Internalizing disorders, such as anxiety and depression, commonly feature cognitive biases in self-evaluation, particularly in the context of social comparison. Despite the role of such biases in the severity and prognosis of internalizing disorders, limited work has identified computational mechanisms underlying self-evaluative biases in social contexts. In a sample of N = 292 participants, in the present study, we applied hierarchical Bayesian computational modeling to a trait-evaluation task in which individuals choose whether positive and negative traits better describe themselves or a close friend. We found that individuals generally engage in self-enhancement during this task, more efficiently processing information that supports positive self-schema. However, this effect flips as individuals report more symptoms such that it becomes more difficult to integrate evidence in support of a positive self-concept. These findings suggest that altered processing of both positive and negative self-referential information is a transdiagnostic mechanism driving aberrant self-evaluation in internalizing disorders.

Abstract Facial perception is a central feature of everyday social encounters and a rich source of emotional information. Classic functional magnetic resonance imaging (fMRI) studies of emotional facial processing used static photos emotional expression to identify regions of the brain showing univariate differences in response magnitudes between different emotional categories. However, there has been much less work identifying how the brain represents dynamic emotional facial expression and the factors that drive the similarity among these representations. In the current study, incorporated dynamic facial expression stimuli and representational similarity analysis to compare three competing hypothesized models of similarity of each of the stimuli presented: action being made, valence of the expression, and the identity of the person being perceived. Participants were shown short videos of fourteen volunteer actors making positive or negative facial expressions directed either toward or away from the camera. Activation patterns were compared against competing models on a trial-by-trial basis using a full multilevel modeling approach. Results showed that the identity of the person in the video was a greater predictor of brain responses similarity than the action or valence across widely distributed brain systems, particularly in the default mode network and lower-level visual processing regions. This suggests that the specific identity of the stimulus being perceived is a central driver of neural response similarity during perceptual encoding in dynamic facial processing.

Knowing the similarities among others is critical for navigating our social environments and building relationships. However, people can evaluate the similarity among others using two perspectives: other-to-other differences (allocentric similarity) or self-to-other differences (egocentric similarity). Here, we use functional magnetic resonance imaging (fMRI) to test whether the similarity of brain-response patterns when thinking of others and the self is predicted by behavioral models of allocentric and egocentric similarity in the representations of acquainted peers from 20 independent groups of adults (total N = 108; within-subjects design). Results show that both allocentric and egocentric similarity during person representation are reflected in brain-response similarity patterns when thinking of others, but they do so differentially and in nonoverlapping brain systems. These results suggest that the brain independently processes both allocentric and egocentric reference frames to encode trait information about conspecifics that we use to represent person knowledge about others within real-world social networks.

As researchers strive to better understand transdiagnostic mechanisms underlying mental health conditions, low self-esteem emerges as an important area of focus given its presence across various clinical disorders. Multiple social psychology theories highlight the role of interpersonal perception in self-esteem and social relationships; yet often unacknowledged across these theories is the notion that mentalizing (i.e., inferring the mental states of others) shapes self-perception. This paper develops a theory that lowered self-esteem alters mentalizing tendencies in a manner that increases risk for the development of internalizing symptoms. To this end, we integrate literature relating mentalizing to self-esteem, with a particular focus on meta-perceptions, the beliefs we hold about how others perceive us. We identify three core tendencies in meta-perceptions– accuracy, propensity, and permeability – that shape our inferences of others’ appraisals in a manner that supports or diminishes self-esteem. We then examine how these tendencies in meta-perception can help explain low self-esteem as a risk factor for mental health disorders that have been associated with these processes, such as unipolar depression and anxiety. We propose that lowered self-esteem is a transdiagnostic risk factor because it leads to downstream changes in self, social, and reward process

Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size. Therefore, we propose four strategies to increase effect sizes in neuroimaging research, which may help to enable the detection of replicable between-person effects in samples in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging tasks and behavioral constructs of interest; (2) increasing the reliability of both neural and psychological measurement; (3) individualization of measures for each participant; and (4) using multivariate approaches with cross-validation instead of univariate approaches. We discuss challenges associated with these methods and highlight strategies for improvements that will help the field to move toward a more robust and accessible neuroscience of individual differences.

Social neuroscientists have made marked progress in understanding the underlying neural mechanisms that contribute to self-esteem. However, these neural mechanisms have not been examined within the rich social contexts that theories in social psychology emphasize. Previous research has demonstrated that neural representations of the self are reflected in the brains of peers in a phenomenon called the self-recapitulation effect (Chavez & Wagner, 2020), but it remains unknown how these processes are influenced by self-esteem. In the current study, we used functional magnetic resonance imaging in a round-robin design within 19 independent groups of participants (total N = 107) to test how self-esteem modulates the representation of self-other similarity in multivariate brain response patterns during interpersonal perception. Our results replicate the self-recapitulation effect in a sample almost ten times the size of the original study and show that these effects are found within distributed brain systems underlying self-representation and social cognition. Furthermore, we extend these findings to demonstrate that individual differences in self-esteem modulate these responses within the medial prefrontal cortex, a region implicated in evaluative self-referential processing. These findings inform theoretical models of self-esteem in social psychology and suggest that greater self-esteem is associated with psychologically distanced self-evaluations from peer-evaluations in interpersonal appraisals.

Understanding others involves inferring traits and intentions, a process complicated by our reliance on stereotypes and generalized information when we lack personal information. Yet, as relationships are formed, we shift toward nuanced and individualized perceptions of others. This study addresses how relationship strength influences the creation of unique or normative representations of others in key regions known to be involved in social cognition. Employing a round-robin interpersonal perception paradigm (N = 111, 20 groups of five to six people), we used functional magnetic resonance imaging to examine whether the strength of social relationships modulated the degree to which multivoxel patterns of activity that represented a specific other were similar to a normative average of all others in the study. Behaviorally, stronger social relationships were associated with more normative trait endorsements. Neural findings reveal that closer relationships lead to more unique representations in the medial prefrontal cortex and anterior insula, areas associated with mentalizing and person perception. Conversely, more generalized representations emerge in posterior regions like the posterior cingulate cortex, indicating a complex interplay between individuated and generalized processing of social information in the brain. These findings suggest that cortical regions typically associated with social cognition may compute different kinds of information when representing the distinctiveness of others.

Extensive work in personality neuroscience has shown mixed results in the ability to localize reliable relationships between personality traits and neuroimaging measures. However, recent work in translational neuroimaging has recognized that multifaceted psychological dispositions are not represented in discrete, highly localized brain areas. As such, standard univariate neuroimaging analyses may not be well-suited for capturing broad personality traits supported by distributed networks. The present study uses an out-of-sample predictive modeling approach to identify multivariate signatures of Big Five personality traits within the structural integrity of the brain’s white matter pathways using diffusion magnetic resonance imaging. In Study 1 (N = 491), we trained a ridge regression model to predict each of the Big Five traits and tested these models in an independent hold-out subsample. We found that models for both Neuroticism and Openness were significantly related to predictive accuracy in the hold-out sample. Study 2 (N = 108) applied the predictive models from Study 1 to an independent set of data collected on a different scanner and using a different Big Five scale. Here, we found that the model for Neuroticism remained a significant predictor of individual difference, providing evidence that this white matter signature of Neuroticism generalizes across differences in measurement and samples.

Humans spontaneously infer information about others to perceive the similarity in behaviors and traits among people in our social environments. However, evaluating the similarity among people can be achieved through multiple frames of reference: other-to-other differences (allocentric similarity) or self-to-other differences (egocentric similarity), which are difficult to dissociate with behavioral measures alone. Here, we use functional magnetic resonance imaging to test whether the similarity of brain response patterns when thinking of others and the self is predicted by models of allocentric and egocentric similarity in the trait-judgments of well-acquainted peers from 20 independent groups of people (total N = 108; within-subject design). Results show that both allocentric and egocentric similarity during person representation are reflected in brain response similarity patterns when thinking of others but do so differentially and in non-overlapping brain systems. Specifically, allocentric similarity was positively related to brain similarity patterns in distributed regions involved in social cognition as well as in regions involved in general non-social spatial reference frame encoding. Egocentric similarity was inversely related to brain response similarity but only within dorsal regions of the medial prefrontal cortex and anterior cingulate, suggesting that activation within this region is important for dissociating self-representation from that of similar others. These results suggest that the brain processes both kinds of reference frames independently to encode trait information about conspecifics that we use to represent person knowledge about other people within real-world social networks.

Human behavior is embedded in social networks. Certain characteristics of the positions that people occupy within these networks appear to be stable within individuals. Such traits likely stem in part from individual differences in how people tend to think and behave, which may be driven by individual differences in the neuroanatomy supporting socio-affective processing. To investigate this possibility, we reconstructed the full social networks of three graduate student cohorts ( N = 275; N = 279; N = 285), a subset of whom ( N = 112) underwent diffusion magnetic resonance imaging. Although no single tract in isolation appears to be necessary or sufficient to predict social network characteristics, distributed patterns of white matter microstructural integrity in brain networks supporting social and affective processing predict eigenvector centrality (how well-connected someone is to well-connected others) and brokerage (how much one connects otherwise unconnected others). Thus, where individuals sit in their real-world social networks is reflected in their structural brain networks. More broadly, these results suggest that the application of data-driven methods to neuroimaging data can be a promising approach to investigate how brains shape and are shaped by individuals’ positions in their real-world social networks.

Determining the generalizability of biological mechanisms supporting psychological constructs is a central goal of cognitive neuroscience. Self-esteem is a popular psychological construct that is associated with a variety of measures of mental health and life satisfaction. Recently, there has been interest in identifying biological mechanisms that support individual differences in self-esteem. Understanding the biological basis of self-esteem requires identifying predictive biomarkers of self-esteem that generalize across groups of individuals. Previous research using diffusion magnetic resonance imaging has shown that self-esteem is related to the integrity of structural connections linking frontostriatal brain systems involved in self-referential processing and reward. However, these findings were based on a small, relatively homogeneous group of participants. In the current study, we used an out-of-sample predictive modeling approach to generalize the results of the previous study to an independent sample of participants more than twice the size of the original study. We found that both linear univariate and multivariate machine learning models trained on frontostriatal integrity from the original data significantly predicted self-esteem in the independent dataset. These findings underscore the relationship between self-esteem and frontostriatal connectivity and suggest these results are robust to differences in scanning acquisition, analytic methods, and participant demographics.

Within our societies, humans form co-operative groups with diverse levels of relationship quality among individual group members. In establishing relationships with others, we use attitudes and beliefs about group members and the group as a whole to establish relationships with particular members of our social networks. However, we have yet to understand how brain responses to group members facilitate relationship quality between pairs of individuals. We address this here using a round-robin interpersonal perception paradigm in which each participant was both a perceiver and target for every other member of their group in a set of 20 unique groups of between 5 and 6 members in each (total N = 111). Using functional magnetic resonance imaging, we show that measures of social relationship strength modulate the brain-to-brain multivoxel similarity patterns between pairs of participants’ responses when perceiving other members of their group in regions of the brain implicated in social cognition. These results provide evidence for a brain mechanism of social cognitive processes serving interpersonal relationship strength among group members.

Marek et al. analyzed three very large magnetic resonance imaging (MRI) datasets and concluded that thousands of participants are necessary to ensure replicable results in “brain-wide associations studies,” which they defined as “studies of the associations between common inter-individual variability in human brain structure/function and cognition or psychiatric symptomatology.” This conclusion overgeneralizes the implications of their findings and is likely to have an unwarranted chilling effect on neuroimaging research focused on individual differences, preventing good research with samples in the hundreds from being funded and conducted. To fend off these negative consequences, we explain why their conclusion is not fully justified, discuss methods that can yield larger effects, and suggest practical guidelines for sample size, recognizing the potential utility of samples in the hundreds.

In this paper we consider the hypothesis, based on our human and mouse studies, that white matter connectivity can be altered by appropriate stimulation at the theta frequency [1,2]. In one study we used optogenetics to change the output of cells in the mouse anterior cingulate cortex (ACC) at 1, 8 or 40Hz. The g ratio (diameter of axon/ axon diameter + myelin) was significantly reduced in brain areas near the ACC, but not in areas far from stimulation [2]. The improved connectivity correlated with behaviors indicating reduced anxiety [3].

The medial prefrontal cortex (MPFC) is among the most consistently implicated brain regions in social and affective neuroscience. Yet, this region is also highly functionally heterogeneous across many domains and has diverse patterns of connectivity. The extent to which the communication of functional networks in this area is facilitated by its underlying structural connectivity fingerprint is critical for understanding how psychological phenomena are represented within this region. In the current study, we combined diffusion magnetic resonance imaging and probabilistic tractography with large-scale meta-analysis to investigate the degree to which the functional co-activation patterns of the MPFC is reflected in its underlying structural connectivity. Using unsupervised machine learning techniques, we compared parcellations between the two modalities and found congruence between parcellations at multiple spatial scales. Additionally, using connectivity and coactivation similarity analyses, we found high correspondence in voxel-to-voxel similarity between each modality across most, but not all, subregions of the MPFC. These results provide evidence that meta-analytic functional co-activation patterns are meaningfully constrained by underlying neuroanatomical connectivity and provide convergent evidence of distinct subregions within the MPFC involved in affective processing and social cognition.

During narrative experiences, identification with a fictional character can alter one's attitudes and self-beliefs to be more similar to those of the character. The ventral medial prefrontal cortex (vMPFC) is a brain region that shows increased activity when introspecting about the self but also when thinking of close friends. Here, we test whether identification with fictional characters is associated with increased neural overlap between self and fictional others. Nineteen fans of the HBO series Game of Thrones performed trait evaluations for the self, 9 real-world friends and 9 fictional characters during functional neuroimaging. Overall, the participants showed a larger response in the vMPFC for self compared to friends and fictional others. However, among the participants higher in trait identification, we observed a greater neural overlap in the vMPFC between self and fictional characters. Moreover, the magnitude of this association was greater for the character that participants reported feeling closest to/liked the most as compared to those they felt least close to/liked the least. These results suggest that identification with fictional characters leads people to incorporate these characters into their self-concept: the greater the immersion into experiences of 'becoming' characters, the more accessing knowledge about characters resembles accessing knowledge about the self.

Humans continually form and update impressions of each other’s identities based on the disclosure of thoughts, feelings, and beliefs. At the same time, individuals also have specific beliefs and knowledge about their own self-concept. Over a decade of social neuroscience research has shown that retrieving information about the self and about other persons recruits similar areas of the medial prefrontal cortex (MPFC), however it remains unclear if an individual’s neural representation of self is reflected in the brains of well-known others or if instead the two representations share no common relationship. Here we examined this question in a tight-knit network of friends as they engaged in a round-robin trait evaluation task in which each participant was both perceiver and target for every other participant and in addition also evaluated their self. Using functional magnetic resonance imaging and a multilevel modeling approach, we show that multivoxel brain activity patterns in the MPFC during a person’s self-referential thought are correlated with those of friends when thinking of that same person. Moreover, the similarity of neural self/other patterns was itself positively associated with the similarity of self/other trait judgments ratings as measured behaviorally in a separate session. These findings suggest that accuracy in person perception may be predicated on the degree to which the brain activity pattern associated with an individual thinking about their own self-concept is similarly reflected in the brains of others.