Common brain networks underlying human social interactions: Evidence from large-scale neuroimaging meta-analysis

Dorsomedial and ventromedial prefrontal cortex lesions differentially impact social influence and temporal discounting

The medial prefrontal cortex (mPFC) has long been associated with economic and social decision-making in neuroimaging studies. Several debates question whether different ventral mPFC (vmPFC) and dorsal mPFC (dmPFC) regions have specific functions or whether there is a gradient supporting social and nonsocial cognition. Here, we tested an unusually large sample of rare participants with focal damage to the mPFC (N = 33), individuals with lesions elsewhere (N = 17), and healthy controls (N = 71) (total N = 121). Participants completed a temporal discounting task to estimate their baseline discounting preferences before learning the preferences of two other people, one who was more temporally impulsive and one more patient. We used Bayesian computational models to estimate baseline discounting and susceptibility to social influence after learning others' economic preferences. mPFC damage increased susceptibility to impulsive social influence compared to healthy controls and increased overall susceptibility to social influence compared to those with lesions elsewhere. Importantly, voxel-based lesion-symptom mapping (VLSM) of computational parameters showed that this heightened susceptibility to social influence was attributed specifically to damage to the dmPFC (area 9; permutation-based threshold-free cluster enhancement (TFCE) p < 0.025). In contrast, lesions in the vmPFC (areas 13 and 25) and ventral striatum were associated with a preference for seeking more immediate rewards (permutation-based TFCE p < 0.05). We show that the dmPFC is causally implicated in susceptibility to social influence, with distinct ventral portions of mPFC involved in temporal discounting. These findings provide causal evidence for sub-regions of the mPFC underpinning fundamental social and cognitive processes.

Altered Patterns of Dynamic Functional Connectivity Underpin Reduced Expressions of Social-Emotional Reciprocity in Autistic Adults

To identify the neurocognitive mechanisms underpinning the social difficulties that characterize autism, we performed functional magnetic resonance imaging on pairs of autistic and non-autistic adults simultaneously whilst they interacted with one another on the iterated Ultimatum Game (iUG)-an interactive task that emulates the reciprocal characteristic of naturalistic interpersonal exchanges. Two age-matched sets of male-male dyads were investigated: 16 comprised an autistic Responder and a non-autistic Proposer, and 19 comprised non-autistic pairs of Responder and Proposer. Players' round-by-round behavior on the iUG was modeled as reciprocal choices, and dynamic functional connectivity (dFC) was measured to identify the neural mechanisms underpinning reciprocal behaviors. Behavioral expressions of reciprocity were significantly reduced in autistic compared with non-autistic Responders, yet no such differences were observed between the non-autistic Proposers in either set of dyads. Furthermore, we identified latent dFC states with temporal properties associated with reciprocity. Autistic interactants spent less time in brain states characterized by dynamic inter-network integration and segregation among the Default Mode Network and cognitive control networks, suggesting that their reduced expressions of social-emotional reciprocity reflect less efficient reconfigurations among brain networks supporting flexible cognition and behavior. These findings advance our mechanistic understanding of the social difficulties characterizing autism.

The Brain That Understands Diversity: A Pilot Study Focusing on the Triple Network

Background/Objectives: Interest in diversity is growing worldwide. Today, an understanding and social acceptance of diverse people is becoming increasingly important. Therefore, in this study, we aimed to clarify the relationship between an individual's gray matter volume (GMV), which is thought to reflect brain health, and their understanding of diversity (gender, sexuality (LGBTQ), and origin). Methods: GMV was determined as the value of the Gray Matter Brain Healthcare Quotient (GM-BHQ) based on MRI image analysis. Meanwhile, participants' understanding and acceptance of diversity was calculated based on their answers to the psychological questions included in the World Values Survey Wave 7 (WVS7). Results: Our analysis indicated that, in the group of participants with the highest understanding of diversity (PHUD. n = 11), not only the GMV at the whole brain level (t = 2.587, p = 0.027, Cohen's d = 0.780) but also the GMV of the central executive network (CEN: t = 2.700, p= 0.022, Cohen's d = 0.814) and saliency network (SN: t = 3.100, p = 0.011, Cohen's d = 0.935) were shown to be significantly higher than the theoretical value estimated from sex, age, and BMI at the 5% level. In addition, the GMV of the default mode network (DMN: t = 2.063, p = 0.066, Cohen's d = 0.622) was also higher than the theoretical value at the 10% level. Meanwhile, in the group of others (n = 10), there was no significant difference from the theoretical value. These differences between PHUD and others were also observed when comparing the two with and without controlling for educational and occupational covariates at the 5% or 10% levels. Conclusions: These results suggest that understanding diversity requires a healthy brain, centered on three networks that govern rational judgment, emotion regulation, other-awareness, self-awareness, and the valuing of actions. This is the first study to show that brain structure is related to an understanding and acceptance of the diversity of people.

Neural Basis of Pain Empathy Dysregulations in Mental Disorders: A Preregistered Neuroimaging Meta-Analysis

BACKGROUND

Pain empathy represents a fundamental building block of several social functions, which have been demonstrated to be impaired across various mental disorders by accumulating evidence from case-control functional magnetic resonance imaging studies. However, it remains unclear whether the dysregulations are underpinned by robust neural alterations across mental disorders.

METHODS

This study utilized coordinate-based meta-analyses to quantitatively determine robust markers of altered pain empathy across mental disorders. To support the interpretation of the findings, exploratory network-level and behavioral meta-analyses were conducted.

RESULTS

Quantitative analysis of 11 case-control functional magnetic resonance imaging studies with data from 296 patients and 229 control participants revealed that patients with mental disorders exhibited increased pain empathic reactivity in the left anterior cingulate gyrus, adjacent medial prefrontal cortex, and right middle temporal gyrus but decreased activity in the left cerebellum IV/V and left middle occipital gyrus compared with control participants. The hyperactive regions showed network-level interactions with the core default mode network and were associated with affective and social cognitive domains.

CONCLUSIONS

The findings suggest that pain empathic alterations across mental disorders are underpinned by excessive empathic reactivity in brain systems involved in empathic distress and social processes, highlighting a shared therapeutic target to normalize basal social dysfunctions in mental disorders.

Distinct neural computations scale the violation of expected reward and emotion in social transgressions

Traditional decision-making models conceptualize humans as adaptive learners utilizing the differences between expected and actual rewards (prediction errors, PEs) to maximize outcomes, but rarely consider the influence of violations of emotional expectations (emotional PEs) and how it differs from reward PEs. Here, we conducted a fMRI experiment (n = 43) using a modified Ultimatum Game to examine how reward and emotional PEs affect punishment decisions in terms of rejecting unfair offers. Our results revealed that reward relative to emotional PEs exerted a stronger prediction to punishment decisions. On the neural level, the left dorsomedial prefrontal cortex (dmPFC) was strongly activated during reward receipt whereas the emotions engaged the bilateral anterior insula. Reward and emotional PEs were also encoded differently in brain-wide multivariate patterns, with a more sensitive neural signature observed within fronto-insular circuits for reward PE. We further identified a fronto-insular network encompassing the left anterior cingulate cortex, bilateral insula, left dmPFC and inferior frontal gyrus that encoded punishment decisions. In addition, a stronger fronto-insular pattern expression under reward PE predicted more punishment decisions. These findings underscore that reward and emotional violations interact to shape decisions in complex social interactions, while the underlying neurofunctional PEs computations are distinguishable.

The neurosociological paradigm of the metaverse

Metaverse integrates people into the virtual world, and challenges depend on advances in human, technological, and procedural dimensions. Until now, solutions to these challenges have not involved extensive neurosociological research. The study explores the pioneering neurosociological paradigm in metaverse, emphasizing its potential to revolutionize our understanding of social interactions through advanced methodologies such as hyperscanning and interbrain synchrony. This convergence presents unprecedented opportunities for neurotypical and neurodivergent individuals due to technology personalization. Traditional face-to-face, interbrain coupling, and metaverse interactions are empirically substantiated. Biomarkers of social interaction as feedback between social brain networks and metaverse is presented. The innovative contribution of findings to the broader literature on metaverse and neurosociology is substantiated. This article also discusses the ethical aspects of integrating the neurosociological paradigm into the metaverse.

Causal mechanisms of quadruple networks in pediatric bipolar disorder

BACKGROUND

Pediatric bipolar disorder (PBD) is characterized by abnormal functional connectivity among distributed brain regions. Increasing evidence suggests a role for the limbic network (LN) and the triple network model in the pathophysiology of bipolar disorder (BD). However, the specific relationship between the LN and the triple network in PBD remains unclear. This study aimed to investigate the aberrant causal connections among these four core networks in PBD.

METHOD

Resting-state functional MRI scans from 92 PBD patients and 40 healthy controls (HCs) were analyzed. Dynamic Causal Modeling (DCM) was employed to assess effective connectivity (EC) among the four core networks. Parametric empirical Bayes (PEB) analysis was conducted to identify ECs associated with group differences, as well as depression and mania severity. Leave-one-out cross-validation (LOOCV) was used to test predictive accuracy.

RESULT

Compared to HCs, PBD patients exhibited primarily excitatory bottom-up connections from the LN to the salience network (SN) and bidirectional excitatory connections between the default mode network (DMN) and SN. In PBD, top-down connectivity from the triple network to the LN was excitatory in individuals with higher depression severity but inhibitory in those with higher mania severity. LOOCV identified dysconnectivity circuits involving the caudate and hippocampus as being associated with mania and depression severity, respectively.

CONCLUSIONS

Disrupted bottom-up connections from the LN to the triple network distinguish PBD patients from healthy controls, while top-down disruptions from the triple network to LN relate to mood state differences. These findings offer insight into the neural mechanisms of PBD.

An Active-Inference Approach to Second-Person Neuroscience

Social neuroscience has often been criticized for approaching the investigation of the neural processes that enable social interaction and cognition from a passive, detached, third-person perspective, without involving any real-time social interaction. With the emergence of second-person neuroscience, investigators have uncovered the unique complexity of neural-activation patterns in actual, real-time interaction. Social cognition that occurs during social interaction is fundamentally different from that unfolding during social observation. However, it remains unclear how the neural correlates of social interaction are to be interpreted. Here, we leverage the active-inference framework to shed light on the mechanisms at play during social interaction in second-person neuroscience studies. Specifically, we show how counterfactually rich mutual predictions, real-time bodily adaptation, and policy selection explain activation in components of the default mode, salience, and frontoparietal networks of the brain, as well as in the basal ganglia. We further argue that these processes constitute the crucial neural processes that underwrite bona fide social interaction. By placing the experimental approach of second-person neuroscience on the theoretical foundation of the active-inference framework, we inform the field of social neuroscience about the mechanisms of real-life interactions. We thereby contribute to the theoretical foundations of empirical second-person neuroscience.

Tipping the balance between fairness and efficiency through temporoparietal stimulation

Maximizing the welfare of society requires distributing goods between groups of people with different preferences. Such decisions are difficult because different moral principles impose irreconcilable solutions. For example, utilitarian efficiency (maximize overall outcome across individuals) may need trade-off against Rawlsian fairness norms (maximize the outcome for the worst-off individual). We identify a brain mechanism enabling decision-makers to solve such trade-offs between efficiency and fairness using separate neuroimaging and sham-controlled brain stimulation experiments. As activity in the temporoparietal junction (TPJ) increases, people are more likely to implement the Rawlsian fairness criterion rather than efficiency or inequality concerns. Strikingly, reducing TPJ excitability with brain stimulation reduces the concern for fairness in fairness-efficiency trade-offs. Moreover, the reduced fairness concerns statistically relate to stimulation-induced reductions in perspective-taking skills as measured in a separate task. Together, our findings not only reveal the neural underpinning of efficiency-fairness trade-offs but also recast the role of TPJ in social decision-making by showing that its perspective-taking function serves to promote fairness for the worst-off rather than efficiency or equality.

The Neurocomputational Mechanism Underlying Decision-Making on Unfairness to Self and Others

Fairness is a fundamental value in human societies, with individuals concerned about unfairness both to themselves and to others. Nevertheless, an enduring debate focuses on whether self-unfairness and other-unfairness elicit shared or distinct neuropsychological processes. To address this, we combined a three-person ultimatum game with computational modeling and advanced neuroimaging analysis techniques to unravel the behavioral, cognitive, and neural patterns underlying unfairness to self and others. Our behavioral and computational results reveal a heightened concern among participants for self-unfairness over other-unfairness. Moreover, self-unfairness consistently activates brain regions such as the anterior insula, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex, spanning various spatial scales that encompass univariate activation, local multivariate patterns, and whole-brain multivariate patterns. These regions are well-established in their association with emotional and cognitive processes relevant to fairness-based decision-making. Conversely, other-unfairness primarily engages the middle occipital gyrus. Collectively, our findings robustly support distinct neurocomputational signatures between self-unfairness and other-unfairness.

Large-scale meta-analyses and network analyses of neural substrates underlying human escalated aggression

Escalated aggression represents a frequent and severe form of violence, sometimes manifesting as antisocial behavior. Driven by the pressures of modern life, escalated aggression is of particular concern due to its rising prevalence and its destructive impact on both individual well-being and socioeconomic stability. However, a consistent neural circuitry underpinning it remains to be definitively identified. Here, we addressed this issue by comparing brain alterations between individuals with escalated aggression and those without such behavioral manifestations. We first conducted a meta-analysis to synthesize previous neuroimaging studies on functional and structural alterations of escalated aggression (325 experiments, 2997 foci, 16,529 subjects). Following-up network and functional decoding analyses were conducted to provide quantitative characterizations of the identified brain regions. Our results revealed that brain regions constantly involved in escalated aggression were localized in the subcortical network (amygdala and lateral orbitofrontal cortex) associated with emotion processing, the default mode network (dorsal medial prefrontal cortex and middle temporal gyrus) associated with mentalizing, and the salience network (anterior cingulate cortex and anterior insula) associated with cognitive control. These findings were further supported by additional meta-analyses on emotion processing, mentalizing, and cognitive control, all of which showed conjunction with the brain regions identified in the escalated aggression. Together, these findings advance the understanding of the risk biomarkers of escalated aggressive populations and refine theoretical models of human aggression.

Common and separable neural alterations in adult and adolescent depression - Evidence from neuroimaging meta-analyses

Depression is a highly prevalent and debilitating mental disorder that often begins in adolescence. However, it remains unclear whether adults and adolescents with depression exhibit common or distinct brain dysfunctions during reward processing. We aimed to identify common and separable neurofunctional alterations during receipt of rewards and brain structure in adolescents and adults with depression. A coordinate-based meta-analysis was employed using Seed-based d mapping with permutation of subject images (SDM-PSI). Compared with healthy controls, both age groups exhibited common activity decreases in the right striatum (putamen, caudate) and subgenual ACC. Adults with depression showed decreased reactivity in the right putamen and subgenual ACC, while adolescents with depression showed decreased activity in the left mid cingulate, right caudate but increased reactivity in the right postcentral gyrus. This meta-analysis revealed shared (caudate) and separable (putamen and mid cingulate cortex) reward-related alterations in adults and adolescents with depression. The findings suggest age-specific neurofunctional alterations and stress the importance of adolescent-specific interventions that target social functions.

Brain responses to self- and other- unfairness under resource distribution context: Meta-analysis of fMRI studies

Under resource distribution context, individuals have a strong aversion to unfair treatment not only toward themselves but also toward others. However, there is no clear consensus regarding the commonality and distinction between these two types of unfairness. Moreover, many neuroimaging studies have investigated how people evaluate and respond to unfairness in the abovementioned two contexts, but the consistency of the results remains to be investigated. To resolve these two issues, we sought to summarize existing findings regarding unfairness to self and others and to further elucidate the neural underpinnings related to distinguishing evaluation and response processes through meta-analyses of previous neuroimaging studies. Our results indicated that both types of unfairness consistently activate the affective and conflict-related anterior insula (AI) and dorsal anterior cingulate cortex/supplementary motor area (dACC/SMA), but the activations related to unfairness to self appeared stronger than those related to others, suggesting that individuals had negative reactions to both unfairness and a greater aversive response toward unfairness to self. During the evaluation process, unfairness to self activated the bilateral AI, dACC, and right dorsolateral prefrontal cortex (DLPFC), regions associated with unfairness aversion, conflict, and cognitive control, indicating reactive, emotional and automatic responses. In contrast, unfairness to others activated areas associated with theory of mind, the inferior parietal lobule and temporoparietal junction (IPL-TPJ), suggesting that making rational judgments from the perspective of others was needed. During the response, unfairness to self activated the affective-related left AI and striatum, whereas unfairness to others activated cognitive control areas, the left DLPFC and the thalamus. This indicated that the former maintained the traits of automaticity and emotionality, whereas the latter necessitated cognitive control. These findings provide a fine-grained description of the common and distinct neurocognitive mechanisms underlying unfairness to self and unfairness to others. Overall, this study not only validates the inequity aversion model but also provides direct evidence of neural mechanisms for neurobiological models of fairness.

Corticostriatal responses to social reward are linked to trait reward sensitivity and subclinical substance use in young adults

Aberrant levels of reward sensitivity have been linked to substance use disorder and are characterized by alterations in reward processing in the ventral striatum (VS). Less is known about how reward sensitivity and subclinical substance use relate to striatal function during social rewards (e.g. positive peer feedback). Testing this relation is critical for predicting risk for development of substance use disorder. In this pre-registered study, participants (N = 44) underwent fMRI while completing well-matched tasks that assess neural response to reward in social and monetary domains. Contrary to our hypotheses, aberrant reward sensitivity blunted the relationship between substance use and striatal activation during receipt of rewards, regardless of domain. Moreover, exploratory whole-brain analyses showed unique relations between substance use and social rewards in temporoparietal junction. Psychophysiological interactions demonstrated that aberrant reward sensitivity is associated with increased connectivity between the VS and ventromedial prefrontal cortex during social rewards. Finally, we found that substance use was associated with decreased connectivity between the VS and dorsomedial prefrontal cortex for social rewards, independent of reward sensitivity. These findings demonstrate nuanced relations between reward sensitivity and substance use, even among those without substance use disorder, and suggest altered reward-related engagement of cortico-VS responses as potential predictors of developing disordered behavior.

Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

The relationship between the resting state functional connectivity and social cognition in schizophrenia: Results from the Italian Network for Research on Psychoses

Deficits in social cognition (SC) interfere with recovery in schizophrenia (SZ) and may be related to resting state brain connectivity. This study aimed at assessing the alterations in the relationship between resting state functional connectivity and the social-cognitive abilities of patients with SZ compared to healthy subjects. We divided the brain into 246 regions of interest (ROI) following the Human Healthy Volunteers Brainnetome Atlas. For each participant, we calculated the resting-state functional connectivity (rsFC) in terms of degree centrality (DC), which evaluates the total strength of the most powerful coactivations of every ROI with all other ROIs during rest. The rs-DC of the ROIs was correlated with five measures of SC assessing emotion processing and mentalizing in 45 healthy volunteers (HVs) chosen as a normative sample. Then, controlling for symptoms severity, we verified whether these significant associations were altered, i.e., absent or of opposite sign, in 55 patients with SZ. We found five significant differences between SZ patients and HVs: in the patients' group, the correlations between emotion recognition tasks and rsFC of the right entorhinal cortex (R-EC), left superior parietal lobule (L-SPL), right caudal hippocampus (R-c-Hipp), and the right caudal (R-c) and left rostral (L-r) middle temporal gyri (MTG) were lost. An altered resting state functional connectivity of the L-SPL, R-EC, R-c-Hipp, and bilateral MTG in patients with SZ may be associated with impaired emotion recognition. If confirmed, these results may enhance the development of non-invasive brain stimulation interventions targeting those cerebral regions to reduce SC deficit in SZ.

One step too far: social cerebellum in norm-violating navigation

Social norms are pivotal in guiding social interactions. The current study investigated the potential contribution of the posterior cerebellum, a critical region involved in perceiving and comprehending the sequential dynamics of social actions, in detecting actions that either conform to or deviate from social norms. Participants engaged in a goal-directed task in which they observed others navigating towards a goal. The trajectories demonstrated either norm-violating (trespassing forbidden zones) or norm-following behaviors (avoiding forbidden zones). Results revealed that observing social norm-violating behaviors engaged the bilateral posterior cerebellar Crus 2 and the right temporoparietal junction (TPJ) from the mentalizing network, and the parahippocampal gyrus (PHG) to a greater extent than observing norm-following behaviors. These mentalizing regions were also activated when comparing social sequences against non-social and non-sequential control conditions. Reproducing norm-violating social trajectories observed earlier, activated the left cerebellar Crus 2 and the right PHG compared to reproducing norm-following trajectories. These findings illuminate the neural mechanisms in the cerebellum associated with detecting norm transgressions during social navigation, emphasizing the role of the posterior cerebellum in detecting and signaling deviations from anticipated sequences.

Brain Activation for Social Cognition and Emotion Processing Tasks in Borderline Personality Disorder: A Meta-Analysis of Neuroimaging Studies

The present meta-analysis summarizes brain activation for social cognition and emotion-processing tasks in borderline personality disorder (BPD). We carried out two meta-analyses to elaborate on commonalities and potential differences between the two types of tasks. In the first meta-analysis, we implemented a more liberal strategy for task selection (including social and emotional content). The results confirmed previously reported hyperactivations in patients with BPD in the bilateral amygdala and prefrontal cortex and hypoactivations in bilateral inferior frontal gyri. When applying a stricter approach to task selection, focusing narrowly on social cognition tasks, we only found activation in prefrontal areas, particularly in the anterior cingulate and ventromedial prefrontal cortex. We review the role of these areas in social cognition in healthy adults, suggesting that the observed BPD hyperactivations may reflect an overreliance on self-related thought in social cognition.

Corticostriatal Responses to Social Reward are Linked to Trait Reward Sensitivity and Subclinical Substance Use in Young Adults

Aberrant levels of reward sensitivity have been linked to substance use disorder and are characterized by alterations in reward processing in the ventral striatum (VS). Less is known about how reward sensitivity and subclinical substance use relate to striatal function during social rewards (e.g., positive peer feedback). Testing this relation is critical for predicting risk for development of substance use disorder. In this pre-registered study, participants (N=44) underwent fMRI while completing well-matched tasks that assess neural response to reward in social and monetary domains. Contrary to our hypotheses, aberrant reward sensitivity blunted the relationship between substance use and striatal activation during receipt of rewards, regardless of domain. Moreover, exploratory whole-brain analyses showed unique relations between substance use and social rewards in temporoparietal junction. Psychophysiological interactions demonstrated that aberrant reward sensitivity is associated with increased connectivity between the VS and ventromedial prefrontal cortex during social rewards. Finally, we found that substance use was associated with decreased connectivity between the VS and dorsomedial prefrontal cortex for social rewards, independent of reward sensitivity. These findings demonstrate nuanced relations between reward sensitivity and substance use, even among those without substance use disorder, and suggest altered reward-related engagement of cortico-VS responses as potential predictors of developing disordered behavior.

The science of justice: The neuropsychology of social punishment

The social punishment (SP) of norm violations has received much attention across multiple disciplines. However, current models of SP fail to consider the role of motivational processes, and none can explain the observed behavioral and neuropsychological differences between the two recognized forms of SP: second-party punishment (2PP) and third-party punishment (3PP). After reviewing the literature giving rise to the current models of SP, we propose a unified model of SP which integrates general psychological descriptions of decision-making as a confluence of affect, cognition, and motivation, with evidence that SP is driven by two main factors: the amount of harm (assessed primarily in the salience network) and the norm violator's intention (assessed primarily in the default-mode and central-executive networks). We posit that motivational differences between 2PP and 3PP, articulated in mesocorticolimbic pathways, impact final SP by differentially impacting the assessments of harm and intention done in these domain-general large-scale networks. This new model will lead to a better understanding of SP, which might even improve forensic, procedural, and substantive legal practices.

Shared Minds, Shared Feedback: tracing the influence of parental feedback on shared neural patterns

Parental feedback affects children in multiple ways. However, little is known about how children, family, and feedback types affect parental feedback neural mechanisms. The current study used functional near-infrared spectroscopy-based hyperscanning to observe 47 mother-daughter pairs's (mean age of mothers: 35.95 ± 3.99 yr old; mean age of daughters: 6.97 ± 0.75 yr old) brain synchronization in a jigsaw game under various conditions. Between parental negative feedback and praise conditions, mother-daughter brain in supramarginal gyrus, left dorsolateral prefrontal cortex, right inferior frontal gyrus, and right primary somatic (S1) differed. When criticized, conformity family-communication-patterned families had much worse brain synchronization in S1, left dorsolateral prefrontal cortex, and right Wernicke's region than conversational families. Resilient children had better mother-child supramarginal gyrus synchronicity under negative feedback. This study supports the importance of studying children's neurological development in nurturing environments to assess their psychological development.

Neurocomputational Substrates Underlying the Effect of Identifiability on Third-Party Punishment

The identifiable target effect refers to the preference for helping identified victims and punishing identifiable perpetrators compared with equivalent but unidentifiable counterparts. The identifiable target effect is often attributed to the heightened moral emotions evoked by identified targets. However, the specific neurocognitive processes that mediate and/or modulate this effect remain largely unknown. Here, we combined a third-party punishment game with brain imaging and computational modeling to unravel the neurocomputational underpinnings of the identifiable transgressor effect. Human participants (males and females) acted as bystanders and punished identified or anonymous wrongdoers. Participants were more punitive toward identified wrongdoers than anonymous wrongdoers because they took a vicarious perspective of victims and adopted lower reference points of inequity (i.e., more stringent norms) in the identified context than in the unidentified context. Accordingly, there were larger activity of the ventral anterior insula, more distinct multivariate neural patterns in the dorsal anterior insula and dorsal anterior cingulate cortex, and lower strength between ventral anterior insula and dorsolateral PFC and between dorsal anterior insula and ventral striatum connectivity in response to identified transgressors than anonymous transgressors. These findings implicate the interplay of expectancy violations, emotions, and self-interest in the identifiability effect. Last, individual differences in the identifiability effect were associated with empathic concern/social dominance orientation, activity in the precuneus/cuneus and temporo-parietal junction, and intrinsic functional connectivity of the dorsolateral PFC. Together, our work is the first to uncover the neurocomputational processes mediating identifiable transgressor effect and to characterize psychophysiological profiles modulating the effect.SIGNIFICANCE STATEMENT The identifiable target effect, more help to identified victims or stronger punishment to identifiable perpetrators, is common in daily life. We examined the neurocomputational mechanisms mediating/modulating the identifiability effect on third-party punishment by bridging literature from economics and cognitive neuroscience. Our findings reveal that identifiable transgressor effect is mediated by lower reference points of inequity (i.e., more stringent norms), which might be associated with a stronger involvement of the emotion processes and a weaker engagement of the analytic/deliberate processes. Furthermore, personality traits, altered brain activity, and intrinsic functional connectivity contribute to the individual variance in the identifiability effect. Overall, our study advances the understanding of the identifiability effect by shedding light on its component processes and modulating factors.

MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks

Metal-organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility-a MOF key performance indicator-of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host-guest interaction studies.

Social and non-social working memory in neurodegeneration

Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both working memory and social deficits. We assessed working memory domain-specificity across behavioral, electrophysiological, and neuroimaging dimensions in 245 participants. A novel working memory task involving social and non-social stimuli with three load levels was assessed across controls and different neurodegenerative conditions with recognized impairments in: working memory and social cognition (behavioral-variant frontotemporal dementia); general cognition (Alzheimer's disease); and unspecific patterns (Parkinson's disease). We also examined resting-state theta oscillations and functional connectivity correlates of working memory domain-specificity. Results in controls and all groups together evidenced increased working memory demands for social stimuli associated with frontocinguloparietal theta oscillations and salience network connectivity. Canonical frontal theta oscillations and executive-default mode network anticorrelation indexed non-social stimuli. Behavioral-variant frontotemporal dementia presented generalized working memory deficits related to posterior theta oscillations, with social stimuli linked to salience network connectivity. In Alzheimer's disease, generalized working memory impairments were related to temporoparietal theta oscillations, with non-social stimuli linked to the executive network. Parkinson's disease showed spared working memory performance and canonical brain correlates. Findings support a social-specific working memory and related disease-selective pathophysiological mechanisms.

The interacting brain: Dynamic functional connectivity among canonical brain networks dissociates cooperative from competitive social interactions

We spend much our lives interacting with others in various social contexts. Although we deal with this myriad of interpersonal exchanges with apparent ease, each one relies upon a broad array of sophisticated cognitive processes. Recent research suggests that the cognitive operations supporting interactive behaviour are themselves underpinned by several canonical functional brain networks (CFNs) that integrate dynamically with one another in response to changing situational demands. Dynamic integrations among these CFNs should therefore play a pivotal role in coordinating interpersonal behaviour. Further, different types of interaction should present different demands on cognitive systems, thereby eliciting distinct patterns of dynamism among these CFNs. To investigate this, the present study performed functional magnetic resonance imaging (fMRI) on 30 individuals while they interacted with one another cooperatively or competitively. By applying a novel combination of analytical techniques to these brain imaging data, we identify six states of dynamic functional connectivity characterised by distinct patterns of integration and segregation among specific CFNs that differ systematically between these opposing types of interaction. Moreover, applying these same states to fMRI data acquired from an independent sample engaged in the same kinds of interaction, we were able to classify interpersonal exchanges as cooperative or competitive. These results provide the first direct evidence for the systematic involvement of CFNs during social interactions, which should guide neurocognitive models of interactive behaviour and investigations into biomarkers for the interpersonal dysfunction characterizing many neurological and psychiatric disorders.

Fear of missing out (FOMO) associates with reduced cortical thickness in core regions of the posterior default mode network and higher levels of problematic smartphone and social media use

BACKGROUND AND AIMS

Fear of missing out (FOMO) promotes the desire or urge to stay continuously connected with a social reference group and updated on their activities, which may result in escalating and potentially addictive smartphone and social media use. The present study aimed to determine whether the neurobiological basis of FOMO encompasses core regions of the reward circuitry or social brain, and associations with levels of problematic smartphone or social media use.

METHODS

We capitalized on a dimensional neuroimaging approach to examine cortical thickness and subcortical volume associations in a sample of healthy young individuals (n = 167). Meta-analytic network and behavioral decoding analyses were employed to further characterize the identified regions.

RESULTS

Higher levels of FOMO associated with lower cortical thickness in the right precuneus. In contrast, no associations between FOMO and variations in striatal morphology were observed. Meta-analytic decoding revealed that the identified precuneus region exhibited a strong functional interaction with the default mode network (DMN) engaged in social cognitive and self-referential domains.

DISCUSSION AND CONCLUSIONS

Together the present findings suggest that individual variations in FOMO are associated with the brain structural architecture of the right precuneus, a core hub within a large-scale functional network resembling the DMN and involved in social and self-referential processes. FOMO may promote escalating social media and smartphone use via social and self-referential processes rather than reward-related processes per se.

Personalized Neural Networks Underlie Individual Differences in Ethnic Identity Exploration and Resolution

This study examined how ethnic identity relates to large-scale brain networks implicated in social interactions, social cognition, self-definition, and cognitive control. Group Iterative Multiple Model Estimation (GIMME) was used to create sparse, person-specific networks among the default mode and frontoparietal resting-state networks in a diverse sample of 104 youths aged 17-21. Links between neural density (i.e., number of connections within and between these networks) and ethnic identity exploration and resolution were evaluated in the full sample. Ethnic identity resolution was positively related to frontoparietal network density, suggesting that having clarity about one's ethnic group membership is associated with brain network organization reflecting cognitive control. These findings help fill a critical knowledge gap about the neural underpinnings of ethnic identity.

Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion

Humans synchronize with one another to foster successful interactions. Here, we use a multimodal data fusion approach with the aim of elucidating the neurobiological mechanisms by which interpersonal neural synchronization (INS) occurs. Our meta-analysis of 22 functional magnetic resonance imaging and 69 near-infrared spectroscopy hyperscanning experiments (740 and 3721 subjects) revealed robust brain regional correlates of INS in the right temporoparietal junction and left ventral prefrontal cortex. Integrating this meta-analytic information with public databases, biobehavioral and brain-functional association analyses suggested that INS involves sensory-integrative hubs with functional connections to mentalizing and attention networks. On the molecular and genetic levels, we found INS to be associated with GABAergic neurotransmission and layer IV/V neuronal circuits, protracted developmental gene expression patterns, and disorders of neurodevelopment. Although limited by the indirect nature of phenotypic-molecular association analyses, our findings generate new testable hypotheses on the neurobiological basis of INS.

Spatio-temporal dynamics of oscillatory brain activity during the observation of actions and interactions between point-light agents

Predicting actions from non-verbal cues and using them to optimise one's response behaviour (i.e. interpersonal predictive coding) is essential in everyday social interactions. We aimed to investigate the neural correlates of different cognitive processes evolving over time during interpersonal predictive coding. Thirty-nine participants watched two agents depicted by moving point-light stimuli while an electroencephalogram (EEG) was recorded. One well-recognizable agent performed either a 'communicative' or an 'individual' action. The second agent either was blended into a cluster of noise dots (i.e. present) or was entirely replaced by noise dots (i.e. absent), which participants had to differentiate. EEG amplitude and coherence analyses for theta, alpha and beta frequency bands revealed a dynamic pattern unfolding over time: Watching communicative actions was associated with enhanced coupling within medial anterior regions involved in social and mentalising processes and with dorsolateral prefrontal activation indicating a higher deployment of cognitive resources. Trying to detect the agent in the cluster of noise dots without having seen communicative cues was related to enhanced coupling in posterior regions for social perception and visual processing. Observing an expected outcome was modulated by motor system activation. Finally, when the agent was detected correctly, activation in posterior areas for visual processing of socially relevant features was increased. Taken together, our results demonstrate that it is crucial to consider the temporal dynamics of social interactions and of their neural correlates to better understand interpersonal predictive coding. This could lead to optimised treatment approaches for individuals with problems in social interactions.

Risk-taking in the human brain: An activation likelihood estimation meta-analysis of the balloon analog risk task (BART)

The Balloon Analog Risk Task (BART) is increasingly used to assess risk-taking behavior and brain function. However, the brain networks underlying risk-taking during the BART and its reliability remain controversial. Here, we combined the activation likelihood estimation (ALE) meta-analysis with both task-based and task-free functional connectivity (FC) analysis to quantitatively synthesize brain networks involved in risk-taking during the BART, and compared the differences between adults and adolescents studies. Based on 22 pooled publications, the ALE meta-analysis revealed multiple brain regions in the reward network, salience network, and executive control network underlying risk-taking during the BART. Compared with adult risk-taking, adolescent risk-taking showed greater activation in the insula, putamen, and prefrontal regions. The combination of meta-analytic connectivity modeling with task-free FC analysis further confirmed the involvement of the reward, salience, and cognitive control networks in the BART. These findings demonstrate the core brain networks for risk-taking during the BART and support the utility of the BART for future neuroimaging and developmental research.

Functional connectivity in behavioral variant frontotemporal dementia

INTRODUCTION

Functional connectivity (FC)-which reflects relationships between neural activity in different brain regions-has been used to explore the functional architecture of the brain in neurodegenerative disorders. Although an increasing number of studies have explored FC changes in behavioral variant frontotemporal dementia (bvFTD), there is no focused, in-depth review about FC in bvFTD.

METHODS

Comprehensive literature search and narrative review to summarize the current field of FC in bvFTD.

RESULTS

(1) Decreased FC within the salience network (SN) is the most consistent finding in bvFTD; (2) FC changes extend beyond the SN and affect the interplay between networks; (3) results within the Default Mode Network are mixed; (4) the brain as a network is less interconnected and less efficient in bvFTD; (5) symptoms, functional impairment, and cognition are associated with FC; and (6) the functional architecture resembles patterns of neuropathological spread.

CONCLUSIONS

FC has potential as a biomarker, and future studies are expected to advance the field with multicentric initiatives, longitudinal designs, and methodological advances.

Domain-specific and domain-general neural network engagement during human-robot interactions

To what extent do domain-general and domain-specific neural network engagement generalize across interactions with human and artificial agents? In this exploratory study, we analysed a publicly available functional MRI (fMRI) data set (n = 22) to probe the similarities and dissimilarities in neural architecture while participants conversed with another person or a robot. Incorporating trial-by-trial dynamics of the interactions, listening and speaking, we used whole-brain, region-of-interest and functional connectivity analyses to test response profiles within and across social or non-social, domain-specific and domain-general networks, that is, the person perception, theory-of-mind, object-specific, language and multiple-demand networks. Listening to a robot compared to a human resulted in higher activation in the language network, especially in areas associated with listening comprehension, and in the person perception network. No differences in activity of the theory-of-mind network were found. Results from the functional connectivity analysis showed no difference between interactions with a human or robot in within- and between-network connectivity. Together, these results suggest that although largely similar regions are activated when speaking to a human and to a robot, activity profiles during listening point to a dissociation at a lower level or perceptual level, but not higher order cognitive level.

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

Joint-Channel-Connectivity-Based Feature Selection and Classification on fNIRS for Stress Detection in Decision-Making

Stress is one of the contributing factors affecting decision-making. Therefore, early stress recognition is essential to improve clinicians' decision-making performance. Functional near-infrared spectroscopy (fNIRS) has shown great potential in detecting stress. However, the majority of previous studies only used fNIRS features at the individual level for classification without considering the correlations among channels corresponding to the brain, which may provide distinguishing features. Hence, this study proposes a novel joint-channel-connectivity-based feature selection and classification algorithm for fNIRS to detect stress in decision-making. Specifically, this approach integrates feature selection and classifier modeling into a sparse model, where intra- and inter-channel regularizers are designed to explore potential correlations among channels to obtain discriminating features. In this paper, we simulated the decision-making of medical students under stress through the Trier Social Stress Test and the Balloon Analog Risk Task and recorded their cerebral hemodynamic alterations by fNIRS device. Experimental results illustrated that our method with the accuracy of 0.961 is superior to other machine learning methods. Additionally, the stress correlation and connectivity of brain regions calculated by feature selection have been confirmed in previous studies, which validates the effectiveness of our method and helps optimize the channel settings of fNIRS. This work was the first attempt to utilize a sparse model that simultaneously considers the sparsity of features and the correlation of brain regions for stress detection and obtained an admirable classification performance. Thus, the proposed model might be a useful tool for medical personnel to automatically detect stress in clinical decision-making situations.

Adolescents' affective and neural responses to parental praise and criticism

Social feedback from parents has a profound impact on the development of a child's self-concept. Yet, little is known about adolescents' affective and neural responses to parental social feedback, such as criticism or praise. Adolescents (n = 63) received standardized social feedback supposedly provided by their mother or father in the form of appraisals about their personality (e.g., 'respectful', 'lazy') during fMRI scanning. After each feedback word, adolescents reported their mood. Additionally, adolescents had rated whether feedback words matched their self-views on an earlier occasion. In line with preregistered hypotheses, negative parental feedback worsened adolescents' mood, which was exacerbated when feedback did not match adolescents' self-views. Negative feedback was associated with increased activity in the neural 'saliency network', including anterior insula, anterior cingulate cortex and dorsomedial prefrontal cortex. Positive feedback improved mood and increased activity in brain regions supporting social cognition, including temporoparietal junction, posterior superior temporal sulcus, and precuneus. A more positive general self-view and perceived parental warmth were associated with elevated mood, independent of feedback valence, but did not impact neural responses. Taken together, these results enhance our understanding of adolescents' neural circuitry involved in the processing of parental praise and criticism, and the impact of parental feedback on well-being.

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.

Loneliness and the Social Brain: How Perceived Social Isolation Impairs Human Interactions

Loneliness is a painful condition associated with increased risk for premature mortality. The formation of new, positive social relationships can alleviate feelings of loneliness, but requires rapid trustworthiness decisions during initial encounters and it is still unclear how loneliness hinders interpersonal trust. Here, a multimodal approach including behavioral, psychophysiological, hormonal, and neuroimaging measurements is used to probe a trust-based mechanism underlying impaired social interactions in loneliness. Pre-stratified healthy individuals with high loneliness scores (n = 42 out of a screened sample of 3678 adults) show reduced oxytocinergic and affective responsiveness to a positive conversation, report less interpersonal trust, and prefer larger social distances compared to controls (n = 40). Moreover, lonely individuals are rated as less trustworthy compared to controls and identified by the blinded confederate better than chance. During initial trust decisions, lonely individuals exhibit attenuated limbic and striatal activation and blunted functional connectivity between the anterior insula and occipitoparietal regions, which correlates with the diminished affective responsiveness to the positive social interaction. This neural response pattern is not mediated by loneliness-associated psychological symptoms. Thus, the results indicate compromised integration of trust-related information as a shared neurobiological component in loneliness, yielding a reciprocally reinforced trust bias in social dyads.

The Internet-Based Conversational Engagement Clinical Trial (I-CONECT) in Socially Isolated Adults 75+ Years Old: Randomized Controlled Trial Protocol and COVID-19 Related Study Modifications

Background: Increasing social interactions through communication technologies could offer a cost-effective prevention approach that slows cognitive decline and delays the onset of Alzheimer's disease. This paper describes the protocol of an active project named "Internet-based conversational engagement clinical trial (I-CONECT)" (ClinicalTrials.gov: NCT02871921). The COVID-19 pandemic related protocol modifications are also addressed in the current paper. Methods: I-CONECT is a multi-site, assessor-blind, randomized controlled behavioral intervention trial (RCT). We aim to randomize 320 socially isolated adults 75+ years old [160 Caucasian and 160 African American participants, 50:50 split between those with normal cognition and mild cognitive impairment (MCI)] recruited from the community to either the video chat intervention group or the control group (1:1 allocation). Those in the video chat group receive a computer and Internet service for the duration of the study, which they use to video chat with study staff for 30 min/day 4×/week for 6 months (high dose), and then 2×/week for an additional 6 months (maintenance dose). Both video chat and control groups have a brief (about 10 min) telephone check-in with study staff once per week. The primary outcome is the change in global cognitive function measured by Montreal Cognitive Assessment (MoCA) from baseline to 6 months. Secondary outcomes include changes in cognition in memory and executive function domains, emotional well-being measured by NIH Toolbox emotional battery, and daily functional abilities assessed with the Revised Observed Tasks of Daily Living (OTDL-R). Eligible participants have MRIs at baseline and 6 months. Participants contribute saliva for genetic testing (optional consent), and all video chats, weekly check-in calls and neuropsychological assessment sessions are recorded for speech and language analysis. The pandemic halted research activities and resulted in protocol modifications, including replacing in-person assessment with remote assessment, remote deployment of study equipment, and revised targeted sample size. Discussion: This trial provides user-friendly hardware for the conversational-based intervention that can be easily provided at participants' homes. The trial aspires to use age and culture-specific conversational materials and a related platform developed in this trial for enhancing cognitive reserve and improving cognitive function.

Individualized video recommendation modulates functional connectivity between large scale networks

With the emergence of AI‐powered recommender systems and their extensive use in the video streaming service, questions and concerns also arise. Why can recommended video content continuously capture users' attention? What is the impact of long‐term exposure to personalized video content on one's behaviors and brain functions? To address these questions, we designed an fMRI experiment presenting participants with personally recommended videos and generally recommended ones. To examine how large‐scale networks were modulated by personalized video content, graph theory analysis was applied to investigate the interaction between seven networks, including the ventral and dorsal attention networks (VAN, DAN), frontal–parietal network (FPN), salience network (SN), and three subnetworks of default mode network (dorsal medial prefrontal (dMPFC), Core, and medial temporal lobe (MTL)). Our results showed that viewing nonpersonalized video content mainly enhanced the connectivity in the DAN‐FPN‐Core pathway, whereas viewing personalized ones increased not only the connectivity in this pathway but also the DAN‐VAN‐dMPFC pathway. In addition, both personalized and nonpersonalized short videos decreased the couplings between SN and VAN as well as between two DMN subsystems, Core and MTL. Collectively, these findings uncovered distinct patterns of network interactions in response to short videos and provided insights into potential neural mechanisms by which human behaviors are biased by personally recommended content.

Human social sensing is an untapped resource for computational social science

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.