambers. The term “echo chambers” describes the fact that individuals tend, in social media, to associate in communities of like-minded people, and they are thus repeatedly exposed to the same kind of information (e.g., a political ideology) and, especially, they are not exposed to information that could counterbalance it. More concerning, it has been suggested that groups of like-minded people tend to produce opinions that are not an “average” of the opinions of the members of the groups, but their radical version, according to a phenomenon called “group polarization” (Sunstein, 2002 ).

The empirical evidence for the existence of echo chambers in social media is, however, mixed. Studies showing their existence considered explicitly separated communities of individuals (e.g., Facebook users associated to groups coded as “science news” and “conspiracy theories” in Del Vicario et al., 2016 ), whereas other researches gave a more nuanced image. Barberá ( 2014 ), in a study of Twitter accounts from Germany, Spain, and the United States, found that the usage of social media decreases political polarization, arguing that social media contains more weak ties (i.e., acquaintances or occasional contacts as opposed to close friends or family) with respect to offline networks. In another example, Shore et al. ( 2016 ) found that Twitter users post links that are, on average, more moderate than the links they receive in their feed, and that the perception of polarization at global level is due to the activity of a core of few, but more active, extremist users.

As above, a cultural evolution approach suggests to look at polarization, and echo chambers formation, from a broader perspective. Cultural evolutionists have identified, among the cultural transmission biases discussed in the previous sections, one that refers to “self-similarity,” i.e., to the fact that individuals preferentially copy from others similar to them. This has been particularly studied for the arbitrary signals that mark ethnic groups membership. As in the case of prestige bias, or popularity bias, there are reasons to think that a self-similarity bias is an adaptive strategy. The logic is that people of the same group are more likely to live in similar situations, and thus to share the same challenges (Henrich, 2016 ). One may thus wonder whether or not social media are amplifying the effects of the similarity bias with respect to offline interactions. How polarized are groups of offline friends or coworkers? And what about traditional, broadcast, media?

The broad perspective suggested by cultural evolution does not imply, of course, that the recent modifications produced by digital media are not important, or that media are neutral, and they do not influence what is transmitted. On the contrary, the long view proposed here might be necessary to bring out clearly the novelties. An example toward this direction concerns the incredible amount of user-generated content that has been developed and published with the advent of the so-called Web 2.0, such as blogs, videos, or wiki platforms (van Dijck, 2009 ). If the motivations of producing some of this content, for example in the case of blogs or video sharing, are likely to be self-promotional, other collaborative enterprises (e.g., Wikipedia, or the WikiHow platform) are more puzzling from a cultural evolutionary point of view. It is common, in cultural evolution (starting from Rogers, 1988 ), to consider social learners as “information scroungers,” that do not pay the cost—and avoid the risk—of individual trial-and-error, relying on the effort of individual learners (Rogers' model shows that populations composed by only, or a great majority of, social learners can not track environmental variation). However, digital media made obvious that, if they have the possibility, individuals seem to be happy to provide, for free, information to unknown “scroungers.” How, and to what degree, this may provide a return in terms of reputation or within-group advantage is an interesting question for cultural evolutionary studies of digital media.

Finally, digital media interactions involve substantial changes in the form in which information is transmitted. On one side, digital media favored a surge of text-based, as opposed to oral, communication. For example, the majority of day-to-day conversations between US teenagers happen through text messaging. Non-digital, in person, contacts are in fourth position, preceded by instant messaging and interactions through social media websites 9 . Arguably, previous works on the differences between oral cultures and cultures where writing is widely diffused (e.g., Ong, 1982 ) are an intriguing starting point to shed light on this phenomenon. Ong ( 1982 ), for instance, classified (his) contemporary culture as characterized by a “secondary” orality, i.e., the orality promoted by traditional-broadcast media, profoundly influenced by writing and thus different from the primary orality. One could use the term “secondary literacy” to describe the current situation. Secondary literacy provide, as primary literacy, a way to improve micro-stability of transmission, making it highly preservative, as discussed at length in the previous section. However, it also differs from primary literacy in several respects, including, among others, a more widespread utilization, informal tone, and instantaneity of transmission. In parallel, transmission based on digital media is characterized by the facility of including non-written content, such as images and videos. A significant proportion of the content successfully spreading in the digital environment is in fact characterized by a combination of visual and textual features (think, for example, to image-macro“memes” such as LOLcats , or “demotivational” posters 10 ).

6. Conclusion

In the previous sections I highlighted few of the possible investigations that a cultural evolution approach to digital media suggests. One is to look to how traits spread in digital media through the lens of cultural transmission biases. Transmission biases, such as preferentially paying attention to prestigious individuals, or to items that are already popular, are considered adaptations. As such, they are tuned to the conditions of small-scale, slow-changing, and orally-based, societies. How these transmission biases operate in contemporary culture, in which cultural transmission heavily relies on the support of digital media, is an important, and so far unanswered, question. In the same time, I endorsed an elastic view of these biases. Popularity and prestige are not—or, at least, not always—blind forces that push people to copy compulsively. Fears of internet epidemics of extremism, harassment, or similar, driven by influentials or informational cascades, should be considered in a broader context. The quantitative data produced by digital media, together with dedicated experiments, may help us to understand when and how social cues, such as prestige and popularity, interact with the individual evaluation of the content of cultural traits and with other tendencies.

Next, I examined how digital media can be seen as a technology that makes cultural transmission preservative, by providing, practically for free, high fidelity of transmission. This is quite a departure from the conditions usually examined in cultural evolutionary experiments, where items are generally transformed when passing from an individual to another. In addition, digitally-mediated cultural transmission is characterized by other features such as speed, dense connections among individuals, heavy utilization of writing and, in the same time, facility of combining written and audio-visual content. How the interactions of these features influence what kind of content is more likely to spread is another important investigation.

Cultural evolution is a mature field that could give its contribution to the exam of contemporary cultural phenomena. The digitalization of many instances of cultural transmission seems both relevant for our society and suitable for the theoretical and methodological tools that cultural evolutionists have developed. More empirical and modeling works are needed for this task, and possibly the suggestions sketched here may provide some guidance.

Author contributions

AA wrote the article, conceived the work, searched and studied the literature, and elaborated the viewpoint that the article expresses.

AA was supported by The Netherlands Organization for Scientific Research (NWO VIDI-grant 016.144312).

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Giosuè Baggio, editor of the Frontiers Topics “Language Development in the Digital Age,” invited me to submit this article. Two reviewers provided helpful comments on previous versions of the manuscript. Paul Smaldino and Mícheál de Barra also read and commented the draft. Finally, I would like to thank Krist Vaesen and the Philosophy & Ethics group at the Eindhoven University of Technology for giving me the time to pursuit my research interests. I hope I am using it wisely.

1 https://newsroom.fb.com/company-info/

2 See also: http://www.cato-unbound.org/2016/02/08/jerome-h-barkow/how-internet-subverts-cultural-transmission

3 https://en.wikipedia.org/wiki/Pick_of_the_Pops

4 One reason might be that shared malicious messages or hoaxes, if reported as such by users, can be easily traced back to the original, and in case all the thread can be deleted by administrators of the social media. Each copy-and-pasted status, by contrast, is an independent piece of content, and can not be immediately linked to the others.

5 http://knowyourmeme.com/memes/downfall-hitler-reacts

6 http://knowyourmeme.com/memes/harlem-shake

7 https://www.youtube.com/watch?v=9bZkp7q19f0

8 http://reports.weforum.org/global-risks-2013/risk-case-1/digital-wildfires-in-a-hyperconnected-world/

9 http://www.pewinternet.org/2015/08/06/teens-technology-and-friendships/

10 http://knowyourmeme.com/memes/image-macros

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• Published: 06 June 2024

Long-term impact of digital media on brain development in children

• Samson Nivins 1 ,
• Bruno Sauce 2 ,
• Magnus Liebherr 3 ,
• Nicholas Judd 1 &
• Torkel Klingberg 1  

Scientific Reports volume  14 , Article number:  13030 ( 2024 ) Cite this article

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• Brain imaging
• Magnetic resonance imaging
• Paediatric research

Digital media (DM) takes an increasingly large part of children’s time, yet the long-term effect on brain development remains unclear. We investigated how individual effects of DM use (i.e., using social media, playing video games, or watching television/videos) on the development of the cortex (i.e., global cortical surface area), striatum, and cerebellum in children over 4 years, accounting for both socioeconomic status and genetic predisposition. We used a prospective, multicentre, longitudinal cohort of children from the Adolescent Brain and Cognitive Development Study, aged 9.9 years when entering the study, and who were followed for 4 years. Annually, children reported their DM usage through the Youth Screen Time Survey and underwent brain magnetic resonance imaging scans every 2 years. Quadratic-mixed effect modelling was used to investigate the relationship between individual DM usage and brain development. We found that individual DM usage did not alter the development of cortex or striatum volumes. However, high social media usage was associated with a statistically significant change in the developmental trajectory of cerebellum volumes, and the accumulated effect of high-vs-low social media users on cerebellum volumes over 4 years was only β = − 0.03, which was considered insignificant. Nevertheless, the developmental trend for heavy social media users was accelerated at later time points. This calls for further studies and longer follow-ups on the impact of social media on brain development.

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Introduction.

Children are increasingly engaged with digital media (DM) more than ever before. For example, in the U.S., children aged 8–12 years, on average, spend 4 h and 44 min daily on DM for entertainment purposes 1 , in addition to its use during school and homework. This rise in usage has sparked concerns among parents, caregivers, and policymakers regarding its potential adverse effects on the developing brains of children. However, research in this domain remains inconclusive and somewhat contradictory.

Concerning the DM's impact on cognitive outcomes, prior studies have reported both beneficial and detrimental associations 2 , 3 , 4 , 5 . Similarly, a recent review on brain development simply noted that DM's effects can be both positive and negative 6 . This inconsistency in findings can be attributed to several factors. First, the general term ‘digital media’ encompasses a wide range of activities, each potentially influencing development in distinct ways or even exerting contrasting effects. Therefore, it is crucial to differentiate between various digital activities, such as playing video games, watching television/videos, and using social media. Second, the age of the participants is a significant factor. For example, research by Orben et al. in 2022 showed that social media use could negatively affect psychological well-being during particular developmental stages, with these stages occurring at different times for boys and girls 7 . In another study, Soares et al. found that boys who spent more time watching television or playing video games at 11 years old, and more time using computers at 11 and 15 years old, showed improved working memory performance at 22 years old 8 . However, this association was not observed in girls. Third, and perhaps most critical, is the conflation of evidence from cross-sectional and longitudinal studies in reviews. Cross-sectional studies can identify correlations but cannot establish causality. Whereas longitudinal studies may even yield opposite results. For example, a longitudinal study using structural equation modeling has found a negative correlation between time spent playing video games and intelligence 4 . However, when controlling for baseline cognition and other background variables, the longitudinal analysis revealed that playing video games positively influenced changes in intelligence (β = 0.17). The initial negative correlation between video gaming and cognitive performance was interpreted as resulting from self-selection.

Longitudinal research on the effect of DM and brain development in children remains limited. A series of studies on a cohort of Japanese children observed that watching television increased grey matter volume in frontal areas 9 , playing video games increased mean diffusivity in the white matter 10 , and internet usage decreased grey matter volume in extensive brain regions 11 . Although informative, this is a single cohort of less than 300 individuals, which varied widely in age, between ages 6 to 18. Brain development during this period is nonlinear, which was not accounted for in the statistical modeling. In 2023, Miller et al. assessed the impact of DM on functional connectivity over 2 years in a cohort of over 4000 children 12 . They reported no effects exceeding a size of 0.2, the predetermined threshold for significance.

The ongoing debate over what constitutes a meaningful effect size continues without consensus in psychology and neuroscience. This issue is particularly relevant in large-scale studies like the Adolescent Brain Cognitive Development (ABCD) study, where statistical significance may not equate to a meaningful effect for the individual 13 . The traditional criteria by Cohen, which categorizes effect sizes of 0.2 as small and 0.5 as medium, were arbitrary from the outset, with Cohen himself acknowledging the lack of solid evidence for these benchmarks 14 . Funder and Ozer propose that effect sizes must be contextualized, and propose as guidelines that an r-value of 0.05 indicates a very small effect and 0.1 a small effect. The frequency of an event may also be crucial, as repeated events can accumulate effects over time, according to Abelson 15 .

Furthermore, when interpreting effect sizes, it is essential to consider additional factors. Even a small effect can have significant implications if it influences various aspects of an individual's life or interacts positively with other variables. Habituation or counteractive responses might mitigate an effect's impact 16 . In our analysis, we regard an annual effect size of 0.05 as meaningful. This threshold is deemed appropriate, considering the cumulative influence of DM and the potential for an effect on a general ability like attention to significantly impact schooling and everyday life.

Our study aimed to investigate the individual effects of DM usage on structural brain development in children aged 9.9 years at baseline (T 0 ) over 4 years, adjusted for age, sex, SES, scanner sites, and genetic predisposition. We selected global cortical surface area (CSA) as the main outcome measure since previous studies have shown a strong relationship between global CSA and intelligence across different age groups 17 , 18 , 19 , 20 , 21 , 22 . Moreover, we used cortical surface area rather than cortical thickness because studies have consistently reported an association of environmental variables, such as SES, on cortical surface area rather than on cortical thickness 23 .

We further investigated the individual brain structures, i.e., the volumes of the striatum and cerebellum, which have been implicated in cross-sectional studies of DM usage 24 , 25 . In general, global CSA tends to increase during this period of childhood a part of normal development with a peak age at 11 years of age 26 . Building upon our prior research findings 4 , we hypothesized that DM usage, particularly playing video games, would be associated with an increase in global CSA. Since DM usage differs between sexes 27 , we will study the effect of sex on these relationships.

Baseline characteristics

Of the 11,875 children from the ABCD study cohort, 6469 children (age, mean (SD) = 9.9 (0.6) years; boys, n (%) = 3369 (52.1%)) fulfilled our inclusion criteria and were included at the T 0 visit (i.e., 9–11 years of age). Of these, 4610 children (age = 12.0 (0.7) years; boys = 2487 (53.9%)) were included at the T 2 (i.e., 2 years later) visit, and 1697 children (age = 13.4 (0.6) years; boys = 949 (55.9%)) were included at the T 4 (i.e., 4 years later) visit. 1462 children had usable data for all three-time points.

The estimated time spent by these children on DM types at T 0 was 0.5 h/day for using social media, 0.9 h/day for playing video games, and 2.1 h/day for watching television/videos (Table 1 ).

Compared to the T 0 visit, the estimated time spent using DM types significantly increased over 4 years in the overall cohort and in boys and girls (Table 1 ). During the 4 years of the follow-up period (i.e., across all four annual visits), children, on average, spent 1.4 h/day using social media, 1.5 h/day playing video games, and 2.2 h/day watching television/videos. Moreover, during this period, boys spent more time playing video games or watching television/videos, whereas girls spent more time using social media or watching television/videos.

As expected, parents reported less total screen time use per day in children compared to child reports across two visits (Table 1 ).

Normal brain developmental trajectory

Overall development followed an inverted U-shaped developmental trend for global CSA, striatum and cerebellum volumes between mid-childhood and early adolescence, i.e., age-related increase during mid-childhood and subsequently decrease during early adolescence. According to the fitted model, the global CSA, striatum, and cerebellum volumes peaked at 10.6, 10.9, and 15.4 years, respectively (Fig.  1 a).

( a ) Plots representing the quadratic effects of age (years) predicting global cortical surface area, striatum and cerebellum volumes in the overall cohort, adjusted for sex, socioeconomic status, polygenic scores for cognitive performance, and 20 principal components, and the grey shade around the regression lines corresponds to a 95% confidence interval of the intercept; ( b ) sex-stratified developmental trend adjusted for the same covariates as mentioned above. The dots represent the peak age, estimated by the first derivative. The y-axis represents brain structures.

Sex effect on brain development

To determine whether the trajectory differed between the sexes, we added interaction terms (age * sex; age 2 * sex) to the pre-existing model. There was a significant interaction for sex with global CSA and cerebellum volumes, but not for striatum (eTable 2 ).

Overall, boys had larger global CSA and cerebellum volumes than girls (eTable 3 ; Fig.  1 b), but the peak was much earlier in girls (global CSA = 10.4 years and cerebellum = 11.9 years) than in boys (global CSA = 11.1 years). Cerebellum volumes increased over this age range for boys.

SES and cogPGS

Overall, we observed a positive association between SES and global CSA (β = 0.08 to 0.11; p < 0.001) (Table 2 ). For illustration purposes, we additionally categorized SES into quartiles and plotted global CSA development across them (Fig.  2 ). Similarly, we categorized SES into three quartiles and studied the trend for cerebellum volumes (Table 2 ). Overall, children from lower levels of SES had lower global CSA and cerebellum volumes compared with their developing peers and had earlier maturation (Fig.  3 ).

Developmental trajectories and socioeconomic status, ( a ) global cortical surface area, and ( b , c ) cerebellum (presented as adjusted and unadjusted for total brain volumes (TBV)). For visual purposes, we present in age (years). socioeconomic status (SES) is categorized into quartiles using ggpredict [quart2] function in R. 89 Children from low levels of socioeconomic status had a relatively smaller global cortical surface area or cerebellum volumes and accelerated maturation of the brain compared to their developing peers. SES socioeconomic status.

Relationship between digital media usage and cerebellum development over time. The interactions presented in (a,c) social media usage (b,d) playing video games, and time 2 on the cerebellum development; however, they are presented in age (years) for visual purposes. Digital media usage is categorized based on quartiles using ggpredict [quart2] function in R. 89 Children who spent a longer time on social media usage (a,c) had a decrease in cerebellum volume. Similar findings were seen for those who spent on mean levels. In contrast, children who spent a longer time playing video games (b,d) had an increase in cerebellum volume.

No association was found between cogPGS and global CSA and the volumes of cerebellum and striatum.

There was no significant three-way interaction found between DM usage, time, and SES on any brain regions studied (i.e., global CSA, and volumes of striatum and cerebellum).

Interaction of DM usage and time on brain development

There were multiple interactions between the average DM usage and both linear and quadratic effects of time on brain development (Table 2 ). Only the significant interactions that survived Bonferroni corrections (p < 0.003) will be discussed below.

Social media usage

We found a significant interaction between average social media usage and both linear and quadratic effects of time (i.e., average social media usage x time and average social media usage x time 2 , respectively) with cerebellum volume. Here, we observed a positive association of social media usage and time with cerebellum volumes for a linear term (β = 0.02) but a negative association of social media usage and time with cerebellum volumes for a quadratic term (β = − 0.02) (Table 2 ). The consequence of these effects is illustrated in (Fig.  3 a,c); there is a slight difference in trajectory, which results in an earlier decline and lower volume at the last time-point.

There was no significant interaction between social media usage and time with other brain regions studied (i.e., global CSA and striatum volumes) (Table 2 ).

Playing video games

In contrast to social media usage on brain development, we observed a significant positive interaction between average time spent playing video games and a quadratic effect of time (but not linear) with cerebellum volume (β = 0.01) (Table 2 ). This resulted in a trajectory with continued increase throughout the study period and a larger cerebellar volume at the last time-point (Fig.  3 b,d).

There was no significant interaction between playing video games and time in other brain regions studied (i.e., global CSA and striatum volumes) (Table 2 ).

Watching television/videos

There was no significant interaction between watching television and time with any of the brain regions studied (i.e., global CSA, volumes of cerebellum and striatum) (Table 2 ).

There were no significant three-way interactions between any DM usage, time, and sex with the brain structures studied. Therefore, we did not carry out separate analyses for boys and girls (eTable 4 ).

Additional analysis

In investigating whether DM estimates preceded changes in cerebellum volume, we observed a negative trend for high social media users with changes in cerebellum volumes (β = − 0.01; p = 0.10). Conversely, there was a significant positive association for high video game users with changes in cerebellum volumes (β = 0.02; p =  < 0.001) (eTable 5 ).

We then investigated whether social media usage at T 0 could predict total changes in cerebellum volumes (T 4 –T 0 ) and found that social media usage at T 0 was not associated with changes in cerebellum volumes (β = − 0.03; p = 0.16) (eTable 6 ). The estimate (β = − 0.03) thus represents the overall effect size of social media usage over a 4-year study period.

Furthermore, after excluding time spent on video chatting or texting from social media usage, we still found that the direction of the observed effects between average social media usage on cerebellum volumes remained significant, with the same effect size (β = − 0.02; p = 0.002).

Sensitivity analyses

When we excluded children who were born preterm, had low birth weight (< 2500 g), or had ADHD diagnosis, 3979 children fulfilled the criteria for eligibility, and the findings of the average social media usage or playing video games and cerebellum volumes remained significant (eTable 7 ).

Further, our analysis was confined to children with MRI data available across all three visits (n = 1462), and despite this restriction, the observed effects between average social media usage or playing video games and cerebellum volumes remained significant (eTable 8 ).

In this large prospective cohort study, we studied the long-term effects of DM usage on the development of the cortex, striatum, and cerebellum across three time points spanning between mid-childhood to adolescence, in children aged 9 to 11 years. Despite our initial hypothesis, we found that DM usage did not significantly alter the development of the global CSA or striatum volume. However, children who devote more time to playing video games had a weak increase in cerebellum volume during the critical developmental window of development (β = 0.01), while those who spent more time using social media had a subtle decrease in cerebellum volume (β = − 0.02). These associations persisted in subsequent analysis, even when factors such as preterm birth, lower birth weight, or those with ADHD diagnosis, were excluded, underscoring the robustness of our findings. And these associations also did not differ between the sexes. However, the effect size observed for this association was smaller than our predefined threshold of 0.05. Moreover, in analysing the accumulated differences in cerebellum volumes over 4 years were also very small, which is likely not of relevance to the individual. Nevertheless, this difference was accelerated during the last year (Fig.  2 ). Thus, it is relevant to conduct further research to analyse the long-term effects of social media on brain development.

The term “social media” consists of a broad spectrum of digital tools associated with social interaction, including social networking sites, text messaging applications, and video chatting. Previous studies examining the association between social media use and functional or neural outcomes in both children and adolescents have often either combined all these digital tools under the umbrella term “social media use” 4 , 28 , or scrutinized them separately, distinguishing between social media platforms (e.g., Facebook) and social communication tools (e.g., text messaging) in their analyses 29 , 30 . Consistent with previous studies we first investigated the effect of social media usage on brain development by combining all these digital tools. We specifically included activities related to social media platforms and studied their singular long-term effect on cerebellum development. Even in this refined analysis, we still observed a persistent weak negative effect of social media usage on cerebellum volumes.

If the negative developmental trend for the cerebellum persists, it might be of significant concern, particularly considering that adolescence serves as the period when many psychiatric disorders have their onset 31 , 32 . Moreover, consistent findings report an association between cerebellum abnormalities with various psychiatric disorders, such as depression and anxiety disorders 33 . In addition, the cerebellum is a core component of the neural circuitry underpinning many cognitive deficits associated with ADHD, including working memory, response inhibition, attention shifting, and processing of rewards and temporal information 34 , 35 , 36 , 37 .

The cerebellum is sensitive to environmental exposures both prenatally, as demonstrated by studies of maternal alcohol, maternal diabetes, hypoxia, and postnatal glucocorticoid exposure 38 , 39 , and postnatally 40 . In our study, we observed that children from lower SES quartiles had smaller cerebellum volumes, providing further for the susceptibility of the cerebellum structure to environmental factors 41 , 42 . The transition from childhood to adolescence represents a critical developmental phase characterized by hormonal and physiological changes, including myelination, strengthening of synapses, and selective pruning of neurons and connections. Social media users often contend with constant distractions, which can significantly impact their behavior, leading to inattention symptoms 43 . Additionally, these users can become easily diverted from tasks like reading or homework, etc. Moreover, the use of social media necessitates continual response to stimuli, decision-making, and the execution of motor movements, among various other cognitive and behavioral tasks. Previous studies on social media usage have consistently reported negative effects on life satisfaction 7 , overall well-being 44 , and depressive symptoms 45 , among adolescents. Based on these observations, one might speculate that a distinct window of susceptibility to emotion and frequent shifts in task stimuli might be key contributing factors to the observed decrease in cerebellum volumes. At the neuronal level, this could reflect the acceleration of the natural process of synaptic pruning and changes in myelination among high social media users, which would then appear as a decrease in cerebellum volume at a later time point.

Consistent with prior research 46 , 47 , we observed an inverted U-shaped trajectory in the development of the cortex during mid-childhood and adolescence, with girls reaching their peak earlier than boys. These findings align with histological studies suggesting continued myelination and reduction in synaptic density during adolescence 48 , 49 . At a microscopic level, cortical maturation involves synaptic overproduction in childhood, followed by selective elimination and strengthening of connections later in development 50 . During these stages of development, environmental exposure might guide selective synapse elimination in adolescence 51 , 52 . Supporting this notion, we found that children from lower SES quartiles exhibited smaller global CSA across development compared to their peers.

Although this is a longitudinal study with a large number of participants, the study has some notable limitations. First, this is an observational study, and therefore, we cannot establish causal inference. However, we adjusted for most of the covariates such as age, sex, SES, and genetics. Additionally, to mitigate selection bias, we ensured the inclusion of only one child per family. Second, the estimated time spent on various DM types was self-reported, introducing potential recall or accuracy bias. Nevertheless, it should be noted that studies have reported high test and retest reliability of self-reported behaviors among adolescents 53 . Third, the survey questionnaire utilized to capture DM usage from T 2 visits onwards was modified compared with T 0 or T 1 visits in response to technological advancements and the heightened usage of DM among adolescents. However, we harmonized the survey questionnaires from the T 2 visit onwards to maintain consistency with the earlier visits. Fourth, the response measure for the survey questionnaire in both T 0 and T 1 visits was set between ‘0 and 4+ hours’; this is one of the major drawbacks of the ABCD questionnaire. For example, a child who spent four hours engaged in video games or using social media would receive the same score as a child who spent 12 h, despite the significant difference in their exposure. Fifth, the ABCD questionnaire failed to capture information regarding the timing of DM usage, either during the day or night, thus impeding the exploration of the potential effects of bedtime DM usage on brain development. Finally, the survey questionnaires used in this study failed to capture any information regarding the genre of video games. Given that different activities and actions of video gaming may exert distinct impacts on brain development.

In summary, DM usage, particularly playing video games, does not alter cortical brain development during the 4-year window, but social media usage is weakly associated with a decrease in cerebellum volumes, a trend that was accelerated at later time-points. These findings should be continued by longer follow-up, and more detailed documentation of DM usage, but is a cause for concern regarding the usage of social media in children and adolescents.

Participants

The neuroimaging and behavioral data used in this study were obtained from the ABCD Study (data release 5.0; https://abcdstudy.org/ ; https://doi.org/10.15154/1523041 ), a longitudinal cohort of 11,875 children born between 2005 and 2009. These children were enrolled at ages 9–11 years from 21 research sites across the U.S. between 2016 and 2018 54 , with the intention of following them for a period of at least 10 years. This recruitment cohort closely matches the sociodemographic composition of the US population of 9–11-year-old children. Most of the children were enrolled through local elementary and charter schools at each data-collection site. A smaller portion was recruited through community outreach and word-of-mouth referrals outside of the school setting. Twins were identified and recruited from birth registries 55 , 56 .

During each visit, children accompanied by a parent/guardian, completed a series of measures. These included neurocognitive tests, mental and physical health questionnaires, environmental exposure data collection, providing biological specimens, and participating in brain imaging 54 , 57 , 58 , 59 , 60 , 61 . All were asked for an in-person assessment session for self- or parent-report of mentioned behavioral measures and for biological specimen collections once a year, with brain imaging conducted biannually. For this study, we used data collected between September 2016 and January 2022, which included baseline (T 0 ), 1-year follow-up (T 1 ), 2-year follow-up (T 2 ), 3-year follow-up (T 3 ), and 4 years follow-up (T 4 ) 60 , 61 . Children were excluded if they were born extremely preterm (< 28 weeks of gestation) or had birth weight (< 1200 g), were not proficient in English, had any neurological problems, had a history of seizures, or had a contraindication to undergo brain MRI scans. All children and their parents/guardians provided informed written consent/assent for participation, and the central Institutional Review Board at the University of California, San Diego approved the study protocols. All the research methods were performed in accordance with the relevant guidelines and regulations.

Children who did not have relevant data on either SES, genetics, DM usage; or neuroimaging were excluded from the present study. Additionally, the ABCD cohort included twins and siblings, therefore we randomly selected one child per family to eliminate this source of bias.

Neuroimaging

Children underwent brain MRI scans on 3-Tesla scanner platforms (Siemens Prisma, Philips, or General Electric 750) using a standard adult-sized head coil at three different time points over a span of 4 years (i.e., T 0 , 2 years later (T 2 ), and 4 years later (T 4 )). A standardized protocol for scanning was used to harmonize the scanning sites and MRI scanners. Three-dimensional T1-weighted images (1-mm isotropic) were acquired using a magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequence and processed using FreeSurfer software (version 5.3.0) 62 . All the pre-processed images were quality-checked according to the ABCD protocol, as described earlier 39 , and children with excessive head motion or poor image quality were excluded from the current study. In brief, all the imaging data and FreeSurfer outputs were evaluated by the ABCD Data Analysis, Informatics, and Resource Center (DAIRC) image processing pipeline for real-time motion detection and correction 54 , 57 . In addition, FreeSurfer output was rated manually by a trained technician for the following errors: motion, homogeneity, white-matter underestimation, pial overestimation, and magnetic susceptibility artifacts; and were rated from 0 to 3 (0 = absent, 1 = mild, 2 = moderate, and 3 = severe). As per the ABCD study recommendation, we excluded children with poor scan quality, did not pass manual quality check, or with any incidental findings.

The Destrieux atlas was used to calculate total brain volumes and global cortical surface area (CSA), while the ASEG atlas was used to segment both striatum and cerebellum volumes 57 , 62 , 63 .

Socioeconomic status

SES was defined as the first principal component from a probabilistic principal component analysis (PCA), capturing 65% of the variance in total household income, highest parental education, and neighbourhood quality. Children missing more than one of these SES measures were excluded. Household income was determined by the combined annual income of all family members over the past 12 months, categorized as less than $49,999 (1), $50,000–74,999 (2), $75,000–99,999 (3); $100,000–199,999 (4); and greater than $200,000 (5). Parental education was categorized into middle school or less (1), some high school (2), high school graduate (3), some college/associate degree (4), bachelor’s degree (5), master's degree (6), or professional degree (7). The neighbourhood quality was determined using the area deprivation index, calculated from the American Community Survey using the address of the primary residency 64 . The SES composite and each subcomponent were normalized (mean = 0, SD = 1).

Polygenic score derivation and analyses

Genotyping, quality control, and imputation.

Saliva samples were collected from all the children during the T 0 visit and genotyped using Rutgers University Cell and DNA repository using the Smokescreen array consisting of 646,247 genetic variants 65 .

Quality control, imputation, and genetic PCA were performed by the National Bioinformatics Infrastructure Sweden (NBIS). The following pre-processing steps were conducted. Briefly, single nucleotide polymorphisms (SNPs) with call rates < 98% or minor allele frequencies (MAFs) < 1% were excluded before imputation. Individuals with high rates of missingness (> 2%) and absolute autosomal heterozygosity > 0.2 were excluded, resulting in 10,069 children and 430,622 genetic variants. Haplotypes were prephased using SHAPEIT2, and genetic markers were imputed using IMPUTE4 software.

We utilized the 1000 Genomes haplotypes—Phase 3 integrated variant set release in NCBI build 37 (hg19) coordinates as reference populations. This dataset consists of 2504 samples and 5008 haplotypes from Europeans, Africans, East Asians, Southern Asians, and Americans ( https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html ). We used this imputation since it provides better concordance in diverse human populations 66 , 67 . After that, genotypes with an INFO score < 0.3 or MAF < 0.001% were excluded, which yielded 40,637,119 SNPs in a total of 10,069 children.

The PCA module, as implemented in RICOPILI 68 , was used to check for outliers and control population structure. SNPs were pruned so that there was little linkage disequilibrium (LD) between SNPs (R2 < 0.2, 200 SNP window: Plink–indep-pairwise 200 100 0.2). LD pruning was repeated until 100 K SNPs were reached. The resulting SNPs were then projected into the PCA 69 , 70 . We utilized the first 20 principal components (20PCs) from the genetic PCA.

cogPGS calculation

We created polygenic scores for cognitive performance (cogPGS) in each child using PRSice-2 71 , which involved summing the effect sizes of thousands of SNPs (weighted by the presence of effect alleles in each child). These SNPs were discovered by large genome-wide association studies (GWAS) on educational attainment, mathematical ability, and general cognitive ability 72 . Details regarding the effect sizes and p values of their SNPs can be assessed through the Social Science Genetics Association Consortium ( https://www.thessgac.org/data ).

We utilized the data provided by the consortium from a multitrait analysis of GWAS 73 , which, in our case, represents a joint polygenic score focused on a GWAS of cognitive performance and complemented by information from a GWAS on educational attainment, a GWAS on the highest-level math class completed, and a GWAS on self-reported math ability. This joint analysis is ideal because pairwise genetic correlations of these traits were high 72 , and these GWAS had hundreds of thousands of individuals. Such a large sample size allows new studies to detect effects in samples of a few hundred individuals with 80% statistical power.

To construct the cogPGS, we performed clumping and pruning to remove nearby SNPs that are correlated with each other. The clumping sliding window was 250 kb, with the linkage disequilibrium clumping set to r 2  > 0.25. We included the weightings of all SNPs, regardless of their p-value from the GWAS (p = 1.00 threshold), resulting in 5255 SNPs. Finally, we normalized (mean = 0, SD = 1) the cogPGS to fairly compare their effects on different phenotypes. For the present study, we used cogPGS to reflect the genetic predisposition of cognitive performance and included 20 genetic principal components (PCs) to account for the possibility of population stratification within the Add Health European-ancestry subsample in the same model.

Digital media usage

The estimated time spent on individual DM usage (i.e., using social media, playing video games, or watching television/videos) was assessed at all annual visits (i.e., T 0 , 1 year later (T 1 ), T 2 , 3 years later (T 3 ), and T 4 ) using the self-reported Youth Screen Time Survey.

Self-report survey

At each visit, children reported the number of hours they spent on a typical weekday (i.e., Monday to Friday during the school year and holiday/school breaks) as well as weekend days (i.e., Saturday and Sunday). These hours were categorized by device, media platform, or activity excluding the number of hours spent on school-related work. Specifically, they reported the number of hours dedicated to the following activities:

watching television or movies,

watching videos (e.g., YouTube),

playing video games on a computer, console, phone, or another device (e.g., Xbox, PlayStation, iPad),

Texting on a cell phone, tablet, or computer (e.g., Google Chat, WhatsApp),

Visiting social networking sites (e.g., Facebook, Twitter, Instagram), and

Using video chat (e.g., Skype, FaceTime).

To be consistent with our earlier study 4 , we categorized DM usage as follows: (a) using social media (4 + 5 + 6), (b) playing video games (3), or (c) watching television/videos (1 + 2). The response options included were none—‘0’, < 30 min—‘0.25’, 30 min—‘0.5’, 1 h—‘1’, 2 h—‘2’, 3 h—‘3’, or > 4 h—‘4’.

To calculate the average hours spent per day for individual DM usage, the following formula was used: [(total number of hours spent on a weekday * 5) + (the total number of hours on a weekend day * 2)]/7.

For both the T 0 and T 1 visits, data were collected using the same categorical scale as described above. However, starting from T 2 , modifications were made to the Youth Screen Time Survey to accommodate the increasing DM usage among school-aged children. The time spent watching television was changed into ‘watching or streaming videos or movies’, while watching videos (such as YouTube) was changed into ‘watching or streaming videos or live streaming (such as YouTube, Twitch)’. Then, these categories were merged into a single category named ‘watching television/videos’. ‘Video chatting, visiting social media apps, and texting cell phone’ were combined into a broader category called ‘using social media’. The activities ‘editing photos and videos’ and ‘searching or browsing the internet’ were excluded as they were not present in the T 0 data. Playing video games was further divided into two subcategories, i.e., ‘time spent on single-player’ and ‘time spent on multi-player’, which were combined as ‘playing video games’.

Additionally, the response format was changed from categorical to continuous, with response options including 0 min, 15 min, 30 min, 45 min, 1 h, 1.5 h, 2 h, 2.5 h, 3 h, and every additional hour up until 24 h.

To ensure consistency across all time points, we standardized the T 2 , T 3 , and T 4 visit data to align with the T 0 and T 1 visit data. As a result, the data from the T 2 , T 3 , and T 4 visits were recoded to match the categories used in the T 0 and T 1 visits. The recoding involved transforming the continuous response options into the following categories: none—‘0’, < 30 min—‘0.25’, 30 min—‘0.5’, 1 h—‘1’, 1.15 h—‘1.25’, 1.30 h—‘1.5’, 2 h—‘2’, 2.15 h—‘2.25’, 2.30 h—‘2.5’, 3 h—‘3’, 3.15 h—‘3.25’, 3.30 h—‘3.5’, and > 4 h—‘4’.

There were good test–retest correlations between individual DM usage across different time points, with r-values ranging from 0.24 to 0.56 (eFigure 1).

Parent-reported survey

Caregivers/parents were asked to report the number of hours spent by their child on a typical weekday and weekend day engaging in total on watching television, shows or videos, texting or chatting, playing games, or visiting social networking sites (Facebook, Twitter, Instagram), excluding the number of hours spent on school-related work during T 0 and T 1 visits. Parents provided the total estimated time spent on these activities in both hours and minutes for weekdays and weekends. To calculate the average hours of screen time per day, we used the following: [(total number of hours spent on a weekday * 5) + (the total number of hours on a weekend day * 2)]/7.

Furthermore, we assessed the agreement between caregivers/parents and child reports regarding the estimated amount of time spent on total screen activities (i.e., watching television/videos, playing video games, or engaging in social media) during the T 0 visit, using a correlation coefficient and found it to be 0.37, indicating fair agreement between caregivers/parents and children. To obtain the child’s report on the estimated screen time at T 0 , we summed the time spent watching television/videos, playing video games, or using social media.

We opted to use the self-reported surveys completed by the children rather than relying on caregivers/parents 74 , 75 . Since caregivers/parents may not be fully aware of specific types of DM used by their children, including those aged 9–11 years and older, who often use DM without supervision, such as in their bedrooms at night. Consequently, children may provide more accurate reports of their estimated time spent on each type of DM usage. There is also substantial evidence showing that children as young as 6 years old can reliably report on their own health 75 .

In light of the COVID-19 lockdown, it is probable that these children could spend more time using DM than anticipated at T 0 . This effect was more pronounced in a US-based study, which reported a two-fold increase in the estimated time spent on DM usage during the COVID-19 lockdown compared to the pre-pandemic period 76 . Therefore, to account for an increase in estimated time spent using DM among children between T 0 and T 4 , we used the average estimated time spent for individual DM usage, rather than relying solely on data from either T 0 or T 4 for the longitudinal analyses. The average estimated time spent for individual DM usage was calculated by averaging the estimated time spent for each type of DM usage across all time points.

These predefined outcomes included the global CSA and the volumes of the striatum and cerebellum. We defined the striatum by combining the volumes of the caudate nucleus, putamen, and accumbens. As for the cerebellum, we combined the volumes of both grey and white matter structures of the cerebellum. Both striatum and cerebellum volumes were adjusted for the total brain volumes. In these analyses, we considered both the left and right hemispheres together.

Statistical analysis

Descriptive statistics including means and standard deviations (SDs) were calculated.

The first research question aimed to assess whether individual DM usage altered (i.e., increased or decreased) brain development over 4 years.

To address this question, we first inspected the developmental trends of brain structures (i.e., global CSA, cerebellum, and striatum) between mid-childhood and early adolescence, which are not always linear. Earlier studies on brain development have reported both linear and quadratic trends between childhood and adolescence 77 , 78 , 79 , 80 . To do so, we compared the default linear model to a complex quadratic model to identify whether adding the quadratic age effect significantly improved the goodness of fit for the global CSA, cerebellum, and striatum. In both these models, we adjusted for SES, polygenic scores cogPGS, and 20PCs. We assessed the fit of the models based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The model with lower AIC and BIC values was considered a better fit (at least by 10 points less than the other model) (eTable 1 ) 81 . The log-likelihood ratio test (χ 2 ) was additionally run to confirm the results.

When we examined the models, the quadratic model fitted the data well and was subsequently used for further analysis. In addition, age-related change in the peak location along with sex effect was assessed. Peak age for each brain structure was calculated using the first derivative of the quadratic equations.

We constructed a quadratic mixed-effect model to investigate the relationship between individual DM usage and brain structures over time. The model (Eq.  1 ) was adjusted for various factors: age at baseline (mean-centered to reduce multicollinearity), SES, cogPGS, 20 PCs, and sex assigned at birth as fixed effects, and study sites were included as random effects.

To test the long-term effect of DM usage on brain development with time (as outcomes of interest), we included a two-way interaction with average DM usage and time as both linear and quadratic terms (i.e., average DM usage x Time; average DM usage x Time 2 ). Furthermore, to account for SES and cogPGS effects on brain development over time, we included three-way interactions in the same model (i.e., for SES, average DM usage x Time x SES; and average DM usage x Time 2 x SES; for cogPGS, average DM usage x Time x cogPGS; and average DM usage x Time 2 x cogPGS). Both the intercepts and the slopes were used as random-effects terms, allowing children to start at different levels of surface area/volumes. The ‘lmer’ function of package lme4 in R software was used to fit the model, and the restricted maximum likelihood method was used to estimate the model parameters 82 , 83 .

β 0 represents the intercept; β represents the parameter estimate, \({cogPGS}_{i}\) represents the polygenic scores for cognitive performance of a child i; \({t}_{ij}\) represents the effect of time (denotes the follow-up time for child i at visit j , fitted as a continuous measure in years; \({Sex}_{i}\) sex of a child, dummy coded (1  =  M, 0  =  F); \({Age}_{i}\) age of child i as a continuous measure at baseline; \({SES}_{i}\) represents the socioeconomic status for child i; \({Ancestry}_{i}\) represents the ancestry differences in genetic structure that could bias the findings; \({DM Usage}_{i}\) represents the amount of average estimated time spent for child i, fitted as a continuous measure; \({\upsilon }_{0i}\) and \({\upsilon }_{1i}\) are the random effects, and \({\varepsilon }_{ij}\) is the random error term at the j th time point for child i.

To determine the effect of sex-related differences on the relationships between DM usage and brain development, we added an interaction effect of sex (i.e., average DM usage x Time x sex; and average DM usage x Time 2 x sex) to the pre-existing model (Eq.  2 ).

Considering the numerous statistical tests conducted, the Bonferroni corrections were applied to control for Type-1 error 84 . In total, we performed three individual DM usage models (i.e., using social media, playing video games, watching television/videos) for three brain structures analyzed in the overall cohort as well as for sex, resulting in a total of 18 tests. P  < 0.003 was considered statistically significant.

An additional analysis was conducted to investigate whether the estimates of DM preceded changes in cerebellum volume. A linear model was employed to ascertain whether the average social media usage of the first two time points (i.e., (T 0  + T 1 )/2) could predict later changes in cerebellum volumes between T 2 and T 4 , while adjusting for the aforementioned covariates (i.e., age at baseline, SES, cogPGS, 20 PCs, and sex assigned at birth as fixed effects, and study sites as random effects). Subsequently, the same analysis was repeated using the average time spent playing video games during the first two time points in cerebellum volumes. We then investigated whether social media usage at T 0 could predict the changes in cerebellum volumes (T 4 –T 0 ) over the study period, while adjusting for prespecified covariates as mentioned above. In addition, we explored whether excluding time spent on video chatting or texting from social media usage would alter the results (Eq.  1 ; Table 2 ).

We ran multiple robustness tests to validate our findings and they were uncorrected. Firstly, we excluded children who were born preterm (< 37 weeks), had low birth weight (< 2500 g) or had a diagnosis of ADHD. Those born preterm or with low birth weight tend to have altered developmental trajectories 85 , 86 , 87 . Similarly, children with ADHD have delayed maturation, which might affect our findings 88 . Secondly, we restricted our analysis by including children with MRI data for all three-time points.

The gestation length and birth weight of each child were reported by caregivers/parents through a self-reported questionnaire. The presence of ADHD symptoms in the child, whether in the past or currently, was assessed through caregivers/parents reports using the computerized Kiddie-Structured Assessment for Affective Disorders and Schizophrenia (KSADS) during the T 0 visit. This tool is based on a well-studied and validated tool, both in research and clinical settings. Diagnoses of ADHD were made in accordance with DSM-5 criteria, which require an endorsement of six or more symptoms of inattention or hyperactivity-impulsivity.

Data availability

The data used for the analyses presented in this paper are from the Adolescent Brain Cognitive Development (ABCD) Study [ https://abcdstudy.org ; NIMH Data Archive (NDA)]. Data can be accessed by directly applying to the NDA.

Code availability

The code to replicate all analysis described in this manuscript can be found here: https://github.com/samniv/ScreenTime-and-Brain/tree/main .

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Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study ( https://abcdstudy.org ), held in the NIMH Data Archive (NDA). The ABCD study was supported by the National Institutes of Health and additional federal partners under Award Nos. U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of supporters is available at  https://abcdstudy.org/?s=nIH+collaborators . A listing of participating sites and a complete listing of the study investigators can be found at  https://abcdstudy.org/principal-investigators.html . The ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Open access funding provided by Karolinska Institute. This study was supported by the Swedish Research Council to Torkel Klingberg.

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Samson Nivins, Nicholas Judd & Torkel Klingberg

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S.N.: Conceptualization, Writing—original draft, Investigation, Methodology, Formal analysis, Visualization. B.S., N.J.: Conceptualization, Methodology, Writing—review and editing. M.L.: Conceptualization, Writing—review and editing. T.K.: Conceptualization, Methodology, Writing—critical review, insights, and editing, Funding acquisition.

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Handbook of Children and Screens

Digital Media, Development, and Well-Being from Birth Through Adolescence

• Open Access
• © 2025

You have full access to this open access Book

• Dimitri A. Christakis 0 ,
• Lauren Hale 1

Seattle Children’s Research Institute, University of Washington, Seattle, USA

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School of Medicine, Stony Brook University, Stony Brook, USA

• Describes the cognitive, physical, and psychosocial impacts of digital technology on infants, children, and adolescents
• Explores how media influences individuals as well as relationships, family, culture, and society
• Examines the impacts of specific digital domains pertinent to youth (e.g., education technology, video gaming)
• This book is open access, which means that you have free and unlimited access

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About this book

This open access handbook synthesizes the current research about the impacts of digital media on children across development. Drawing on the expertise of scientists and researchers as well as clinicians and practitioners, the book summarizes research through interdisciplinary expert reviews. First, it addresses the cognitive, physical, mental, and psychosocial impacts on infants, children, and adolescents. Next, the book explores how media influences relationships, family, culture, and society. Finally, it examines the impacts of specific digital domains pertinent to youth, including education technology, video gaming, and emerging technologies. Chapters employ a parallel structure, including background on the topic, summary of the current state of the research, future research directions, and recommendations for relevant stakeholders. The volume examines the timely issue of optimal child development in an increasingly digital age, offering innovative approaches to establish a solid and robust scientific foundation for this field of study as well as evidence-based action for adults who support positive youth development.

Key areas of coverage include:

• Cognition and brain development.

• Physical and mental health.

• Problematic uses of the internet.

• Gender and sexuality.

• Parenting in the digital age.

• Cyberbullying and digital cruelty.

• Media policy.

The  Handbook of Children and Screens is a must-have resource for researchers, professors, and graduate students as well as clinicians, therapists, educators, and related professionals in clinical child, school, and developmental psychology, social work, public health, epidemiology, neuroscience, human development and family studies, social psychology, sociology, and communication.

This is an open access book.

• Body image, disordered eating, digital media, adolescents
• Digital media, cognition, brain development, infancy, childhood
• Cyberbullying, digital cruelty, children, adolescents
• Diversity, marginalized youth, online identity
• Education technology, data privacy, surveillance, datafication
• Empathy, kindness, dignity, digital media
• Family, parenting, culture, society, media influences
• Gaming disorder, screen use, children, adolescents
• Gender, sexuality, adolescence, digital media
• Language development, digital media use, early childhood
• Neural development, screen media, learning, children
• Obesity, nutrition, digital media, children, teens
• Race, racism, youth, digital media
• Relationships, identity, social media, childhood, adolescence
• Screen media, children, imagination, creativity, play
• Sleep health, screen use, childhood, adolescence

Table of contents (87 chapters)

Front matter, introduction.

• Dimitri A. Christakis, Lauren Hale

Research Concerning Cognitive, Physical, Mental, and Psychosocial Impacts on Children

Introduction to the section on digital media, cognition, and brain development.

• Heather Kirkorian

Digital Media, Cognition, and Brain Development in Infancy and Childhood

• Heather Kirkorian, Rachel Barr, Sarah M. Coyne, Tiffany Grace-Chung Munzer, Martin Paulus, Moriah E. Thomason

Digital Media, Cognition, and Brain Development in Adolescence

• Laura Marciano, Bernadka Dubicka, Lucía Magis-Weinberg, Rosalba Morese, Kasisomayajula Viswanath, René Weber

The Short- and Long-Term Effects of Digital Media Use on Attention

• Susanne E. Baumgartner, Douglas A. Parry, Ine Beyens, Wisnu Wiradhany, Melina Uncapher, Anthony D. Wagner et al.

Digital Media Use and Language Development in Early Childhood

• Rebecca A. Dore, Mengguo Jing, Gemma Taylor, Sheri Madigan, Preeti G. Samudra, Annette S. Sundqvist et al.

Imagination, Creativity, and Play

• Rebekah A. Richert, Koeun Choi, Tracy R. Gleason, Thalia R. Goldstein, Susan M. Sibert

Digital Media and Neurodevelopmental Differences

• Meryl Alper, Alyssa M. Alcorn, Kristen Harrison, Jennifer A. Manganello, Rachel R. Romeo

Introduction to the Section on Screens and Physical Health

Lauren Hale

Digital Screen Media Use, Movement Behaviors, and Child Health

• Mark S. Tremblay, Nicholas Kuzik, Stuart J. H. Biddle, Valerie Carson, Mai J. M. Chinapaw, Dorothea Dumuid et al.

Screen Media, Obesity, and Nutrition

• Amanda E. Staiano, Alyssa M. Button, Gary S. Goldfield, Thomas N. Robinson, Jorge Mota, Susan J. Woolford et al.

Digital Food Marketing and Children’s Health and Well-being

• Jennifer L. Harris, Frances Fleming-Milici, Ashley N. Gearhardt, Sonya Grier, Kathryn Montgomery, Maria Romo-Palafox et al.

Alcohol, Tobacco, and Firearm Promotion in Digital Media: Corporate Influences on Adolescent Health

• Jennifer A. Emond, Jeffrey Chester, Jonathan Noel, Jon-Patrick Allem, Brad J. Bushman, Brian Primack et al.

What Do We Know About the Link Between Screens and Sleep Health?

• Lauren Hale, Lauren E. Hartstein, Rebecca Robbins, Michael A. Grandner, Monique K. LeBourgeois, Michelle M. Garrison et al.

Screen Use, Physical Injuries, and Orthopedic Health

• Jennifer A. Manganello, Lara B. McKenzie, Despina Stavrinos, Leon Straker

Introduction to the Section on Digital Media and Mental Health

• Paul Weigle

Social Media and Youth Mental Health: A Departure from the Status Quo

• Sarah M. Coyne, César Escobar-Viera, Mesfin A. Bekalu, Linda Charmaraman, Brian Primack, Reem M. A. Shafi et al.

Youth Anxiety in the Digital Age: Present Status and Future Considerations

• Merlin Ariefdjohan, Jacqueline Nesi, Benjamin Mullin, Matthew Pesko, Sandra Fritsch

Editors and Affiliations

Dimitri A. Christakis

About the editors

Dimitri A. Christakis, MD, MPH., is the George Adkins Professor of Pediatrics at the University of Washington, the Chief Health Officer at Special Olympics International, Editor in Chief of JAMA Pediatrics, and Chief Science Officer at Children and Screens: Institute of Digital Media and Child Development. He has devoted his career to investigating how early experiences impact children and to helping parents improve their children’s early learning environments. He and his colleagues in the Christakis Lab have made a number of landmark findings, including discovering that young children who watch TV are more likely to develop attention problems and other health and behavioral issues. He is the author of more than 250 original research articles, a textbook on pediatrics, and co-author of the book,  The Elephant in the Living Room: Make Television Work for Your Kids . He has appeared on CNN, NPR, Today, CBS News, ABC News, and NBC News and was recently featured as a TEDx speaker. 

Lauren Hale, Ph.D., Professor of Family, Population, and Preventive Medicine; Core Faculty, Program in Public Health; Renaissance School of Medicine, Stony Brook University, studies the social patterning of sleep health and how it contributes to inequalities in health and well-being with current or previous funding from NICHD, NIDDK, NHLBI, and NIA in addition to private funding. Dr. Hale has more than 160 publications in peer-reviewed journal articles. She serves on the Board of Directors (recently as Chair) of the National Sleep Foundation and is the founding Editor-in-Chief of the journal Sleep Health as well as the Scientific Advisory Panel of the Pajama Program and Children and Screens: Institute of Digital Media and Child Development.

Bibliographic Information

Book Title : Handbook of Children and Screens

Book Subtitle : Digital Media, Development, and Well-Being from Birth Through Adolescence

Editors : Dimitri A. Christakis, Lauren Hale

DOI : https://doi.org/10.1007/978-3-031-69362-5

Publisher : Springer Cham

eBook Packages : Behavioral Science and Psychology , Behavioral Science and Psychology (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s) 2025

Hardcover ISBN : 978-3-031-69361-8 Due: 17 January 2025

Softcover ISBN : 978-3-031-69364-9 Due: 17 January 2026

eBook ISBN : 978-3-031-69362-5 Published: 05 December 2024

Edition Number : 1

Number of Pages : XLI, 657

Number of Illustrations : 4 b/w illustrations, 4 illustrations in colour

Topics : Developmental Psychology , Pediatrics , Child and School Psychology , Children, Youth and Family Policy , Clinical Psychology , Public Health

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