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The Neural and Network Dynamics of Social Influence Across Adolescence

Periodic Reporting for period 2 - Social Smart (The Neural and Network Dynamics of Social Influence Across Adolescence)

Reporting period: 2020-11-01 to 2022-04-30

Peer influence has long been established as one of the most important factors in almost all aspects of adolescent development and behavior, including academic achievement, prosocial behavior and risk-taking. More recently, peer influence seems to be amplified by developments in social media (e.g. viral risk-taking challenges). However, surprisingly little is known about the mechanisms underlying peer influence, or if and when adolescents are specifically sensitive to their peers. Here we zoom in on the contextual factors that play a role in peer influence by integrating social network analyses, and in addition we zoom in on the cognitive mechanisms that are involved in peer influence by using experimental design in combination with computational modeling and neuroimaging.

During the adolescent social re-orientation there is a shift in influence from parents to peers. This is part of the natural process of adolescence becoming independent adults. However, this also means that peers will have more and more influence on the development of adolescents and important life choices they will make. These formative years can set you up for a successful and thriving adulthood, but is also a period that is associated with the onset of most mental and behavioral problems. Understanding the role of peer influence is thus vital for understanding how we can best support adolescent in their development in this period. In our experiments we are able to tease apart the different motivations that drive peer influence and also to find which peers are especially influential.

Often when we think about peer influence and adolescence we think about all the negative things that result from this combination. But social learning is a very important human skill and is the cognitive ability that is foundational for human culture and cultural evolution. Our objectives are thus two-fold. First, we want to further our insights in the context and mechanisms of peer influence, second, we want to better understand under which circumstances social learning is beneficial or as unfavorable impact on adolescent development. Combining both questions we want to study how adolescent become socially smart (SOCIAL SMART), that is when do they have the insights and cognitive capacities to use social information to their benefit and avoid the harm.
We started out our project with developing a new method for studying how social influence works in adolescent social networks. For this we have set-up a mobile lab to do research at schools. This consists of 64 android tablets, 4 wifi routers, and dividers shield to ensure privacy (see Figure 1). With this mobile lab, we went to schools to collect data in two waves. First, we established the social network in a classroom based on sociometric data (e.g. friend ratings). At the same time, we measured behavior of interest on an experimental task. In the second session, we would return, and the pupils would do the same task but this time they would be confronted with earlier answers of their peers. Together with the detailed information about the social networks this allowed us to ask specific scientific questions regarding the impact of social information in relation to specific peer relations, peer status or position of pupils in the network. Finally, for our work at schools we also have developed a web-portal for pupils, parents and teachers, where they can find information about our work and sign up for studies (see Figure 1).

We have studied the role of social impact in several domains across different studies such as pro-social behavior (paper under revision), decisions under uncertainty (under revision), social norms (Pinho et al., 2021) and perception (Gradassi et al., 2021). With these studies we have also looked at different social relationships and have already gained a few key insights about social learning in adolescence. First, we have established that the impact of peers is generally declining with age across adolescence. Second, we have been able to confirm that high status individuals indeed have outsized social impact, even on those who are not directly friends. However, we have also found that those who are perceived as smart also have significantly more influence than others. The latter is hopeful because this already indicates that social information use in adolescence is to some extent a rational choice.

The perceptual estimation task has been central in the development of our conceptual (Molleman et al., 2019) and computational framework (Molleman et al., 2020) and nicely illustrates our approach. In this simple task people must quickly estimate the number of animals they see on the screen. Once they have done this, they are presented with the estimate of another person who also saw the same picture (see Figure 2). Now they can decide to change their initial estimate, the extent to which they change is our measure of social impact. They are rewarded for accuracy on both initial and second estimate, so should only change their mind when they think it really improves their results. This task was developed to be easily done by children as well as adolescents. The estimation task has now also been deployed in the two Science Museums: The Humboldt Forum in Berlin, and the NEMO science center in Amsterdam. In Berlin, the task is part of the permanent science exhibition (Humboldt Labor; currently data of 2000+ visitors, age ranges 10-90 from 25+ countries) and in Amsterdam we have used it to collect data for 8 weekends on social learning within families (135 families participated fig 3).
Our experimental approach is beyond the current state of the art given that it combines social network data with experimental control. Previously social network studies had mainly focused on observational or self-report of behavior at different time points. What happened in between in terms of social interactions is then inferred from the data based on several assumptions, here we can directly measure those interactions and their consequences. On the other hand, most previous experimental work used anonymous/unknown peers and thus did not reveal anything about the impact of social relations on social influence, which we were able to show are key. In our next set of studies, we will focus on additional aspects that may impact social learning, for instance how certain or uncertain individuals are before they receive social information. In addition, we will focus on information search, that is not just presenting the adolescents with information but letting them actively search for it. This agentic perspective has up to know been mostly ignored.

In the near future, functional magnetic imaging to gain insight in how brain development, is associated with changes in specific computational processes that are involved in social learning in adolescence. In this set of studies, we will focus on how social information from different sources in processed by the developing brain, how the social network is represented and how that impacts social information use. These studies may help further our understanding of why adolescents differ from both children and adults in their social information use. &
Classroom setup
data collection at museum
Estimation task