Functional Magnetic Resonance Imaging research has identified the striatum as a key hub in PE learning , along with the insula and anterior cingulate cortex . These regions also exhibit significant neurodevelopment during adolescence . Indeed, the first study to have examined PE in adolescents found that teenagers exhibit a similar, albeit stronger striatal response for positive PE compared to adults , and a more recent study found adolescents learn faster from reward PE than adults , suggesting that key PE learning systems are similar though perhaps more sensitive during adolescence. In the current study, we adopted a violations of expectations framework, which is reflective of both the PE calculation as well as the social context in which it occurs. Within this framework, we sought to determine whether adolescents show exaggerated behavioral responses to VoEs, and to extend this question to examine whether they demonstrate unique neural responses when they receive expected and unexpected social feedback from a friend. In adults, social learning tasks with relationship partners and conspecifics implicate a similar neural network as that involved in PE, suggesting that PE computations are conserved across domains. Adolescents spend much of their time with friends but it is unknown whether specific peer feedback , valence, and value from known peers influences behavior and recruitment of still-maturing neural regions. Although several studies have examined adolescent neural reactivity to unexpected peer feedback and peer evaluation ,trimming cannabis few have done so within the context of a close friend, whose opinion may yield more realistic behavioral and neural responses.
The goal of this study was to identify the neural mechanisms associated with learning social information from a friend using a novel social PE task in adolescents. Exploring this phenomenon in adolescents is important compared to other developmental age groups because adolescents often rely on their friends’ opinions to form perceptions of themselves , and learn many social skills necessary for development during this time from their friends. Because building up social expectations is challenging in a laboratory setting, we leveraged real-life social expectations between friends and manipulated them in the laboratory. We hypothesized that adolescents would: 1) demonstrate decreased reaction time when expectations were met compared to when they were violated ; 2) report feeling happier learning their friend reported something better than they expected compared to something worse than what they expected ; 3) activate brain regions previously associated with PE, such as the ventral striatum when they learned something better than expected from their friend; and 4) activate regions associated with socioemotional processing, such as the subgenual anterior cingulate cortex when they learned something worse than expected from their friend. Participants completed a Social Violations of Expectations task while undergoing a brain scan. In this task, we presented target participants with statements that were purportedly based on the friends’ responses to the Friendship Questionnaire. The responses were presented as if they represented the friend’s true response, but in fact they were manipulated. To create positive violations of expectations, we changed the friend’s responses to be greater than the target’s expectation . To meet the expectations of the participant, we modified the friend’s response to match the target’s prediction.
To create negative violations of expectations, we modified the friend’s response to be worse than the target’s expectation . Because the dyads were close friends, participants tended to have relatively positive expectations of their friends’ responses, which limited our ability to change valence and VoE value of the friend’s “responses”. Thus, during the session, each participant was presented with an average of 14.96 positive violations, 7.04 items where expectations were met, and 17.88 negative violations. The task was programmed in E-Prime 2.0 and was presented through an LCD Optoma projector connected via fiber optic cables. Participants were presented with all 40 statements from the Friendship Questionnaire and were asked to press a button on a 4-button button box with their right index finger to proceed to the next question/trial. The trial began with the presentation of the question, followed by the target’s expectation, a 2000-6000ms jittered interstimulus-interval , their friend’s “response,” a jittered ISI, and a request to press a button to proceed to the next trial followed by a 4000-8000ms jittered inter-trial-interval . Participants were allotted a maximum of 5000 ms to press the button before a jittered fixation cross appeared on the screen, and the subsequent trial began . Following the scan, participants completed a questionnaire that contained each statement from the Friendship Questionnaire, along with their predictions and their friend’s “responses.” For example, participants were reminded of the statement, “My friend thinks I’m nice” and were shown that they expected their friend to report 6, while their friend “reported” 10. They were asked to report how seeing this made them feel on a scale of 1 to 10 . The present study was a within-subjects, event related design. Our independent variable was the violation of expectation, operationalized by modifying the friend’s initial response to be better than, equal to, or worse than the target’s expectation. Our dependent variables were 1) behavioral response, 2) self-reported response, and 3) neural activation when targets experienced a violation of expectations. Behavioral response was measured by indexing reaction time to press a button on a response box to move on to the next trial. Self-reported responses were collected via survey following completion of the task. Neural activation was examined using a priori regions of interest .
Behavioral Analyses IBM SPSS Statistics Software, version 23.0 was used to analyze RT and self-report responses to social violations of expectations. The data were analyzed three ways: 1) effects of expected versus unexpected information; 2) effects of valence ; and 3) parametric effects of incremental differences between the expectations and the associated outcomes—which was represented by violations that ranged between 9 values less than their expectation and 9 values greater than their expectation . fMRI Analyses Images were collected using a 3-Tesla Siemens Trio MRI machine equipped with 16- channels at the Staglin Center for Cognitive Neuroscience at UCLA. Two structural MRI images were collected at the start of the scan: a T1-weighted magnetization-prepared rapid-acquisition gradient echo image , 2000 ms; echo time , 2100 ms; matrix, 256 x 256; and field of view, 250 mm) and a T2- weighted matched bandwidth high-resolution scan,vertical cannabis which was prescribed to the functional images. One functional run was collected and consisted of a maximum of 440 and an average of 382 T2*-weighted echo-planar images . The first three TRs of the run were automatically discarded. Data preprocessing and analyses were conducted using the FMRIB Software Library version 5.0 . Images were corrected for motion using MCFLIRT and denoised using multivariate exploratory linear optimized decomposition into independent components analysis. Data were smoothed using a 5-mm full width-half-maximum Gaussian kernel and filtered with a nonlinear high-pass filter . A three-step registration process was used to align individual participant data into standard Montreal Neurological Institute space. EPI images were first registered to the matched band width image, then to the MPRAGE image, and finally to MNI space. Two subjects’ data required truncating of the last 115 volumes due to excess motion in the latter half of the scan. Data were analyzed using a subtraction method to model violations of expectations, the difference between the outcome and the expectation .Six motion parameters were included as covariates in the model for each run for each of the participants. Statistical analyses were performed on each participant’s data using a general linear model to observe neural activation associated with change in expectations. Each participant’s data were modeled using a three-column regressor that contained the onset of each event , its duration, and a standard weight of 1 . Neuroimaging analyses were performed three ways to examine neural response to the following: 1) social versus non-social trials); 2) violations versus nonviolations of expectations; and 3) valence of information . To compare neural activation to violations versus nonviolations of expectations, we averaged all VoE values different from 0 and tested them against VoE0 . To compare neural activation by valence , violations between VoE-9 and VoE-1 were grouped and averaged as negative trials, non-violations were grouped and averaged to represent when expectations were not violated , and VoE+1 to VoE+9 were grouped and averaged as positive trials; respectively. Analyses were conducted using FMRI expert analysis tool , first at a lower level to represent one level of a condition within an individual subject, then at a second level using a fixed-effects model to represent all levels of a condition for one subject. Finally, data were analyzed at a group level analysis using FMRIB’s Local Analysis of Mixed Effects with automatic outlier detection to group all participants together and compare contrasts of interest. Structural ROI masks containing a cluster radius of 6 voxels were created for the amygdala, dorsal anterior cingulate cortex , insula, subgenual anterior cingulate cortex , and ventral striatum based on probability maps from Neurosynth . These masks were added as the Pre-threshold Masks for each respective ROI and were clustered at the voxel level with a Z threshold of 2.3 and probability threshold of p = .05. To compare the RT between trial types to information type , a two-way repeated measures analysis of variance was performed.
This analysis did not reveal a main effect of trial type or information, or an interaction . To determine whether there were differences between valences in the violations, we ran a two-way repeated measures ANOVA comparing trial type to valence of the violations . The results revealed no main effect of valence or interaction between trial type and valence 1 . Thus, we sought to determine whether there were parametric differences between VoE values. Because not all participants had the same number of valence and value trials, a cross classification analysis was performed by indexing each variable of interest by each individual trial. This analysis enabled us to account for the variation that naturally occurred within each participant’s responses, as well as between participants. A regression was performed on the reorganized cross-classified data to account for 1) each trial for each participant; and 2) each VoE value associated with each trial.The goal of this study was to characterize the neural correlates of social violations of expectations in adolescents based on feedback from a close friend. Behaviorally, we found that adolescents’ reaction times decreased linearly as social expectations transitioned from negative to increasingly positive violations. Self-reported happiness increased linearly for social information, as social expectations transitioned from negative to increasingly positive violations. Correlational analyses indicated participants who reported closer friendships were happier to learn they were accurate in their expectations about their friendship, while participants who were not as close were happier when their expectations were positively violated. Neurobiologically, participants demonstrated greater recruitment of the VS for social positive compared to social negative violations; and greater recruitment of the subACC and insula for social negative compared to social positive violations. These results are in accordance with previous literature, such that increased reaction time to negative social expectations may have reflected greater cognitive interference on these trials , especially when the stimuli are emotional in nature . This may have been true specifically for increasingly negative social violations compared to positive social violations, as positive social violations may not have been as surprising to participants who were close friends. Thus, we speculate that participants’ self reported closeness may have contributed to this result, whereby participants were more surprised to receive negative social feedback from their friend.Interestingly, we found participants who reported being closer friends reported feeling happier when expectations were not violated compared to when they were violated, and were increasingly happier as social expectations were increasingly positively violated. Reminding participants of their predictions and their friends’ responses may have felt threatening whereby participants knew their friend could have reported something worse than they expected. In turn, this may have amplified the happiness they reported when expectations were met, perhaps indicating they were relieved and were pleased to experience reciprocity or something better than what they expected. We posit that experiencing a violation of any kind is conflicting and emotionally arousing for an adolescent, as they are hyper-sensitive to peer feedback, and learning they are incorrect about their friendship may be disconcerting compared to learning they are correct.