Neural computations underlying social risk sensitivity

Under standard models of expected utility, preferences over stochastic events are assumed to be independent of the source of uncertainty. Thus, in decision-making, an agent should exhibit consistent preferences, regardless of whether the uncertainty derives from the unpredictability of a random process or the unpredictability of a social partner. However, when a social partner is the source of uncertainty, social preferences can influence decisions over and above pure risk attitudes (RA). Here, we compared risk-related hemodynamic activity and individual preferences for two sets of options that differ only in the social or non-social nature of the risk. Risk preferences in social and non-social contexts were systematically related to neural activity during decision and outcome phases of each choice. Individuals who were more risk averse in the social context exhibited decreased risk-related activity in the amygdala during non-social decisions, while individuals who were more risk averse in the non-social context exhibited the opposite pattern. Differential risk preferences were similarly associated with hemodynamic activity in ventral striatum at the outcome of these decisions. These findings suggest that social preferences, including aversion to betrayal or exploitation by social partners, may be associated with variability in the response of these subcortical regions to social risk

Neural cognitive control moderates the association between insular risk processing and risk-taking behaviors via perceived stress in adolescents

Adolescence is a critical period for the initiation of risk-taking behaviors. We examined the longitudinal interplay between neural correlates of risk processing and cognitive control in predicting risk-taking behaviors via stress. The sample consisted of 167 adolescents (53% males) who were assessed twice (MAgeTime1 = 14.13, MAgeTime2 = 15.05). Neural risk processing was operationalized as blood-oxygen-level-dependent (BOLD) responses in the anterior insula during a lottery choice task and neural cognitive control as BOLD responses during an inhibitory control task. Adolescents reported on perceived stress and risk-taking behaviors. Structural equation modeling analyses indicated that low insular risk processing predicted increases in perceived stress, while perceived stress did not predict changes in insular risk processing across one year. Moreover, significant moderation by neural cognitive control indicated that low insular risk processing predicted increases in risk-taking behaviors via increases in perceived stress among adolescents with poor neural cognitive control, but not among adolescents with good neural cognitive control. The results suggest that risk processing in the anterior insular cortex plays an important role in stress experience and risk-taking behaviors particularly for vulnerable adolescents with poor neural cognitive control

Social Risk Taking in Social Groups

Adolescence is a period marked by increased risk taking behaviors that can either have positive or negative health consequences. A large body of literature has given much attention to examining adolescents' engagement in risky behaviors that have a negative impact on their health such as substance use, reckless driving, and unsafe sexual behavior (Reyna & Farley, 2006). However, investigating positive risk taking behavior during adolescence is equally important for developing adaptive patterns of behavior that promote health (Duell & Steinberg, 2018; van Hoorn, McCormick, & Telzer, 2018). Prior research indicates that risk taking is a normative developmental process providing adolescents with learning opportunities critical for developing self-identity, social relationships, and healthy exploration (Goldenberg, Telzer, Lieberman, Fuligni, & Galvan, 2017). Initiating interactions with peers is a form of positive social risk taking behavior that is necessary for creating meaningful relationships and development of social skills as well as understanding social norms (van Hoorn et al., 2018). Previous studies demonstrate that as adolescents increase in independence from parents and spend greater amounts of time with peers, they become hypersensitive to social signals and may avoid taking social risks (Gardner & Steinberg, 2005; Blakemore, 2018). Taking social risks to engage with peers may vary depending on the number of peers present within a social group. The proposed study aims to test whether social risk taking changes as a function of the number of peers present within a social group. Moreover, we are interested in whether the topological structure of social connections that make up an adolescent's social network is associated with the extent to which adolescents take social risks to engage with peer social groups. Participants' social network structures will be measured using a facilitated name generating method. To protect the privacy of the participants, the names/nicknames that participants reported will be anonymized and recoded to numbers before the data is recorded. As such, no personally identifiable information will be recorded in this task

Temporal changes in social network and social risk taking

Adolescence, defined as the transition phase between childhood and adulthood, is an important developmental period for learning, adjustment, and obtaining independence. During adolescence, individuals are learning how to navigate new, and often changing, social environments (Crone & Dahl, 2012). Adolescents demonstrate increased risk-taking behavior compared to adults (Duell et al., 2018). This increase in risk-taking is thought to be necessary and adaptive as it facilitates the exploration of new social roles, creation of meaningful relationships, learning new skills, and engagement in other rewarding experiences (Duell & Steinberg, 2018;van Hoorn et al., 2018). Although extensive prior work has examined temporal changes in general risk-taking, these studies mostly focused on risk-taking in laboratory tasks and real-life risk-taking in health and antisocial domains (i.e. Duell et al., 2018). Less research has examined longitudinal changes in adolescents' social risk-taking (i.e. answering a question in front of the class). A recent study (Blakemore, 2018) proposed that as adolescents demonstrate increased sensitivity to social cues (including negative social consequences such as peer rejection and exclusion), adolescents may demonstrate equal, or even decreased tendencies to take social risks. As such, the proposed study aims to investigate the temporal stability and changes in 1) adolescents' social risk-taking tendencies and 2) social network structures. Second, as adolescents start to spend more time with their peers compared to their parents, social relationships and peer network plays an increasingly vital role in adolescents' development (Gardner & Steinberg, 2005). Being part of a supportive, connected social network provides the adolescent with social support, diverse social contexts and new experiences, and contributes to physical and psychological wellness (Jackson & Bosma, 1992; Cruwys et al., 2013). Social network characteristics are likely to influence and be influenced by individual differences in information processing, social cognition, and decision making (Falk & Bassett, 2017). For instance, people who are open to new experiences tend to have smaller friend groups but are more likely to occupy brokerage network positions (Fang et al., 2015). That is, people with high levels of openness are more likely to connect otherwise unconnected nodes. In addition, preliminary evidence suggests that adolescents with higher neural sensitivity to risks tended to have more clusters in their social networks (Pei et al., in prep), suggesting that individuals with higher risk-taking tendencies may be more willing to try new activities and reach out to unfamiliar friend groups, and thus have more clusters (groups) in their social network. To this end, the second aim of the proposed study is to assess longitudinal relationships between social risk-taking and social network structure. Given the difficulty of experimentally manipulating individuals' social risk tendencies as well as their social network structures, we propose to utilize a longitudinal study design with three time points to study the extent to which social network structure (i.e., density of connections, social clusters) may influence adolescents' social risk-taking tendencies as well as how social network structure may influence the selection of a particular strategy. Participants' social network structures will be measured using a facilitated name generating method. To protect the privacy of the participants, the names/nicknames that participants reported will be anonymized and recoded to numbers before the data is recorded. As such, no personally identifiable information will be recorded in this task

Linking Risk Perceptions and Risk Taking

Adolescence compared to children and adults engage in increased risk taking behaviors that can either have positive or negative health consequences (Crone & Dahl, 2012). One critical factor known to contribute to engagement in health behaviors is the extent to which individuals perceives risk in their environment (Slovic, 1964; Portnoy, Ferrer, Bergman, & Klein, 2014; Weinstein, 2003; Schmalzle, Renner, & Schupp, 2017). Specifically, a few studies have suggested that adolescents may overestimate the likelihood of positive outcomes and underestimate the likelihood of negative outcomes, though the evidence is limited (for review, see Reyna & Farley, 2006). In addition, there are some studies that report a negative relation between health risk behaviors and adolescents' estimations of the likelihood of negative outcomes (Chapin, 2001; Johnson et al., 2002). It is important to note that the majority of this work has examined positive and negative outcomes separately using different methods. Furthermore, this work has primarily centered on health risk behaviors and not on how estimations are made about health promotion behaviors. This is a critical gap in the literature given that most educational interventions to reduce negative behaviors are based on increasing positive risk taking behaviors. While these interventions represent worthwhile efforts, understanding how adolescents perceive health risk and health promotion behaviors and their consequences could help the design of interventions to maximize such efforts. Furthermore, it is also unclear how adolescents update their existing risk perceptions when given information that does not match their original estimation. In this proposal, we seek to examine these gaps in the literature by: i) quantifying risk perceptions of both health risk and health promotion behaviors that vary in their likelihood that they occur alone or with others; ii) examining the extent to which risk perceptions are updated for different types of risk; and iii) test the relation between risk perceptions of both health risk and health promotion behaviors and risk taking behavior in non-social and social contexts. This work has the potential to directly inform current and future intervention work on reducing adolescent risky behavior and improving adolescent health outcomes

Social risk taking and adaptive adolescent development

Adolescents across the world demonstrate increased risk-taking behavior compared to adults. Although adolescent risk-taking may lead to negative consequences (Reyna & Farley, 2006), recent studies highlight ways that risk-taking during adolescence can be adaptive (Duell & Steinberg, 2018). Specifically, these studies indicate that increased risk-taking behavior during adolescence is a normative developmental process that brings crucial opportunities for health promotion, academic achievements, social engagement, and life success (Crone & Dahl, 2012). As such, an important goal for parents, educators, and health practitioners is to effectively promote positive risk-taking behaviors in adolescents. Here we focus on one specific type of potentially positive risk-taking behavior: social risk-taking. We focus on social risk-taking because 1) social risk-taking during adolescence is functionally adaptive as it facilitates the exploration of new social roles, creation of meaningful relationships, learning new skills, and engagement in other rewarding experiences (van Hoorn et al., 2018). For instance, intellectual risk-taking (i.e. sharing one's thoughts during class discussions), can help shape students' academic identity and promote academic achievement (Streitmatter, 1997).; and 2) as adolescents start to spend more time with their friends than their parents, they are particularly sensitive to social cues (Gardner & Steinberg, 2005) and may particularly avoid social risk-taking during this time period (Blakemore, 2018). To this end, our proposal is centered on assessing the effectiveness of various strategies in promoting social risk-taking in adolescents. The strategies included in this proposal are drawn from two lines of literature, social influence and emotion regulation. First, peers have been found to exert great influence on human decision making (Blankenstein et al., 2016), especially on risky decision making in adolescents (Brechwald & Prinstein, 2011). Injunctive and descriptive norms are two types of norms that guide decision making, with the former providing information about the expectation of peers, and the latter describing what is actually and commonly done by others (Cialdini, Reno, & Kallgren, 1990). Second, cognitive reappraisal, the mental process of reinterpreting an emotional event into less emotional terms (Gross, 1998), is a particularly effective emotion-regulation strategy that can increase or decrease emotion depending on the goal (Ochsner & Gross, 2008). Recent work has adopted this technique into influencing individual responses to health messages and showed that cognitive regulation strategies can promote long-lasting behavioral changes following health messages exposure. Given that social relationships increase during adolescence, it is also important to examine how the structure of social connections in an adolescent's life may influence social risk-taking behavior. We are also interested in examining the extent to which social network structure (i.e., the density of connections, social clusters) may influence the extent to which adolescents engage in social risk-taking as well as how social network structure may influence the selection of a particular strategy. Participants' social network structures will be measured using a facilitated name generating method. To protect the privacy of the participants, the names/nicknames that participants reported will be anonymized and recoded to numbers before the data is recorded. As such, no personally identifiable information will be recorded in this task. In sum, we propose a between-subject experiment to examine the effectiveness of descriptive norm, injunctive norm, and cognitive regulation strategies in promoting positive adolescent risk-taking

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External and Internal Attribution in Human-Agent Interaction: Insights from Neuroscience and Virtual Reality

Agents are designed in the image of humans, both internally and externally. The internal systems of agents imitate the human brain, both at the levels of hardware (i.e., neuromorphic computing) and software (i.e., neural networks). Furthermore, the external appearance and behaviors of agents are designed by people and based on human data. Sometimes, these humanlike qualities of agents are purposely selected to increase their social influence over human users, and sometimes the human factors that influence perceptions of agents are hidden. Inspired by Blascovich’s “threshold of social influence’, a model designed to explain the effects of different methods of anthropomorphizing embodied agents in virtual environments, we propose a novel framework for understanding how humans’ attributions of human qualities to agents affects their social influence in human-agent interaction. The External and Internal Attributions model of social influence (EIA) builds on previous work on agent-avatars in immersive virtual reality and provides a framework to link previous social science theories to neuroscience. EIA connects external and internal attributions of agents to two brain networks related to social influence. the external perception system, and the mentalizing system. Focusing human-agent interaction research along each of the attributional dimensions of the EIA model, or at the functional integration of the two, may lead to a better understanding of the thresholds of social influence necessary for optimal human-agent interaction