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Moderate social sensitivity in a risky context supports adaptive decision making in adolescence: evidence from brain and behavior

Moderate social sensitivity in a risky context supports adaptive decision making in adolescence:... Adolescence is a time of increased social-affective sensitivity, which is often related to heightened health-risk behaviors. However, moderate levels of social sensitivity, relative to either low (social vacuum) or high levels (exceptionally attuned), may confer benefits as it facilitates effective navigation of the social world. The present fMRI study tested a curvilinear rela- tionship between social sensitivity and adaptive decision-making. Participants (ages 12–16; N¼ 35) played the Social Analogue Risk Task, which measures participants’ willingness to knock on doors in order to earn points. With each knock, the facial expression of the house’s resident shifted from happy to somewhat angrier. If the resident became too angry, the door slammed and participants lost points. Social sensitivity was defined as the extent to which adolescents adjusted their risky choices based on shifting facial expressions. Results confirmed a curvilinear relationship between social sensitivity and self-reported adaptive decision-making at the behavioral and neural level. Moderate adolescent social sensitivity was modulated via heightened tracking of social cues in the temporoparietal junction, insula and dorsolateral prefrontal cortex and related to adaptive decision-making. These findings suggest that social-affective sensitivity may positively impact out- comes in adolescence and have implications for interventions to help adolescents reach mature social goals into adulthood. Key words: adolescence; social sensitivity; risk taking; facial expression; adaptive decision-making Introduction social world. Indeed, adolescents show a uniquely heightened sensitivity to social experiences, and moderate levels of adoles- Adolescence, the developmental period between childhood and adulthood, is characterized by heightened social-affective sen- cent social sensitivity may be considered adaptive in order to meet this social developmental milestone. However, being in a sitivity (Crone and Dahl, 2012; Blakemore and Mills, 2014). An important developmental milestone during this time is to learn social vacuum (low social sensitivity) or being exceptionally attuned to one’s social environment (high social sensitivity) to effectively navigate the social world (Blakemore and Mills, 2014). There are tremendous maturational changes in how the likely limits an individual’s ability to successfully interact with others (Nelson and Guyer, 2011; Blakemore and Mills, 2014). brain codes and generates responses to social information throughout adolescence (Nelson et al., 2005; Nelson and Guyer, Hence, this study tests whether moderate levels, as compared to low or high levels of social sensitivity, confer benefits in the 2011; Nelson et al., 2016). These changes in the brain equip ado- lescents with the tools to navigate the increasingly complex context of risky decision-making. Received: 17 July 2017; Revised: 9 February 2018; Accepted: 22 February 2018 V C The Author(s) (2018). Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 547 Social sensitivity and the developing brain differences in sensitivity to social context (Scriber and Guyer, 2017). In particular, the degree to which adolescents are tuned Adolescence is a period of changing social dynamics, with an to their environment may be calibrated through individual dif- increased saliency of social acceptance and rejection, changing ferences in structural and functional neural characteristics. social relationships with parents and peers, and a tendency to Moderate levels of social sensitivity and recruitment of social- explore societal boundaries in many different ways (Crone and affective neurocircuitry are likely related to adaptive outcomes Dahl, 2012; Nelson et al., 2016). Adolescents begin to make in- during adolescence, because this is crucial for competently creasingly independent decisions about how to navigate the interacting with others and flexible social behavior, as well as complex social world based on limited experiences (Ellis et al., normative exploration and risk taking (Nelson and Guyer, 2011; 2012; McLaughlin et al., 2015). Indeed, strategic exploration (i.e. Scriber and Guyer, 2017). However, too much or too little social making choices that provide new experiences and information) sensitivity may be related to maladaptive psychosocial out- emerges rapidly in adolescence and is related to the propensity comes, as each hinders effectively navigating the social world. for risk-taking behaviors in daily life (Somerville et al., 2017). On the more extreme end of the continuum, high social sen- Exploration during adolescence has recently been linked to sitivity may be related to maladaptive outcomes, as research adaptive behaviors in a risky context, such as learning and has shown that greater social-affective sensitivity is related to lower perceptions of real-world risk taking (Goldenberg et al., heightened health-risk behaviors (Chein et al., 2011; Heller and 2017; McCormick and Telzer, 2017). Casey, 2016). Drawing from clinical work, highly socially anx- In order to effectively interact with a wider range of social ious individuals are hyper-attentive to social cues and tend to agents, adolescents need to develop more complex social cogni- have lower thresholds to detect angry faces, which results in tive abilities (Blakemore and Mills, 2014). For example, they impaired social functioning in daily life (Gilboa-Schechtman interact with different teachers for each course in school, de- and Shachar-Lavie, 2013). In line with these findings, chronic- velop more dynamic peer relationships and begin exploring ro- ally victimized adolescent girls as compared to non-victimized mantic interests without direct scaffolding from parents. In girls show greater risk-taking behavior after an episode of exclu- order to navigate these more complex social relationships, so- sion which is mediated by greater activation in regions involved cial cognitive abilities are required, which include being attuned in affective sensitivity, social cognition and cognitive control to and adequately processing social cues, engaging in mentaliz- (Telzer et al., 2017). On the other end of the continuum, detri- ing processes to consider what others are thinking and feeling, ments in social sensitivity are apparent in individuals along the as well as flexibly adjusting behaviors based on social feedback autism spectrum, who experience psychosocial difficulties be- (Nelson and Guyer, 2011; Somerville, 2013). Within the broader cause of impairments in social cognition (Lai, Lombardo and domain of social cognition, social sensitivity can be defined as a Baron-Cohen, 2014). Individuals along the autism spectrum shifting motivation that intensifies the attention, salience, and tend to show reduced sensitivity to social cues, which is modu- emotion implicated in processing social cues (Somerville, 2013). lated by diminished activity in social cognition and affective Significant developmental changes in the adolescent brain may brain regions (for a meta-analysis, see Philip et al., 2012). Taken partly underlie this shifting motivation in social sensitivity. together, empirical and theoretical work suggests a curvilinear A collection of brain regions, including basic affective regions, relationship between social sensitivity and adaptive decision- social brain regions and cognitive control regions likely work in making—either too little or too much social sensitivity may be concert in the process of social sensitivity. In regards to affective maladaptive, whereas a moderate level of social sensitivity may regions, a key aspect of social sensitivity is processing social cues be associated with adaptive outcomes. While prior work sug- from facial expressions. Recruitment of the amygdala, anterior in- gests impairments in social sensitivity at both the extremely sula (AI), and superior temporal sulcus allow individuals to recog- high end (e.g. socially anxious) or extremely low end (e.g. aut- nize emotions in others (Cohen Kadosh et al., 2013b;Fuhrmann ism spectrum), we do not currently know how variability in so- et al., 2016). Indeed, neural processing of facial expressions de- cial sensitivity within a normative adolescent sample is linked velops throughout adolescence, with sensitivity to social feedback to adaptive outcomes in a risky context. peaking in this network during adolescence (Guyer et al., 2009, 2012; Jones et al., 2014). A higher-order aspect of social sensitivity is understanding and acting on more complex social emotions. Present study That is, emotions that require the representation of other people’s The goal of the present fMRI study was to test the hypothesized mental states, or mentalizing (Burnett et al., 2009). Activation in curvilinear relationship between social sensitivity on a behav- the social brain network required for mentalizing, including the ioral and neural level and adaptive decision-making in adoles- medial prefrontal cortex (mPFC) and temporoparietal junction cence. We were specifically interested in individual differences (TPJ), is greater in adolescence than children or adults when evalu- in social sensitivity in the context of risky decision-making and ating social emotions (Burnett et al., 2009; Somerville, 2013). employed the novel Social Analogue Risk Task (SART), a social Finally, this network of affective and social brain regions works to- adaptation of the well-validated Balloon Analogue Risk Task gether with the cognitive-regulatory network (e.g. lateral pre- (Lejuez et al., 2002; Humphreys et al., 2016). The SART is a ‘trick- frontal cortex, lPFC) to support the execution of goal-directed and or-treat game’ that measures participants’ willingness to knock flexible social behaviors, for example after social feedback (Nelson on doors in order to earn points. With each knock on a door, and Guyer, 2011; Casey, 2015). In sum, social sensitivity is sup- points increase while the facial expression of the house’s resi- ported by a network of regions including affective processing, so- dent morphs from happy to somewhat angrier. Knocking is cial cognition and cognitive control. associated with an increasing risk, because if the resident gets too angry and slams the door, all points for that door are lost. A curvilinear relationship between social sensitivity and Crucially, some residents are faster to get angry than others, adaptive outcomes and so adolescents need to flexibly adapt their risk-taking be- A recent neurobiological susceptibility model posits that adoles- havior (i.e. number of knocks on each door) in the context of cent development is shaped by brain-based individual each new resident in order to collect the most points. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 548 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Social sensitivity was defined as the extent to which adoles- form is a well validated and widely-used task to assess risk- cents adjusted their risk-taking behavior based on information taking behaviors across development (Lejuez et al., 2002; about the anger level of the house’s resident. As such, social McCormick and Telzer, 2017). Risk taking on the BART is corre- sensitivity in the SART likely taps into social cues from facial lated with real-life risk taking in adolescents, both concurrently expressions, as well as mentalizing about the thoughts and feel- (Telzer et al., 2015) and longitudinally (Qu et al., 2015). Similar to ings of the resident. That is, greater social sensitivity would en- the BART, the SART involves sequential risk-taking in pursuit of tail knocking less on doors of residents who changed from points. The novel aspect of the SART is a social component, happy to angry relatively fast, and knocking more on doors of which makes it more comparable to real-world adolescent risk- residents who were slow to get angry. We hypothesized that taking behavior that tends to occur in an interactive social en- relatively moderate social sensitivity (i.e. normative exploration vironment. This social environment was created by displaying and cashing-out right before the resident gets too angry and dynamically changing facial expressions in response to partici- slams the door) would be related to greater self-reported adap- pants’ risky decisions. The current version of the SART was tive decision-making. Low or high levels of social sensitivity, based off of previous behavioral studies (e.g. Humphreys et al., relative to moderate levels, are likely related to lower self- 2016), but was modified in several ways as outlined in more de- reported adaptive decision-making. tail below. At the neural level, we expected that moderate social sensi- The task was explained as a ‘trick-or-treat’ game, in which tivity would be related to enhanced tracking of the changes in participants were presented with a series of 24 people at their emotional expression in affective, social cognition and cognitive houses. Participants could knock on the door at each house to control regions during risky decision-making, as prior work sug- earn points for every knock (i.e. more knocks resulted in more gests that these brain regions are involved in attuning to social points). They were instructed to earn as many points as pos- cues and flexible social behavior (Nelson and Guyer, 2011; sible, which could be cashed in for prizes after the study. All Somerville, 2013; Rosen et al., 2017). We expected to find a curvi- residents initially started out with a happy facial expression linear effect in these regions, such that compared to moderate and grew increasingly angry with each successive knock, and levels of social sensitivity, low and high levels of social sensitiv- would eventually slam the door, at which point the participant ity would result in less neural tracking of the changes in emo- would lose all points earned for that respective door. tional expression in these regions. Finally, we predicted that Alternatively, participants had the option to cash out the points enhanced neural tracking in these regions would be linked to they earned for that respective door at any point. During train- greater self-reported adaptive decision-making. ing, participants were shown what these two options (i.e. knocking and cashing out) and their outcomes would look like. Each knock decision was accompanied by a knocking sound, Materials and methods slam events were accompanied by a loud slamming sound, and cash-out decisions were accompanied by a short note that indi- Participants and procedure cated point receipt. A running total of points earned was pre- This study included 35 healthy adolescents between 12 and sented on a points meter throughout the task (Figure 1A). 16 years (M ¼ 15.28 years, s.d. ¼ 1.34; 61% female). One add- Age The door always slammed at a 50% angry facial expression, itional participant was excluded due to excessive movement which was not explicitly explained to participants. Yet, some (> 2 mm movement between slices on>10% of slices). Within residents were slower to get angry than others. Thus, although the sample, 74% identified as European-American, 14% as all faces morphed along the same continuum from 100% happy mixed ethnicity, 6% as African-American, 3% as Latin-American to 100% neutral to 50% angry, the threshold for slamming the and 3% as Asian-American. Participants took part in a larger door varied between 3 and 10 knocks (Figure 1B for an illustra- cross-sectional fMRI study about the development of decision- tion). Therefore, some residents morphed into 50% anger after 3 making. Given that previous work using SART has shown devel- knocks, whereas others morphed more slowly and became 50% opmental changes (McCormick et al., 2018), and adolescence is angry after 10 knocks. Hence, participants could use the socioaf- uniquely characterized as a phase of social reorientation, we se- fective information in the faces to guide their decisions to deter- lected the adolescent sample a priori. All adolescent partici- mine whether they should keep knocking or cash out points. pants that had SART task data as well as Flinders questionnaire Table 1 shows descriptives concerning number of cash-out deci- data (see below) were included in this study. sions, slams as well as average and range of knocks on the task. Participants were recruited through a participant database, The task consisted of one run with 24 self-paced trials word of mouth and flyers. We screened all participants to make (Table 1 lists display of reaction times). The run lengths ranged sure that they were free of psychiatric disorders, neurological between 6.49 and 12.65 min, with a median of 9.28 min. Note disorders and MRI contraindications. A training session was that the run length mostly affects the data for the knocks condi- completed outside the scanner to train participants on how to tion, because participants with shorter runs knock fewer times perform the scan task correctly. The actual scan session lasted than those with longer runs. Trials and each consecutive facial 1.5 h. Participants received a $50 endowment and selected expression (i.e. angrier expression following a decision to knock; prizes from a prize box based on the points they earned on the new face after decision to cash-out; new face following a slam tasks during the scan. Participants and their parents provided trial) were separated with a random jitter (500 –4000 ms). The written informed consent and assent prior to the start of the faces were drawn from the NimStim face database and modeled study. All procedures were approved by the Institutional Review off of a study by Humphreys and colleagues (Tottenham et al., Board of the University of Illinois. 2009; Humphreys et al., 2016). Participants saw 12 individual faces (4 European-American, 4 African-American and 4 Asian- American; all faces were female) twice during the task. For each Measures participant, faces were presented in the same order and with fixed but previously randomly determined slam thresholds. As Social analogue risk task. Participants played an adapted version of the Balloon Analogue Risk Task (BART), which in its original such, the current fMRI task was adapted from previous work by Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 549 Fig. 1. (A) Illustration of the SART. The cash-out decisions, highlighted in the red square, were the focus of the current analyses. Each decision was self-paced and there was a jitter (500–4000 ms) between each event. Participants played a ‘Trick-or-Treat’ game during which they knocked on doors in order to earn points. With each knock, the facial expression of the house’s resident morphed from happy to somewhat angrier. At 50% angry, the resident slammed the door and all points for that door were lost. Participants could also cash out at any moment. (B) Example of variable anger increments between residents. The upper resident slams the door after four knocks, whereas the lower resident is slower to anger and takes seven knocks to slam the door. individual trial. Finally, the timing was also adjusted such that Table 1. Descriptives for SART task behavior the task could be administered in the MRI scanner. Task parameters Mean s.d. Range Social sensitivity. Social sensitivity during the SART was opera- Number of cash-out decisions 21.89 1.66 17–24 tionalized as the extent to which individuals adjusted their Number of door slams 2.11 1.66 0–7 number of knocks on the current trial based on information Average # of knocks on cash-out trials 4.81 1.55 3.19–6.5 about the anger level of the house’s resident. More specifically, Average reaction time (s) all trial types 1.18 0.21 0.75–1.73 social sensitivity was operationalized as knocking more on houses in which the resident morphed to 50% angry more Note: Slams and cash-out decisions are opposite to each other and add up to 24 slowly and knocking less on houses in which the resident trials in total. morphed to 50% angry more quickly. We employed hierarchical including more faces, each of which was shown twice, as well linear modeling (HLM; Raudenbush and Bryk, 2002) to obtain as having variable probabilities of door slamming for each this social sensitivity index. We modeled 24 nested trials for Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 550 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 each participant, with number of knocks as our outcome vari- 2000, p. 280), the Vigilant scale is measured with three items and able, and the anger level of the face as the predictor. The level 1 the Self-confident decision-making scale with five items. Since equation was as following: we were interested in overall adaptive decision-making, we aver- aged these subscales to have a more potent measure of adaptive Number of Knocks ¼ b þ b ðAnger Þþ b ðSlam Þ decision-making with eight items total (a ¼ 0.79). A maladaptive ij 0j 1j 2j ðN1Þ ðNÞ pattern of decision-making is indexed by 14 items with the sub- þ b ðSlam Þþ b ðTrial NumberÞþ e ðNÞ 3j 4j ij scales Panic, Evasiveness and Complacency. Reflecting the original measure, the responses are coded on a 4-point scale ranging Total knocks on a particular trial (i)for aparticular adolescent(j) from ‘Never’ to ‘Always’ and the total score is calculated as the was modeled as a function of the average number of knocks across average of the respective items. Higher scores are indicative of the task (b ) and the anger level of the face on the current trial 0j more adaptive decision-making, whereas lower scores are indica- b (Anger ) (i.e. # of knocks required to get to 50% anger). We also 1j (N) tive of less adaptive decision-making. This scale has been used to included three controls. The main control variable was whether the link neural processing to adaptive decision making in adolescents previous trial (b ) was a cash-out or slam [coded Cash-Out ¼ 0; 2j (N1) (e.g. Telzer et al., 2013). Slam ¼ 1], which represents non-social feedback learning. This (N1) variable allowed us to test how likely participants were to use infor- mation from the previous trial to guide their knocks on the next fMRI data acquisition trial, adjusting their behavior when their previous decision resulted Imaging data were obtained with a 3-T Siemens Trio MRI scan- in maladaptive outcomes. We included this variable as a control to ner, using a 12-channel head coil. The task consisted of one make sure that our measure actually reflected social sensitivity, and self-paced run. Functional data were collected using T2*- not general feedback learning (see McCormick and Telzer, 2017 for weighted echoplanar images (EPI) (slice thickness ¼ 3 mm; 38 a feedback learning approach). Two additional controls included slices; TR ¼ 2s; TE ¼ 25 ms; matrix ¼ 9292; FOV ¼ 230 mm; voxel were whether the current trial resulted in a cash-out or a slam [b ; 3j size 2.52.53mm ). To provide an anatomical reference, coded Cash-Out ¼ 0; Slam ¼ 1] and the trial number (b ), (N1) (N1) 4j structural scans were obtained, including a T2*weighted, whichare controls oftenusedinmodelingofBARTbehavior (e.g., matched-bandwidth (MBW; TR ¼ 4s; TE ¼ 64 ms; FOV ¼ 230; ma- McCormick and Telzer, 2017). Trial number controls for cumulative trix ¼ 192192; slice thickness ¼ 3 mm; 38 slices) and a T1* learning over time, and this parameter did not predict the number magnetization-prepared rapid-acquisition gradient echo of knocks/risk-taking (P¼ 0.680; see Supplementary for an overview (MPRAGE; TR ¼ 1.9 s; TE ¼ 2.3 ms; FOV ¼ 230; matrix ¼ 256256; of theentire HLM model).Notethatsocial sensitivity effects were sagittal plane; slice thickness ¼ 1 mm; 192 slices). The MBW and similar when we controlled for gender in our HLM level 2 equation. EPI scans were acquired with an oblique axial orientation. Therefore, we did not control for gender in further behavioral and neural analyses. To use the index of social sensitivity (variable of interest) fMRI data preprocessing and analysis and non-social feedback learning (control) in our analyses, We used the SPM8 software package (Wellcome Department of Empirical Bayes estimates were extracted for each participant. Cognitive Neurology, UK) for preprocessing and data analysis. These estimates represent optimally weighted averages that are Preprocessing involved correction for head motion with spatial re- computed through a combination of estimates on an individual alignment, co-registration to a high-resolution T1* MPRAGE struc- and group level, and shrinks the individual’s estimates towards tural scan, and segmentation into grey matter, white matter and the overall mean (Diez-Roux, 2002; McCormick and Telzer, cerebrospinal fluid. For one participant, the co-registration was 2017). The extracted estimate provides an individual difference conducted with the MBW scan because the T1* was missing. We measure indicating whether participants changed their behav- applied the resulting transformation matrices to the MBW and EPI ior as a function of anger level. Values larger than 0 indicate images in order to warp them into the standard stereotactic space that participants adjusted their risk-taking behavior based on as defined by Montreal Neurological Institute (MNI). EPI images social sensitivity (i.e. knocked more on houses where the face were spatially smoothed with an 8-mm FWHM isotropic Gaussian morphed more slowly and knocked less on houses where the kernel. The fMRI time series for each trial were convolved with the face morphed more quickly), whereas values around 0 indicate hemodynamic response function. To remove low-frequency scan- little or no adjustment based on social sensitivity. ner drift across time we applied a 128 s high-pass filter, and esti- mated serial autocorrelations using a restricted maximum Adaptive decision-making. The concept of social sensitivity likelihood algorithm with an autoregressive model order of 1. applies to general decision-making strategies in real-life as well We conducted statistical analyses on individual subjects’ as more specifically to social situations, as most decisions are data using the general linear model in SPM8. In the fixed-effects made in a social context or with input from social others, espe- model, knock decisions, cash-out decisions and slam events cially during adolescence when individuals are especially attuned were modeled as separate events of interest. The jitter between to the social context (Albert et al.,2013; Blakemore and Mills, 2014; events was not modeled and utilized as an implicit baseline. Scriber and Guyer, 2017). The Flinders Adolescent Decision The trials were modeled from the onset of the trial to when par- Making scale assesses adolescent decision-making patterns and ticipants made their decision using the reaction time, and as distinguishes between adaptive and maladaptive decision- such represent the decision-making phase. A parametric modu- making (Mann et al., 1989; Tuinstra et al., 2000). Adaptive lator (PM) was included to model increasing risk across knock decision-making encompasses the subscales Vigilant (precision decisions. The cash-out decision, which we focus on in the cur- and deliberation in making decisions; e.g. ‘I like to think about rent manuscript, represents neural activity at the moment par- my decision before I make it’) and Self-confident decision-making ticipants decided to cash out when the face was ‘too’ angry, and (confidence and efficacy when making decisions; e.g. ‘The deci- thus, when social sensitivity likely affects decisions. PM values sions I make turn out well’), representing careful and deliberated signified the number of knocks for the entire trial (i.e. the total decision-making. Within the revised 22-item scale (Tuinstra et al. risk that is taken for each house), and were centered within a Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 551 person around the average number of knocks for each door. We focus on the cash-out PM condition rather than the knocks condition, such that we were able to examine neural tracking of increasingly angry faces at the moment when participants decided to cash out. The resulting contrast images, computed at the individual level, were submitted to random-effects group- level analyses. At the group level, analyses were conducted using GLMFlex, which removes outliers and sudden activation changes in the brain, partitions error terms, analyzes all voxels containing data, and corrects for variance-covariance inequality (http://mrtools.mgh.harvard.edu/index.php/GLM_Flex). We first looked at the main effect and then performed whole-brain regression analyses on the cash-out PM condition, in which social sensitivity was entered as a quadratic term, given our hypothesis that a moderate amount of social sensitiv- ity would be most beneficial. In this analysis, we controlled for social sensitivity as a linear predictor as well as the index for nonsocial feedback-learning. Given the use of GLM-Flex, we cor- rected for multiple comparisons using a Monte Carlo simulation through 3dclustsim (updated version November 2016) in the Fig. 2. Quadratic relationship between social sensitivity and self-reported adap- software package AFNI (Ward, 2000), and computed the smooth- tive decision-making, as indicated by the Flinders adolescent decision-making ness of the data with the acf function within the 3dFWHMx scale. command. For the main effect, the simulation resulted in a voxel-wise threshold of P< 0.005 and minimum cluster size of 79 voxels for the whole-brain, which corresponds to P< 0.05, baseline model, added social sensitivity in model 2, and non- FWE cluster-corrected. For the regression, the simulation re- social feedback learning in model 3. Kolmogorov–Smirnov tests sulted in a voxel-wise threshold of P< 0.005 and minimum clus- of normality showed that all of these variables were normally ter size of 107 voxels for the whole-brain. distributed (P’s> 0.05). Model 2 (social sensitivity controlling for social sensitivity); and model 3 (social sensitivity control- ling for social sensitivity and non-social feedback learning) Results were significant (Ps< 0.05), but only model 2 predicted signifi- cantly more variance than the baseline model (r ¼ 0.209; change Behavioral results P ¼ 0.006). As such, model 2 was the best fit to the data, explain- ing 20% of the variance [(F(2, 34) ¼ 5.249, P ¼ 0.011]. As expected, First, we employed HLM to estimate how adolescents adjusted there was a curvilinear relationship between social sensitivity their decision-making based on social sensitivity to shifting fa- and adaptive decision making, such that moderate levels of so- cial expressions, as well as non-social feedback learning on the cial sensitivity were associated with more adaptive decision task. As expected, adolescents showed significant social sensi- making, whereas low and high levels of social sensitivity were tivity on average, as indexed by a strong association between associated with lower adaptive decision making [b¼4.392, number of knocks and anger level (b ¼ 0.545, SE ¼ 0.041, SE ¼ 1.475, b¼2.232, P ¼ .006; Figure 2). Non-social feedback P< 0.001). Thus, adolescents knocked more when residents’ fa- learning in model 3 did not contribute to the prediction of adap- cial expressions were slower to change from happy to angry, tive decision-making (b ¼ 0.897, SE ¼ 0.742, b ¼ 0.286, P ¼ 0.236). and knocked less when residents’ facial expressions were faster Together, these findings underscore the important adaptive to change from happy to angry. Moreover, adolescents also role of moderate social sensitivity for adolescent decision- showed significant non-social feedback learning, as indexed by making processes. knocking less if the previous trial was a slam (b ¼0.587, SE ¼ 0.118, P< .001). As such, they learned to adjust their behav- ior when this previously resulted in a maladaptive slam out- fMRI results come. The complete set of results of the first level model can be Neural tracking of cash-out decisions. We first examined the main found in Supplementary Table S1. Next, we extracted the Empirical Bayes estimates for social sensitivity and non-social effect of the cash-out decisions, during which participants decided to cash out points before the door was slammed. feedback learning for each participant. There was considerable variability in the Empirical Bayes estimate of social sensitivity, Results showed that the left precuneus and occipital gyrus tracked increasingly angry faces with corresponding increased M ¼ 0.545, s.d.¼ 0.224, range 0.129–0.909. Whereas some partici- pants showed relatively low social sensitivity (i.e. estimates risk when participants decided to cash out (Table 2). In addition, the right TPJ was recruited, but this region did not survive cor- closer to 0), other adolescents showed relatively high social sen- sitivity (i.e. estimates closer to 1). Non-social feedback learning rection. As such, these findings show that social brain regions and visual regions are recruited more as participants knock also showed individual variability (M¼0.587, s.d. ¼ 149), range 0.842 to 0.183. Some participants showed a larger decrease in more and see increasingly angry faces on trials where they de- cide to cash out. the number of knocks after the previous outcome was a slam (i.e. estimates closer to 1), while others showed a small de- crease in knocks after a slam (i.e. estimates closer to 0). Links between social sensitivity and neural tracking during cash-out Next, we ran a regression model to examine whether social decisions. In our primary analyses, we conducted a whole-brain sensitivity showed curvilinear relations with adaptive decision regression analysis, in which social sensitivity was entered as a making. We entered linear social sensitivity in model 1 as a quadratic predictor, and linear social sensitivity and non-social Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 552 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Table 2. Brain regions that displayed a main effect for the cash-out level. In order to do so, the novel SART, a ‘trick-or-treat’ game, PM contrast was employed in a sample of 12–16-year-old adolescents. Behavioral findings revealed a curvilinear relationship between Region label Volume t-Value MNI coordinates social sensitivity and adaptive real-world decision-making. (mm ) During decisions to cash-out, activity in the TPJ, insula and xy z dlPFC displayed a curvilinear relationship with social sensitiv- L Superior occipital gyrus 88 4.058 21 79 40 ity, indicating that these brain areas tracked shifting facial ex- L Precuneus 88 3.659 9 73 55 pressions the most at a moderate level of social sensitivity, but R Superior temporal 66 3.974 60 37 13 less so at low or high levels of social sensitivity. Finally, brain gyrus (TPJ) activity in each of these regions was associated with greater lev- els of adaptive real-world decision-making, with the TPJ show- Note: Analysis for negative relationships showed no significant clusters of ing the strongest effect, and insula and dlPFC at the trend level activation. P<0.05, FWE cluster-corrected. a after correcting for multiple comparisons. Findings suggest that Does not survive FWE-cluster correction. moderate social sensitivity is related to adaptive decision- making in a risky context, and this is modulated via heightened tracking of social cues in the TPJ, insula and dlPFC. feedback learning were included as covariates. Results indicated a quadratic relationship for several regions implicated in social Moderate social sensitivity and adaptive cognition and cognitive control, including the left TPJ, right in- decision-making sula and right dorsolateral prefrontal cortex (dlPFC), demon- strating that these brain regions track increasing anger the Adolescence is a developmental period marked by a major so- most at moderate levels of social sensitivity, but track less so at cial re-orientation, with changes in social as well as health-risk either low or high levels of social sensitivity (Figure 3; Table 3). behaviors (Ellis et al., 2012; Nelson et al., 2016). Elevated adoles- Next, we ran a whole-brain regression analysis in which feed- cent social-affective sensitivity has traditionally been linked to back learning was entered as a predictor, controlling for social increases in excessive health-risk behaviors. More recently sensitivity and social sensitivity, to examine whether this ef- however, research has started to highlight the more adaptive fect was unique to social sensitivity. This analysis yielded no properties of normative exploration and risk-taking, as well as significant results, demonstrating that these neural effects are social sensitivity (Crone and Dahl, 2012). There is a large and indeed unique to social sensitivity. meaningful within-group variability in adolescent risk taking compared to childhood or adulthood (Van Duijvenvoorde et al., Neural activity and adaptive decision-making. Finally, we ex- 2016), which further illustrates the need to characterize norma- tracted the parameter estimates (averaged over the whole clus- tive risk taking vs excessive risk taking. For example, explor- ter) from the left TPJ, right insula and right dlPFC which showed ation facilitates learning and understanding of the (social) significant quadratic relations with social sensitivity and con- environment, with greater experience relating to more available ducted a series of regressions to predict self-reported adaptive information, which is helpful in consequent decision-making decision-making. For the neural activation and self-reported (Goldenberg et al., 2017; McCormick and Telzer, 2017; Somerville adaptive decision making, high scores on each are predicted to et al., 2017). Our behavioral findings resonate with and extend be adaptive (i.e. moderate social sensitivity related to high previous work by revealing that those with moderate social sen- adaptive decision making and high neural activation). sitivity to shifting facial expressions, relative to low or high so- Therefore, taking out social sensitivity in the equation here, we cial sensitivity, showed greater adaptive decision-making. expected high neural activity to be related to high adaptive Taken together, the present findings provide evidence that decision-making, which should result in a linear relationship moderate adolescent social-affective sensitivity may confer ad- rather than a quadratic relationship. Separate regressions were vantages in a risky context. run with a Bonferroni correction for multiple comparisons Theoretical and empirical work shows evidence for intensi- (0.05/3 ¼ 0.017), for each brain region. The regression model for fied processing of social cues from the environment during ado- the TPJ was significant [F(1, 33) ¼ 10.79; R ¼ 0.224, P ¼ 0.002], adj lescence (Somerville, 2013). Sensitivity to social cues, and in such that increased tracking in the TPJ predicted greater adap- particular to changing emotional expressions, is key in navigat- tive decision-making (b ¼ 2.729, SE ¼ 0.235, b ¼ 0.496; P ¼ 0.002). ing the social world (Neta and Whalen, 2011). Greater social sen- The regression for dlPFC [F(1, 33)¼ 5.90; R ¼ .126, P ¼ 0.021; adj sitivity to facial expressions has been linked to less social b ¼ 0.428, SE ¼ 0.176, b ¼ 0.389, P ¼ 0.021] and for insula anxiety and fewer social problems (Rosen et al., 2017). In this [F(1, 33) ¼ 4.271; R ¼ 0.088, P ¼ 0.047; b ¼ 0.319, SE ¼ 0.154, adj study, we assessed social sensitivity based on task behavior, as b ¼ 0.339; P ¼ 0.047] revealed similar patterns at the trend level, the extent to which adolescents adjusted their risky decision- when we accounted for multiple comparisons (Figure 3 shows a making based on how quickly the facial expression changed display of these analyses; visualization purposes only). Taken from happy to angry. This enabled us to look at individual dif- together, these findings demonstrate that more neural tracking ferences in social sensitivity to facial expressions, and for the in these regions associated with social cognition and cognitive first time, how this affects decision-making in a risky context. control is associated with higher levels of adaptive decision- Our findings supported the hypothesis that being moderately making. sensitive to changing facial expressions facilitates goal-directed and adaptive risky decisions. Given that the adolescent period encompasses the time when individuals reach mature social Discussion goals (Cohen et al., 2016), these findings intuitively make sense. This study tested whether moderate levels of social sensitivity That is, being in a social vacuum (low social sensitivity) or being to changing facial expressions was associated with adaptive exceptionally attuned to one’s social environment (high social decision-making in a risky context, on a behavioral and neural sensitivity) limits successful interactions with others Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 553 Fig. 3. Quadratic relationship between social sensitivity and tracking of increasing anger in left TPJ (MNI 60 40 40), right dlPFC (MNI 36 47 25) and right insula (MNI 45 1 7) during decisions to cash-out (P<0.05, FWE-cluster corrected; for visualization purposes only). Table 3. Brain regions that displayed a quadratic relationship with social sensitivity when adolescents chose to cash out, controlling for nonso- cial feedback learning and the linear predictor for social sensitivity Region label Volume (mm ) t-Value MNI coordinates xy z R Insula 401 5.264 45 17 R Superior temporal gyrus 401 3.952 69 19 1 L Cerebellum 354 5.328 12 52 26 L Supramarginal gyrus (TPJ) 157 5.280 60 40 40 R Superior frontal gyrus (premotor) 122 4.767 21 164 R Middle frontal gyrus (dlPFC) 108 4.758 36 47 25 R Cerebellum 129 4.594 15 49 32 Note: Analysis for positive relationships showed no significant clusters of activation. P<0.05, FWE cluster-corrected. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 554 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 (Nelson and Guyer, 2011; Blakemore and Mills, 2014). Moderate feedback) (Guyer et al., 2009; Lamm and Singer, 2010). As such, social sensitivity on the other hand likely enables adolescents the insula plays a critical role in guiding decision-making and to successfully interact with a wide range of social agents, has particular importance for risk-taking (Smith et al., 2014). In a including parents, teachers, and peers. Ultimately, successful similar task design, adolescents showed greater insula activa- social relationships may pave the way to reaching mature social tion when taking risks in a social feedback relative to non-social goals in adulthood. feedback condition (Op de Macks et al., 2017). The present find- ings fit with previous work and suggest that for individuals with moderate levels of social sensitivity, shifting emotional expres- Neural correlates of moderate social sensitivity and sions were most salient, eliciting increased allocation of atten- adaptive decision-making tional resources that may facilitate social learning from the facial expression. Perhaps for adolescents on both the low and Changes in social and health-risk behaviors during adolescence high end of the social sensitivity continuum, facial expressions are paralleled by dynamic restructuring of the neural architec- are not as salient, or potentially hypersalient, which hinders the ture of affective, social and cognitive control networks (Crone social learning process from facial expression and is detrimen- and Dahl, 2012; Blakemore and Mills, 2014). A recent neurobio- tal to adaptive decision-making in a social context. logical susceptibility model poses that adolescent development Imbalance models of neurocognitive development denote is shaped by brain-based individual differences in sensitivity to the implication of lateral regions of the PFC in cognitive control social context (Scriber and Guyer, 2017). In line with this model, and inhibition (Somerville et al., 2010; Casey, 2015; Shulman the current study examined individual differences in adolescent et al., 2016). In a social-affective context, the lPFC is linked to neural tracking related to social sensitivity. During decisions to flexible social behaviors and recruited together with face pro- cash-out, adolescents with moderate levels of social sensitivity cessing regions when more cognitive processes are layered onto showed increased neural tracking of shifting facial expressions passively viewing facial expressions, like labeling expressions from happy to angry with increasing risk in the TPJ, insula and (Neta and Whalen, 2011; Flannery et al., 2017). In such dlPFC. These brain regions showed less tracking at either low or cognitively-taxing affective tasks, the dlPFC facilitates working high levels of social sensitivity. Together, regions associated memory to guide goal-directed behaviors and ultimately meet with affective, social cognition, and cognitive control processing task demands (Neta and Whalen, 2011). Thus, increased track- showed differential tracking based on individual differences in ing of the dlPFC with moderate social sensitivity likely reflects social sensitivity. increased cognitive control, guiding the decision to stop knock- The TPJ is an integral part of the so-called social brain net- ing on the door and instead cash-out when the facial expression work which is implicated in social cognition and mentalizing becomes too angry. That is, the dlPFC plays a role in the ability (Blakemore and Mills, 2014). In particular, TPJ activation has to flexibly switch risk-taking behaviors in light of the social cues been linked to perspective-taking and understanding more from the social environment. On the other hand, adolescents on complex emotions, as well as more general attention processes the low and high end of social sensitivity show less tracking in in the social domain (Burnett et al., 2009; Van den Bos et al., this area implicated in cognitive control, which is associated 2011). Although TPJ activation did not survive stringent correc- with less adaptive decision-making. tion in the main effect when we solely examined neural track- ing during decisions to cash-out independent of social sensitivity, it is an interesting finding given that the non-social Future directions and conclusions BART does not elicit any TPJ activity for a similar contrast (McCormick and Telzer, 2017). However, the effect with the A few limitations should be noted. First, this study only incorpo- curvilinear relation to social sensitivity during decisions to rated dynamically changing female adult faces, because the cash-out did survive stringent correction. One interpretation for task was based on previous work (Humphreys et al., 2016). TPJ activity may be perspective-taking, i.e. trying to understand Future research should replicate and extend the current find- the resident’s thoughts and feelings. Alternatively, TPJ activa- ings with male faces, as well as different emotional expressions tion may represent a more general attention process of being (e.g. happy shifting to sad). Another interesting direction for fu- attuned to the changing emotions displayed by the resident of ture research would be to examine the relation between social the house. As such, relatively moderate social sensitivity may sensitivity and neural responses to peer faces, for example be facilitated by social attention or perspective-taking processes using the recently validated duckEES dynamic facial expres- that guides adaptive decision-making. Because our analyses uti- sions dataset (Giuliani et al., 2017). Given the value placed on lized a PM, the neural effects represent linear increases in the peer relationships and the increase in risk-taking when adoles- neural tracking of emotional expressions in the faces rather cents are with their friends (Chein et al., 2011), the peer context than mean level activation. Thus, those with low and high so- represents an important and salient context to further investi- cial sensitivity each showed low TPJ tracking of increasing gate social sensitivity. Previous studies that directly compared anger, suggesting that these adolescents are less sensitive to adolescent neural responses to peer and adult faces have the changing social cues in the facial expressions, which is shown mostly overlapping brain regions, except for enhanced associated with less adaptive decision-making. amygdala response to positive peer faces and angry adult faces The insula is considered a ‘hub’ between cognitive control (Marusak et al., 2013; Flannery et al., 2017). and affective brain networks and has been linked to a wide The SART does not allow us to disentangle the neural re- range of functions across different contexts. Indeed, the insula sponse to increasing risk and shifting emotional expressions as is implicated in salience detection and attentional resource for they increase at the same time. Although we used a PM to spe- goal-directed behavior (Menon and Uddin, 2010; Smith et al., cifically examine neural tracking with increasing risk, which in 2014). In the affective literature, insula activity has been re- itself controls for the social and non-social aspects of the task, ported for basic face processing, perceiving emotional states of future research is needed to disentangle and study these two the self and others and integrating this internal information processes independently. It will also be important to extend the with external cues from the social environment (i.e. social current findings with an additional non-social control Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 555 Cohen, A.O., Breiner, K., Steinberg, L., et al. (2016). When is an condition, for example using shapes that change color signaling the rate of transition instead of faces, or balloons in which the adolescent an adult? Assessing cognitive control in emotional size indicates the explosion threshold, unlike the traditional and nonemotional contexts. Psychological Science, 27(4), 549–62. BART in which the balloon can explode at any time with no cue Cohen Kadosh, K., Johnson, M.H., Dick, F., Cohen Kadosh, R., indicating the threshold. In addition, while self-report of Blakemore, S.-J. (2013a). Effects of age, task performance, and decision-making strategies gets at adolescents’ own perception structural brain development on face processing. Cerebral Cortex, 23, 1630–42. of their decisions, this method is therefore also limited. Future studies can employ different methods to get at other aspects of Cohen Kadosh, K., Johnson, M.H., Henson, R.N.A., Dick, F., this decision-making concept, such as using observational Blakemore, S.-J. (2013b). 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Salience Available: https://afni.nimh.nih.gov/pub/dist/doc/manual/Alp network response to changes in emotional expressions of haSim.pdf (17 November 2017, last accessed date). Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Cognitive and Affective Neuroscience Oxford University Press

Moderate social sensitivity in a risky context supports adaptive decision making in adolescence: evidence from brain and behavior

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Abstract

Adolescence is a time of increased social-affective sensitivity, which is often related to heightened health-risk behaviors. However, moderate levels of social sensitivity, relative to either low (social vacuum) or high levels (exceptionally attuned), may confer benefits as it facilitates effective navigation of the social world. The present fMRI study tested a curvilinear rela- tionship between social sensitivity and adaptive decision-making. Participants (ages 12–16; N¼ 35) played the Social Analogue Risk Task, which measures participants’ willingness to knock on doors in order to earn points. With each knock, the facial expression of the house’s resident shifted from happy to somewhat angrier. If the resident became too angry, the door slammed and participants lost points. Social sensitivity was defined as the extent to which adolescents adjusted their risky choices based on shifting facial expressions. Results confirmed a curvilinear relationship between social sensitivity and self-reported adaptive decision-making at the behavioral and neural level. Moderate adolescent social sensitivity was modulated via heightened tracking of social cues in the temporoparietal junction, insula and dorsolateral prefrontal cortex and related to adaptive decision-making. These findings suggest that social-affective sensitivity may positively impact out- comes in adolescence and have implications for interventions to help adolescents reach mature social goals into adulthood. Key words: adolescence; social sensitivity; risk taking; facial expression; adaptive decision-making Introduction social world. Indeed, adolescents show a uniquely heightened sensitivity to social experiences, and moderate levels of adoles- Adolescence, the developmental period between childhood and adulthood, is characterized by heightened social-affective sen- cent social sensitivity may be considered adaptive in order to meet this social developmental milestone. However, being in a sitivity (Crone and Dahl, 2012; Blakemore and Mills, 2014). An important developmental milestone during this time is to learn social vacuum (low social sensitivity) or being exceptionally attuned to one’s social environment (high social sensitivity) to effectively navigate the social world (Blakemore and Mills, 2014). There are tremendous maturational changes in how the likely limits an individual’s ability to successfully interact with others (Nelson and Guyer, 2011; Blakemore and Mills, 2014). brain codes and generates responses to social information throughout adolescence (Nelson et al., 2005; Nelson and Guyer, Hence, this study tests whether moderate levels, as compared to low or high levels of social sensitivity, confer benefits in the 2011; Nelson et al., 2016). These changes in the brain equip ado- lescents with the tools to navigate the increasingly complex context of risky decision-making. Received: 17 July 2017; Revised: 9 February 2018; Accepted: 22 February 2018 V C The Author(s) (2018). Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 547 Social sensitivity and the developing brain differences in sensitivity to social context (Scriber and Guyer, 2017). In particular, the degree to which adolescents are tuned Adolescence is a period of changing social dynamics, with an to their environment may be calibrated through individual dif- increased saliency of social acceptance and rejection, changing ferences in structural and functional neural characteristics. social relationships with parents and peers, and a tendency to Moderate levels of social sensitivity and recruitment of social- explore societal boundaries in many different ways (Crone and affective neurocircuitry are likely related to adaptive outcomes Dahl, 2012; Nelson et al., 2016). Adolescents begin to make in- during adolescence, because this is crucial for competently creasingly independent decisions about how to navigate the interacting with others and flexible social behavior, as well as complex social world based on limited experiences (Ellis et al., normative exploration and risk taking (Nelson and Guyer, 2011; 2012; McLaughlin et al., 2015). Indeed, strategic exploration (i.e. Scriber and Guyer, 2017). However, too much or too little social making choices that provide new experiences and information) sensitivity may be related to maladaptive psychosocial out- emerges rapidly in adolescence and is related to the propensity comes, as each hinders effectively navigating the social world. for risk-taking behaviors in daily life (Somerville et al., 2017). On the more extreme end of the continuum, high social sen- Exploration during adolescence has recently been linked to sitivity may be related to maladaptive outcomes, as research adaptive behaviors in a risky context, such as learning and has shown that greater social-affective sensitivity is related to lower perceptions of real-world risk taking (Goldenberg et al., heightened health-risk behaviors (Chein et al., 2011; Heller and 2017; McCormick and Telzer, 2017). Casey, 2016). Drawing from clinical work, highly socially anx- In order to effectively interact with a wider range of social ious individuals are hyper-attentive to social cues and tend to agents, adolescents need to develop more complex social cogni- have lower thresholds to detect angry faces, which results in tive abilities (Blakemore and Mills, 2014). For example, they impaired social functioning in daily life (Gilboa-Schechtman interact with different teachers for each course in school, de- and Shachar-Lavie, 2013). In line with these findings, chronic- velop more dynamic peer relationships and begin exploring ro- ally victimized adolescent girls as compared to non-victimized mantic interests without direct scaffolding from parents. In girls show greater risk-taking behavior after an episode of exclu- order to navigate these more complex social relationships, so- sion which is mediated by greater activation in regions involved cial cognitive abilities are required, which include being attuned in affective sensitivity, social cognition and cognitive control to and adequately processing social cues, engaging in mentaliz- (Telzer et al., 2017). On the other end of the continuum, detri- ing processes to consider what others are thinking and feeling, ments in social sensitivity are apparent in individuals along the as well as flexibly adjusting behaviors based on social feedback autism spectrum, who experience psychosocial difficulties be- (Nelson and Guyer, 2011; Somerville, 2013). Within the broader cause of impairments in social cognition (Lai, Lombardo and domain of social cognition, social sensitivity can be defined as a Baron-Cohen, 2014). Individuals along the autism spectrum shifting motivation that intensifies the attention, salience, and tend to show reduced sensitivity to social cues, which is modu- emotion implicated in processing social cues (Somerville, 2013). lated by diminished activity in social cognition and affective Significant developmental changes in the adolescent brain may brain regions (for a meta-analysis, see Philip et al., 2012). Taken partly underlie this shifting motivation in social sensitivity. together, empirical and theoretical work suggests a curvilinear A collection of brain regions, including basic affective regions, relationship between social sensitivity and adaptive decision- social brain regions and cognitive control regions likely work in making—either too little or too much social sensitivity may be concert in the process of social sensitivity. In regards to affective maladaptive, whereas a moderate level of social sensitivity may regions, a key aspect of social sensitivity is processing social cues be associated with adaptive outcomes. While prior work sug- from facial expressions. Recruitment of the amygdala, anterior in- gests impairments in social sensitivity at both the extremely sula (AI), and superior temporal sulcus allow individuals to recog- high end (e.g. socially anxious) or extremely low end (e.g. aut- nize emotions in others (Cohen Kadosh et al., 2013b;Fuhrmann ism spectrum), we do not currently know how variability in so- et al., 2016). Indeed, neural processing of facial expressions de- cial sensitivity within a normative adolescent sample is linked velops throughout adolescence, with sensitivity to social feedback to adaptive outcomes in a risky context. peaking in this network during adolescence (Guyer et al., 2009, 2012; Jones et al., 2014). A higher-order aspect of social sensitivity is understanding and acting on more complex social emotions. Present study That is, emotions that require the representation of other people’s The goal of the present fMRI study was to test the hypothesized mental states, or mentalizing (Burnett et al., 2009). Activation in curvilinear relationship between social sensitivity on a behav- the social brain network required for mentalizing, including the ioral and neural level and adaptive decision-making in adoles- medial prefrontal cortex (mPFC) and temporoparietal junction cence. We were specifically interested in individual differences (TPJ), is greater in adolescence than children or adults when evalu- in social sensitivity in the context of risky decision-making and ating social emotions (Burnett et al., 2009; Somerville, 2013). employed the novel Social Analogue Risk Task (SART), a social Finally, this network of affective and social brain regions works to- adaptation of the well-validated Balloon Analogue Risk Task gether with the cognitive-regulatory network (e.g. lateral pre- (Lejuez et al., 2002; Humphreys et al., 2016). The SART is a ‘trick- frontal cortex, lPFC) to support the execution of goal-directed and or-treat game’ that measures participants’ willingness to knock flexible social behaviors, for example after social feedback (Nelson on doors in order to earn points. With each knock on a door, and Guyer, 2011; Casey, 2015). In sum, social sensitivity is sup- points increase while the facial expression of the house’s resi- ported by a network of regions including affective processing, so- dent morphs from happy to somewhat angrier. Knocking is cial cognition and cognitive control. associated with an increasing risk, because if the resident gets too angry and slams the door, all points for that door are lost. A curvilinear relationship between social sensitivity and Crucially, some residents are faster to get angry than others, adaptive outcomes and so adolescents need to flexibly adapt their risk-taking be- A recent neurobiological susceptibility model posits that adoles- havior (i.e. number of knocks on each door) in the context of cent development is shaped by brain-based individual each new resident in order to collect the most points. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 548 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Social sensitivity was defined as the extent to which adoles- form is a well validated and widely-used task to assess risk- cents adjusted their risk-taking behavior based on information taking behaviors across development (Lejuez et al., 2002; about the anger level of the house’s resident. As such, social McCormick and Telzer, 2017). Risk taking on the BART is corre- sensitivity in the SART likely taps into social cues from facial lated with real-life risk taking in adolescents, both concurrently expressions, as well as mentalizing about the thoughts and feel- (Telzer et al., 2015) and longitudinally (Qu et al., 2015). Similar to ings of the resident. That is, greater social sensitivity would en- the BART, the SART involves sequential risk-taking in pursuit of tail knocking less on doors of residents who changed from points. The novel aspect of the SART is a social component, happy to angry relatively fast, and knocking more on doors of which makes it more comparable to real-world adolescent risk- residents who were slow to get angry. We hypothesized that taking behavior that tends to occur in an interactive social en- relatively moderate social sensitivity (i.e. normative exploration vironment. This social environment was created by displaying and cashing-out right before the resident gets too angry and dynamically changing facial expressions in response to partici- slams the door) would be related to greater self-reported adap- pants’ risky decisions. The current version of the SART was tive decision-making. Low or high levels of social sensitivity, based off of previous behavioral studies (e.g. Humphreys et al., relative to moderate levels, are likely related to lower self- 2016), but was modified in several ways as outlined in more de- reported adaptive decision-making. tail below. At the neural level, we expected that moderate social sensi- The task was explained as a ‘trick-or-treat’ game, in which tivity would be related to enhanced tracking of the changes in participants were presented with a series of 24 people at their emotional expression in affective, social cognition and cognitive houses. Participants could knock on the door at each house to control regions during risky decision-making, as prior work sug- earn points for every knock (i.e. more knocks resulted in more gests that these brain regions are involved in attuning to social points). They were instructed to earn as many points as pos- cues and flexible social behavior (Nelson and Guyer, 2011; sible, which could be cashed in for prizes after the study. All Somerville, 2013; Rosen et al., 2017). We expected to find a curvi- residents initially started out with a happy facial expression linear effect in these regions, such that compared to moderate and grew increasingly angry with each successive knock, and levels of social sensitivity, low and high levels of social sensitiv- would eventually slam the door, at which point the participant ity would result in less neural tracking of the changes in emo- would lose all points earned for that respective door. tional expression in these regions. Finally, we predicted that Alternatively, participants had the option to cash out the points enhanced neural tracking in these regions would be linked to they earned for that respective door at any point. During train- greater self-reported adaptive decision-making. ing, participants were shown what these two options (i.e. knocking and cashing out) and their outcomes would look like. Each knock decision was accompanied by a knocking sound, Materials and methods slam events were accompanied by a loud slamming sound, and cash-out decisions were accompanied by a short note that indi- Participants and procedure cated point receipt. A running total of points earned was pre- This study included 35 healthy adolescents between 12 and sented on a points meter throughout the task (Figure 1A). 16 years (M ¼ 15.28 years, s.d. ¼ 1.34; 61% female). One add- Age The door always slammed at a 50% angry facial expression, itional participant was excluded due to excessive movement which was not explicitly explained to participants. Yet, some (> 2 mm movement between slices on>10% of slices). Within residents were slower to get angry than others. Thus, although the sample, 74% identified as European-American, 14% as all faces morphed along the same continuum from 100% happy mixed ethnicity, 6% as African-American, 3% as Latin-American to 100% neutral to 50% angry, the threshold for slamming the and 3% as Asian-American. Participants took part in a larger door varied between 3 and 10 knocks (Figure 1B for an illustra- cross-sectional fMRI study about the development of decision- tion). Therefore, some residents morphed into 50% anger after 3 making. Given that previous work using SART has shown devel- knocks, whereas others morphed more slowly and became 50% opmental changes (McCormick et al., 2018), and adolescence is angry after 10 knocks. Hence, participants could use the socioaf- uniquely characterized as a phase of social reorientation, we se- fective information in the faces to guide their decisions to deter- lected the adolescent sample a priori. All adolescent partici- mine whether they should keep knocking or cash out points. pants that had SART task data as well as Flinders questionnaire Table 1 shows descriptives concerning number of cash-out deci- data (see below) were included in this study. sions, slams as well as average and range of knocks on the task. Participants were recruited through a participant database, The task consisted of one run with 24 self-paced trials word of mouth and flyers. We screened all participants to make (Table 1 lists display of reaction times). The run lengths ranged sure that they were free of psychiatric disorders, neurological between 6.49 and 12.65 min, with a median of 9.28 min. Note disorders and MRI contraindications. A training session was that the run length mostly affects the data for the knocks condi- completed outside the scanner to train participants on how to tion, because participants with shorter runs knock fewer times perform the scan task correctly. The actual scan session lasted than those with longer runs. Trials and each consecutive facial 1.5 h. Participants received a $50 endowment and selected expression (i.e. angrier expression following a decision to knock; prizes from a prize box based on the points they earned on the new face after decision to cash-out; new face following a slam tasks during the scan. Participants and their parents provided trial) were separated with a random jitter (500 –4000 ms). The written informed consent and assent prior to the start of the faces were drawn from the NimStim face database and modeled study. All procedures were approved by the Institutional Review off of a study by Humphreys and colleagues (Tottenham et al., Board of the University of Illinois. 2009; Humphreys et al., 2016). Participants saw 12 individual faces (4 European-American, 4 African-American and 4 Asian- American; all faces were female) twice during the task. For each Measures participant, faces were presented in the same order and with fixed but previously randomly determined slam thresholds. As Social analogue risk task. Participants played an adapted version of the Balloon Analogue Risk Task (BART), which in its original such, the current fMRI task was adapted from previous work by Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 549 Fig. 1. (A) Illustration of the SART. The cash-out decisions, highlighted in the red square, were the focus of the current analyses. Each decision was self-paced and there was a jitter (500–4000 ms) between each event. Participants played a ‘Trick-or-Treat’ game during which they knocked on doors in order to earn points. With each knock, the facial expression of the house’s resident morphed from happy to somewhat angrier. At 50% angry, the resident slammed the door and all points for that door were lost. Participants could also cash out at any moment. (B) Example of variable anger increments between residents. The upper resident slams the door after four knocks, whereas the lower resident is slower to anger and takes seven knocks to slam the door. individual trial. Finally, the timing was also adjusted such that Table 1. Descriptives for SART task behavior the task could be administered in the MRI scanner. Task parameters Mean s.d. Range Social sensitivity. Social sensitivity during the SART was opera- Number of cash-out decisions 21.89 1.66 17–24 tionalized as the extent to which individuals adjusted their Number of door slams 2.11 1.66 0–7 number of knocks on the current trial based on information Average # of knocks on cash-out trials 4.81 1.55 3.19–6.5 about the anger level of the house’s resident. More specifically, Average reaction time (s) all trial types 1.18 0.21 0.75–1.73 social sensitivity was operationalized as knocking more on houses in which the resident morphed to 50% angry more Note: Slams and cash-out decisions are opposite to each other and add up to 24 slowly and knocking less on houses in which the resident trials in total. morphed to 50% angry more quickly. We employed hierarchical including more faces, each of which was shown twice, as well linear modeling (HLM; Raudenbush and Bryk, 2002) to obtain as having variable probabilities of door slamming for each this social sensitivity index. We modeled 24 nested trials for Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 550 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 each participant, with number of knocks as our outcome vari- 2000, p. 280), the Vigilant scale is measured with three items and able, and the anger level of the face as the predictor. The level 1 the Self-confident decision-making scale with five items. Since equation was as following: we were interested in overall adaptive decision-making, we aver- aged these subscales to have a more potent measure of adaptive Number of Knocks ¼ b þ b ðAnger Þþ b ðSlam Þ decision-making with eight items total (a ¼ 0.79). A maladaptive ij 0j 1j 2j ðN1Þ ðNÞ pattern of decision-making is indexed by 14 items with the sub- þ b ðSlam Þþ b ðTrial NumberÞþ e ðNÞ 3j 4j ij scales Panic, Evasiveness and Complacency. Reflecting the original measure, the responses are coded on a 4-point scale ranging Total knocks on a particular trial (i)for aparticular adolescent(j) from ‘Never’ to ‘Always’ and the total score is calculated as the was modeled as a function of the average number of knocks across average of the respective items. Higher scores are indicative of the task (b ) and the anger level of the face on the current trial 0j more adaptive decision-making, whereas lower scores are indica- b (Anger ) (i.e. # of knocks required to get to 50% anger). We also 1j (N) tive of less adaptive decision-making. This scale has been used to included three controls. The main control variable was whether the link neural processing to adaptive decision making in adolescents previous trial (b ) was a cash-out or slam [coded Cash-Out ¼ 0; 2j (N1) (e.g. Telzer et al., 2013). Slam ¼ 1], which represents non-social feedback learning. This (N1) variable allowed us to test how likely participants were to use infor- mation from the previous trial to guide their knocks on the next fMRI data acquisition trial, adjusting their behavior when their previous decision resulted Imaging data were obtained with a 3-T Siemens Trio MRI scan- in maladaptive outcomes. We included this variable as a control to ner, using a 12-channel head coil. The task consisted of one make sure that our measure actually reflected social sensitivity, and self-paced run. Functional data were collected using T2*- not general feedback learning (see McCormick and Telzer, 2017 for weighted echoplanar images (EPI) (slice thickness ¼ 3 mm; 38 a feedback learning approach). Two additional controls included slices; TR ¼ 2s; TE ¼ 25 ms; matrix ¼ 9292; FOV ¼ 230 mm; voxel were whether the current trial resulted in a cash-out or a slam [b ; 3j size 2.52.53mm ). To provide an anatomical reference, coded Cash-Out ¼ 0; Slam ¼ 1] and the trial number (b ), (N1) (N1) 4j structural scans were obtained, including a T2*weighted, whichare controls oftenusedinmodelingofBARTbehavior (e.g., matched-bandwidth (MBW; TR ¼ 4s; TE ¼ 64 ms; FOV ¼ 230; ma- McCormick and Telzer, 2017). Trial number controls for cumulative trix ¼ 192192; slice thickness ¼ 3 mm; 38 slices) and a T1* learning over time, and this parameter did not predict the number magnetization-prepared rapid-acquisition gradient echo of knocks/risk-taking (P¼ 0.680; see Supplementary for an overview (MPRAGE; TR ¼ 1.9 s; TE ¼ 2.3 ms; FOV ¼ 230; matrix ¼ 256256; of theentire HLM model).Notethatsocial sensitivity effects were sagittal plane; slice thickness ¼ 1 mm; 192 slices). The MBW and similar when we controlled for gender in our HLM level 2 equation. EPI scans were acquired with an oblique axial orientation. Therefore, we did not control for gender in further behavioral and neural analyses. To use the index of social sensitivity (variable of interest) fMRI data preprocessing and analysis and non-social feedback learning (control) in our analyses, We used the SPM8 software package (Wellcome Department of Empirical Bayes estimates were extracted for each participant. Cognitive Neurology, UK) for preprocessing and data analysis. These estimates represent optimally weighted averages that are Preprocessing involved correction for head motion with spatial re- computed through a combination of estimates on an individual alignment, co-registration to a high-resolution T1* MPRAGE struc- and group level, and shrinks the individual’s estimates towards tural scan, and segmentation into grey matter, white matter and the overall mean (Diez-Roux, 2002; McCormick and Telzer, cerebrospinal fluid. For one participant, the co-registration was 2017). The extracted estimate provides an individual difference conducted with the MBW scan because the T1* was missing. We measure indicating whether participants changed their behav- applied the resulting transformation matrices to the MBW and EPI ior as a function of anger level. Values larger than 0 indicate images in order to warp them into the standard stereotactic space that participants adjusted their risk-taking behavior based on as defined by Montreal Neurological Institute (MNI). EPI images social sensitivity (i.e. knocked more on houses where the face were spatially smoothed with an 8-mm FWHM isotropic Gaussian morphed more slowly and knocked less on houses where the kernel. The fMRI time series for each trial were convolved with the face morphed more quickly), whereas values around 0 indicate hemodynamic response function. To remove low-frequency scan- little or no adjustment based on social sensitivity. ner drift across time we applied a 128 s high-pass filter, and esti- mated serial autocorrelations using a restricted maximum Adaptive decision-making. The concept of social sensitivity likelihood algorithm with an autoregressive model order of 1. applies to general decision-making strategies in real-life as well We conducted statistical analyses on individual subjects’ as more specifically to social situations, as most decisions are data using the general linear model in SPM8. In the fixed-effects made in a social context or with input from social others, espe- model, knock decisions, cash-out decisions and slam events cially during adolescence when individuals are especially attuned were modeled as separate events of interest. The jitter between to the social context (Albert et al.,2013; Blakemore and Mills, 2014; events was not modeled and utilized as an implicit baseline. Scriber and Guyer, 2017). The Flinders Adolescent Decision The trials were modeled from the onset of the trial to when par- Making scale assesses adolescent decision-making patterns and ticipants made their decision using the reaction time, and as distinguishes between adaptive and maladaptive decision- such represent the decision-making phase. A parametric modu- making (Mann et al., 1989; Tuinstra et al., 2000). Adaptive lator (PM) was included to model increasing risk across knock decision-making encompasses the subscales Vigilant (precision decisions. The cash-out decision, which we focus on in the cur- and deliberation in making decisions; e.g. ‘I like to think about rent manuscript, represents neural activity at the moment par- my decision before I make it’) and Self-confident decision-making ticipants decided to cash out when the face was ‘too’ angry, and (confidence and efficacy when making decisions; e.g. ‘The deci- thus, when social sensitivity likely affects decisions. PM values sions I make turn out well’), representing careful and deliberated signified the number of knocks for the entire trial (i.e. the total decision-making. Within the revised 22-item scale (Tuinstra et al. risk that is taken for each house), and were centered within a Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 551 person around the average number of knocks for each door. We focus on the cash-out PM condition rather than the knocks condition, such that we were able to examine neural tracking of increasingly angry faces at the moment when participants decided to cash out. The resulting contrast images, computed at the individual level, were submitted to random-effects group- level analyses. At the group level, analyses were conducted using GLMFlex, which removes outliers and sudden activation changes in the brain, partitions error terms, analyzes all voxels containing data, and corrects for variance-covariance inequality (http://mrtools.mgh.harvard.edu/index.php/GLM_Flex). We first looked at the main effect and then performed whole-brain regression analyses on the cash-out PM condition, in which social sensitivity was entered as a quadratic term, given our hypothesis that a moderate amount of social sensitiv- ity would be most beneficial. In this analysis, we controlled for social sensitivity as a linear predictor as well as the index for nonsocial feedback-learning. Given the use of GLM-Flex, we cor- rected for multiple comparisons using a Monte Carlo simulation through 3dclustsim (updated version November 2016) in the Fig. 2. Quadratic relationship between social sensitivity and self-reported adap- software package AFNI (Ward, 2000), and computed the smooth- tive decision-making, as indicated by the Flinders adolescent decision-making ness of the data with the acf function within the 3dFWHMx scale. command. For the main effect, the simulation resulted in a voxel-wise threshold of P< 0.005 and minimum cluster size of 79 voxels for the whole-brain, which corresponds to P< 0.05, baseline model, added social sensitivity in model 2, and non- FWE cluster-corrected. For the regression, the simulation re- social feedback learning in model 3. Kolmogorov–Smirnov tests sulted in a voxel-wise threshold of P< 0.005 and minimum clus- of normality showed that all of these variables were normally ter size of 107 voxels for the whole-brain. distributed (P’s> 0.05). Model 2 (social sensitivity controlling for social sensitivity); and model 3 (social sensitivity control- ling for social sensitivity and non-social feedback learning) Results were significant (Ps< 0.05), but only model 2 predicted signifi- cantly more variance than the baseline model (r ¼ 0.209; change Behavioral results P ¼ 0.006). As such, model 2 was the best fit to the data, explain- ing 20% of the variance [(F(2, 34) ¼ 5.249, P ¼ 0.011]. As expected, First, we employed HLM to estimate how adolescents adjusted there was a curvilinear relationship between social sensitivity their decision-making based on social sensitivity to shifting fa- and adaptive decision making, such that moderate levels of so- cial expressions, as well as non-social feedback learning on the cial sensitivity were associated with more adaptive decision task. As expected, adolescents showed significant social sensi- making, whereas low and high levels of social sensitivity were tivity on average, as indexed by a strong association between associated with lower adaptive decision making [b¼4.392, number of knocks and anger level (b ¼ 0.545, SE ¼ 0.041, SE ¼ 1.475, b¼2.232, P ¼ .006; Figure 2). Non-social feedback P< 0.001). Thus, adolescents knocked more when residents’ fa- learning in model 3 did not contribute to the prediction of adap- cial expressions were slower to change from happy to angry, tive decision-making (b ¼ 0.897, SE ¼ 0.742, b ¼ 0.286, P ¼ 0.236). and knocked less when residents’ facial expressions were faster Together, these findings underscore the important adaptive to change from happy to angry. Moreover, adolescents also role of moderate social sensitivity for adolescent decision- showed significant non-social feedback learning, as indexed by making processes. knocking less if the previous trial was a slam (b ¼0.587, SE ¼ 0.118, P< .001). As such, they learned to adjust their behav- ior when this previously resulted in a maladaptive slam out- fMRI results come. The complete set of results of the first level model can be Neural tracking of cash-out decisions. We first examined the main found in Supplementary Table S1. Next, we extracted the Empirical Bayes estimates for social sensitivity and non-social effect of the cash-out decisions, during which participants decided to cash out points before the door was slammed. feedback learning for each participant. There was considerable variability in the Empirical Bayes estimate of social sensitivity, Results showed that the left precuneus and occipital gyrus tracked increasingly angry faces with corresponding increased M ¼ 0.545, s.d.¼ 0.224, range 0.129–0.909. Whereas some partici- pants showed relatively low social sensitivity (i.e. estimates risk when participants decided to cash out (Table 2). In addition, the right TPJ was recruited, but this region did not survive cor- closer to 0), other adolescents showed relatively high social sen- sitivity (i.e. estimates closer to 1). Non-social feedback learning rection. As such, these findings show that social brain regions and visual regions are recruited more as participants knock also showed individual variability (M¼0.587, s.d. ¼ 149), range 0.842 to 0.183. Some participants showed a larger decrease in more and see increasingly angry faces on trials where they de- cide to cash out. the number of knocks after the previous outcome was a slam (i.e. estimates closer to 1), while others showed a small de- crease in knocks after a slam (i.e. estimates closer to 0). Links between social sensitivity and neural tracking during cash-out Next, we ran a regression model to examine whether social decisions. In our primary analyses, we conducted a whole-brain sensitivity showed curvilinear relations with adaptive decision regression analysis, in which social sensitivity was entered as a making. We entered linear social sensitivity in model 1 as a quadratic predictor, and linear social sensitivity and non-social Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 552 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 Table 2. Brain regions that displayed a main effect for the cash-out level. In order to do so, the novel SART, a ‘trick-or-treat’ game, PM contrast was employed in a sample of 12–16-year-old adolescents. Behavioral findings revealed a curvilinear relationship between Region label Volume t-Value MNI coordinates social sensitivity and adaptive real-world decision-making. (mm ) During decisions to cash-out, activity in the TPJ, insula and xy z dlPFC displayed a curvilinear relationship with social sensitiv- L Superior occipital gyrus 88 4.058 21 79 40 ity, indicating that these brain areas tracked shifting facial ex- L Precuneus 88 3.659 9 73 55 pressions the most at a moderate level of social sensitivity, but R Superior temporal 66 3.974 60 37 13 less so at low or high levels of social sensitivity. Finally, brain gyrus (TPJ) activity in each of these regions was associated with greater lev- els of adaptive real-world decision-making, with the TPJ show- Note: Analysis for negative relationships showed no significant clusters of ing the strongest effect, and insula and dlPFC at the trend level activation. P<0.05, FWE cluster-corrected. a after correcting for multiple comparisons. Findings suggest that Does not survive FWE-cluster correction. moderate social sensitivity is related to adaptive decision- making in a risky context, and this is modulated via heightened tracking of social cues in the TPJ, insula and dlPFC. feedback learning were included as covariates. Results indicated a quadratic relationship for several regions implicated in social Moderate social sensitivity and adaptive cognition and cognitive control, including the left TPJ, right in- decision-making sula and right dorsolateral prefrontal cortex (dlPFC), demon- strating that these brain regions track increasing anger the Adolescence is a developmental period marked by a major so- most at moderate levels of social sensitivity, but track less so at cial re-orientation, with changes in social as well as health-risk either low or high levels of social sensitivity (Figure 3; Table 3). behaviors (Ellis et al., 2012; Nelson et al., 2016). Elevated adoles- Next, we ran a whole-brain regression analysis in which feed- cent social-affective sensitivity has traditionally been linked to back learning was entered as a predictor, controlling for social increases in excessive health-risk behaviors. More recently sensitivity and social sensitivity, to examine whether this ef- however, research has started to highlight the more adaptive fect was unique to social sensitivity. This analysis yielded no properties of normative exploration and risk-taking, as well as significant results, demonstrating that these neural effects are social sensitivity (Crone and Dahl, 2012). There is a large and indeed unique to social sensitivity. meaningful within-group variability in adolescent risk taking compared to childhood or adulthood (Van Duijvenvoorde et al., Neural activity and adaptive decision-making. Finally, we ex- 2016), which further illustrates the need to characterize norma- tracted the parameter estimates (averaged over the whole clus- tive risk taking vs excessive risk taking. For example, explor- ter) from the left TPJ, right insula and right dlPFC which showed ation facilitates learning and understanding of the (social) significant quadratic relations with social sensitivity and con- environment, with greater experience relating to more available ducted a series of regressions to predict self-reported adaptive information, which is helpful in consequent decision-making decision-making. For the neural activation and self-reported (Goldenberg et al., 2017; McCormick and Telzer, 2017; Somerville adaptive decision making, high scores on each are predicted to et al., 2017). Our behavioral findings resonate with and extend be adaptive (i.e. moderate social sensitivity related to high previous work by revealing that those with moderate social sen- adaptive decision making and high neural activation). sitivity to shifting facial expressions, relative to low or high so- Therefore, taking out social sensitivity in the equation here, we cial sensitivity, showed greater adaptive decision-making. expected high neural activity to be related to high adaptive Taken together, the present findings provide evidence that decision-making, which should result in a linear relationship moderate adolescent social-affective sensitivity may confer ad- rather than a quadratic relationship. Separate regressions were vantages in a risky context. run with a Bonferroni correction for multiple comparisons Theoretical and empirical work shows evidence for intensi- (0.05/3 ¼ 0.017), for each brain region. The regression model for fied processing of social cues from the environment during ado- the TPJ was significant [F(1, 33) ¼ 10.79; R ¼ 0.224, P ¼ 0.002], adj lescence (Somerville, 2013). Sensitivity to social cues, and in such that increased tracking in the TPJ predicted greater adap- particular to changing emotional expressions, is key in navigat- tive decision-making (b ¼ 2.729, SE ¼ 0.235, b ¼ 0.496; P ¼ 0.002). ing the social world (Neta and Whalen, 2011). Greater social sen- The regression for dlPFC [F(1, 33)¼ 5.90; R ¼ .126, P ¼ 0.021; adj sitivity to facial expressions has been linked to less social b ¼ 0.428, SE ¼ 0.176, b ¼ 0.389, P ¼ 0.021] and for insula anxiety and fewer social problems (Rosen et al., 2017). In this [F(1, 33) ¼ 4.271; R ¼ 0.088, P ¼ 0.047; b ¼ 0.319, SE ¼ 0.154, adj study, we assessed social sensitivity based on task behavior, as b ¼ 0.339; P ¼ 0.047] revealed similar patterns at the trend level, the extent to which adolescents adjusted their risky decision- when we accounted for multiple comparisons (Figure 3 shows a making based on how quickly the facial expression changed display of these analyses; visualization purposes only). Taken from happy to angry. This enabled us to look at individual dif- together, these findings demonstrate that more neural tracking ferences in social sensitivity to facial expressions, and for the in these regions associated with social cognition and cognitive first time, how this affects decision-making in a risky context. control is associated with higher levels of adaptive decision- Our findings supported the hypothesis that being moderately making. sensitive to changing facial expressions facilitates goal-directed and adaptive risky decisions. Given that the adolescent period encompasses the time when individuals reach mature social Discussion goals (Cohen et al., 2016), these findings intuitively make sense. This study tested whether moderate levels of social sensitivity That is, being in a social vacuum (low social sensitivity) or being to changing facial expressions was associated with adaptive exceptionally attuned to one’s social environment (high social decision-making in a risky context, on a behavioral and neural sensitivity) limits successful interactions with others Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 553 Fig. 3. Quadratic relationship between social sensitivity and tracking of increasing anger in left TPJ (MNI 60 40 40), right dlPFC (MNI 36 47 25) and right insula (MNI 45 1 7) during decisions to cash-out (P<0.05, FWE-cluster corrected; for visualization purposes only). Table 3. Brain regions that displayed a quadratic relationship with social sensitivity when adolescents chose to cash out, controlling for nonso- cial feedback learning and the linear predictor for social sensitivity Region label Volume (mm ) t-Value MNI coordinates xy z R Insula 401 5.264 45 17 R Superior temporal gyrus 401 3.952 69 19 1 L Cerebellum 354 5.328 12 52 26 L Supramarginal gyrus (TPJ) 157 5.280 60 40 40 R Superior frontal gyrus (premotor) 122 4.767 21 164 R Middle frontal gyrus (dlPFC) 108 4.758 36 47 25 R Cerebellum 129 4.594 15 49 32 Note: Analysis for positive relationships showed no significant clusters of activation. P<0.05, FWE cluster-corrected. Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 554 | Social Cognitive and Affective Neuroscience, 2018, Vol. 13, No. 5 (Nelson and Guyer, 2011; Blakemore and Mills, 2014). Moderate feedback) (Guyer et al., 2009; Lamm and Singer, 2010). As such, social sensitivity on the other hand likely enables adolescents the insula plays a critical role in guiding decision-making and to successfully interact with a wide range of social agents, has particular importance for risk-taking (Smith et al., 2014). In a including parents, teachers, and peers. Ultimately, successful similar task design, adolescents showed greater insula activa- social relationships may pave the way to reaching mature social tion when taking risks in a social feedback relative to non-social goals in adulthood. feedback condition (Op de Macks et al., 2017). The present find- ings fit with previous work and suggest that for individuals with moderate levels of social sensitivity, shifting emotional expres- Neural correlates of moderate social sensitivity and sions were most salient, eliciting increased allocation of atten- adaptive decision-making tional resources that may facilitate social learning from the facial expression. Perhaps for adolescents on both the low and Changes in social and health-risk behaviors during adolescence high end of the social sensitivity continuum, facial expressions are paralleled by dynamic restructuring of the neural architec- are not as salient, or potentially hypersalient, which hinders the ture of affective, social and cognitive control networks (Crone social learning process from facial expression and is detrimen- and Dahl, 2012; Blakemore and Mills, 2014). A recent neurobio- tal to adaptive decision-making in a social context. logical susceptibility model poses that adolescent development Imbalance models of neurocognitive development denote is shaped by brain-based individual differences in sensitivity to the implication of lateral regions of the PFC in cognitive control social context (Scriber and Guyer, 2017). In line with this model, and inhibition (Somerville et al., 2010; Casey, 2015; Shulman the current study examined individual differences in adolescent et al., 2016). In a social-affective context, the lPFC is linked to neural tracking related to social sensitivity. During decisions to flexible social behaviors and recruited together with face pro- cash-out, adolescents with moderate levels of social sensitivity cessing regions when more cognitive processes are layered onto showed increased neural tracking of shifting facial expressions passively viewing facial expressions, like labeling expressions from happy to angry with increasing risk in the TPJ, insula and (Neta and Whalen, 2011; Flannery et al., 2017). In such dlPFC. These brain regions showed less tracking at either low or cognitively-taxing affective tasks, the dlPFC facilitates working high levels of social sensitivity. Together, regions associated memory to guide goal-directed behaviors and ultimately meet with affective, social cognition, and cognitive control processing task demands (Neta and Whalen, 2011). Thus, increased track- showed differential tracking based on individual differences in ing of the dlPFC with moderate social sensitivity likely reflects social sensitivity. increased cognitive control, guiding the decision to stop knock- The TPJ is an integral part of the so-called social brain net- ing on the door and instead cash-out when the facial expression work which is implicated in social cognition and mentalizing becomes too angry. That is, the dlPFC plays a role in the ability (Blakemore and Mills, 2014). In particular, TPJ activation has to flexibly switch risk-taking behaviors in light of the social cues been linked to perspective-taking and understanding more from the social environment. On the other hand, adolescents on complex emotions, as well as more general attention processes the low and high end of social sensitivity show less tracking in in the social domain (Burnett et al., 2009; Van den Bos et al., this area implicated in cognitive control, which is associated 2011). Although TPJ activation did not survive stringent correc- with less adaptive decision-making. tion in the main effect when we solely examined neural track- ing during decisions to cash-out independent of social sensitivity, it is an interesting finding given that the non-social Future directions and conclusions BART does not elicit any TPJ activity for a similar contrast (McCormick and Telzer, 2017). However, the effect with the A few limitations should be noted. First, this study only incorpo- curvilinear relation to social sensitivity during decisions to rated dynamically changing female adult faces, because the cash-out did survive stringent correction. One interpretation for task was based on previous work (Humphreys et al., 2016). TPJ activity may be perspective-taking, i.e. trying to understand Future research should replicate and extend the current find- the resident’s thoughts and feelings. Alternatively, TPJ activa- ings with male faces, as well as different emotional expressions tion may represent a more general attention process of being (e.g. happy shifting to sad). Another interesting direction for fu- attuned to the changing emotions displayed by the resident of ture research would be to examine the relation between social the house. As such, relatively moderate social sensitivity may sensitivity and neural responses to peer faces, for example be facilitated by social attention or perspective-taking processes using the recently validated duckEES dynamic facial expres- that guides adaptive decision-making. Because our analyses uti- sions dataset (Giuliani et al., 2017). Given the value placed on lized a PM, the neural effects represent linear increases in the peer relationships and the increase in risk-taking when adoles- neural tracking of emotional expressions in the faces rather cents are with their friends (Chein et al., 2011), the peer context than mean level activation. Thus, those with low and high so- represents an important and salient context to further investi- cial sensitivity each showed low TPJ tracking of increasing gate social sensitivity. Previous studies that directly compared anger, suggesting that these adolescents are less sensitive to adolescent neural responses to peer and adult faces have the changing social cues in the facial expressions, which is shown mostly overlapping brain regions, except for enhanced associated with less adaptive decision-making. amygdala response to positive peer faces and angry adult faces The insula is considered a ‘hub’ between cognitive control (Marusak et al., 2013; Flannery et al., 2017). and affective brain networks and has been linked to a wide The SART does not allow us to disentangle the neural re- range of functions across different contexts. Indeed, the insula sponse to increasing risk and shifting emotional expressions as is implicated in salience detection and attentional resource for they increase at the same time. Although we used a PM to spe- goal-directed behavior (Menon and Uddin, 2010; Smith et al., cifically examine neural tracking with increasing risk, which in 2014). In the affective literature, insula activity has been re- itself controls for the social and non-social aspects of the task, ported for basic face processing, perceiving emotional states of future research is needed to disentangle and study these two the self and others and integrating this internal information processes independently. It will also be important to extend the with external cues from the social environment (i.e. social current findings with an additional non-social control Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018 J. van Hoorn et al. | 555 Cohen, A.O., Breiner, K., Steinberg, L., et al. (2016). When is an condition, for example using shapes that change color signaling the rate of transition instead of faces, or balloons in which the adolescent an adult? Assessing cognitive control in emotional size indicates the explosion threshold, unlike the traditional and nonemotional contexts. Psychological Science, 27(4), 549–62. BART in which the balloon can explode at any time with no cue Cohen Kadosh, K., Johnson, M.H., Dick, F., Cohen Kadosh, R., indicating the threshold. In addition, while self-report of Blakemore, S.-J. (2013a). Effects of age, task performance, and decision-making strategies gets at adolescents’ own perception structural brain development on face processing. Cerebral Cortex, 23, 1630–42. of their decisions, this method is therefore also limited. Future studies can employ different methods to get at other aspects of Cohen Kadosh, K., Johnson, M.H., Henson, R.N.A., Dick, F., this decision-making concept, such as using observational Blakemore, S.-J. (2013b). 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Salience Available: https://afni.nimh.nih.gov/pub/dist/doc/manual/Alp network response to changes in emotional expressions of haSim.pdf (17 November 2017, last accessed date). Downloaded from https://academic.oup.com/scan/article-abstract/13/5/546/4908664 by Ed 'DeepDyve' Gillespie user on 21 June 2018

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Social Cognitive and Affective NeuroscienceOxford University Press

Published: Feb 24, 2018

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