Consistent Neural Activity Patterns Represent Personally Familiar People

Normativity vs. uniqueness: effects of social relationship strength on neural representations of others

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.

Orthogonal neural encoding of targets and distractors supports multivariate cognitive control

The complex challenges of our mental life require us to coordinate multiple forms of neural information processing. Recent behavioural studies have found that people can coordinate multiple forms of attention, but the underlying neural control process remains obscure. We hypothesized that the brain implements multivariate control by independently monitoring feature-specific difficulty and independently prioritizing feature-specific processing. During functional MRI, participants performed a parametric conflict task that separately tags target and distractor processing. Consistent with feature-specific monitoring, univariate analyses revealed spatially segregated encoding of target and distractor difficulty in the dorsal anterior cingulate cortex. Consistent with feature-specific attentional priority, our encoding geometry analysis revealed overlapping but orthogonal representations of target and distractor coherence in the intraparietal sulcus. Coherence representations were mediated by control demands and aligned with both performance and frontoparietal activity, consistent with top-down attention. Together, these findings provide evidence for the neural geometry necessary to coordinate multivariate cognitive control.

The boundary between real and fictional others in the medial prefrontal cortex is blurred in lonelier individuals

People spend much of their free time engaging with narrative fiction. Research shows that, like real-life friends, fictional characters can sometimes influence individuals' attitudes, behaviors, and self-beliefs. Moreover, for certain individuals, fictional characters can stand in for real-life friends by providing the experience of belonging. Despite these parallels between how people think of real and fictional others, it is unclear whether, and to what degree, their neural representations are similar. Does the brain treat psychologically close fictional others as it does close real-world friends, or are real others somehow privileged in their neural representation? In the present study, fans of the HBO series Game of Thrones performed a trait-evaluation task for the self, 9 real-life friends/acquaintances, and 9 fictional characters from Game of Thrones while undergoing functional magnetic resonance imaging. Using both brain decoding and representational similarity analysis, we found evidence of a categorical boundary between real and fictional others within the medial prefrontal cortex. However, the boundary between these categories was blurred in lonelier individuals. These results suggest that lonelier individuals may turn to fictional characters to meet belongingness needs, and this, in turn, alters the manner in which these categories are encoded within the social brain.

Specialized Networks for Social Cognition in the Primate Brain

Primates have evolved diverse cognitive capabilities to navigate their complex social world. To understand how the brain implements critical social cognitive abilities, we describe functional specialization in the domains of face processing, social interaction understanding, and mental state attribution. Systems for face processing are specialized from the level of single cells to populations of neurons within brain regions to hierarchically organized networks that extract and represent abstract social information. Such functional specialization is not confined to the sensorimotor periphery but appears to be a pervasive theme of primate brain organization all the way to the apex regions of cortical hierarchies. Circuits processing social information are juxtaposed with parallel systems involved in processing nonsocial information, suggesting common computations applied to different domains. The emerging picture of the neural basis of social cognition is a set of distinct but interacting subnetworks involved in component processes such as face perception and social reasoning, traversing large parts of the primate brain.

Distributed and hierarchical neural encoding of multidimensional biological motion attributes in the human brain

The human visual system can efficiently extract distinct physical, biological, and social attributes (e.g. facing direction, gender, and emotional state) from biological motion (BM), but how these attributes are encoded in the brain remains largely unknown. In the current study, we used functional magnetic resonance imaging to investigate this issue when participants viewed multidimensional BM stimuli. Using multiple regression representational similarity analysis, we identified distributed brain areas, respectively, related to the processing of facing direction, gender, and emotional state conveyed by BM. These brain areas are governed by a hierarchical structure in which the respective neural encoding of facing direction, gender, and emotional state is modulated by each other in descending order. We further revealed that a portion of the brain areas identified in representational similarity analysis was specific to the neural encoding of each attribute and correlated with the corresponding behavioral results. These findings unravel the brain networks for encoding BM attributes in consideration of their interactions, and highlight that the processing of multidimensional BM attributes is recurrently interactive.

A tripartite view of the posterior cingulate cortex

The posterior cingulate cortex (PCC) is one of the least understood regions of the cerebral cortex. By contrast, the anterior cingulate cortex has been the subject of intensive investigation in humans and model animal systems, leading to detailed behavioural and computational theoretical accounts of its function. The time is right for similar progress to be made in the PCC given its unique anatomical and physiological properties and demonstrably important contributions to higher cognitive functions and brain diseases. Here, we describe recent progress in understanding the PCC, with a focus on convergent findings across species and techniques that lay a foundation for establishing a formal theoretical account of its functions. Based on this converging evidence, we propose that the broader PCC region contains three major subregions - the dorsal PCC, ventral PCC and retrosplenial cortex - that respectively support the integration of executive, mnemonic and spatial processing systems. This tripartite subregional view reconciles inconsistencies in prior unitary theories of PCC function and offers promising new avenues for progress.

Bipartite functional fractionation within the neural system for social cognition supports the psychological continuity of self versus other

Research of social neuroscience establishes that regions in the brain's default-mode network (DN) and semantic network (SN) are engaged by socio-cognitive tasks. Research of the human connectome shows that DN and SN regions are both situated at the transmodal end of a cortical gradient but differ in their loci along this gradient. Here we integrated these 2 bodies of research, used the psychological continuity of self versus other as a "test-case," and used functional magnetic resonance imaging to investigate whether these 2 networks would encode social concepts differently. We found a robust dissociation between the DN and SN-while both networks contained sufficient information for decoding broad-stroke distinction of social categories, the DN carried more generalizable information for cross-classifying across social distance and emotive valence than did the SN. We also found that the overarching distinction of self versus other was a principal divider of the representational space while social distance was an auxiliary factor (subdivision, nested within the principal dimension), and this representational landscape was more manifested in the DN than in the SN. Taken together, our findings demonstrate how insights from connectome research can benefit social neuroscience and have implications for clarifying the 2 networks' differential contributions to social cognition.

Shared neural codes for visual and semantic information about familiar faces in a common representational space

Our brain processes faces of close others differently than faces of visually familiar individuals. While both types of faces activate similar visual areas, faces of close others activate areas involved in processing social and semantic information. Here, we used between-subject linear classifiers trained on hyperaligned brain data to investigate the neural code for visual and semantic information about familiar others. The identity of both visually and personally familiar faces could be decoded across participants from brain activity in visual areas. Instead, only the identity of personally familiar faces could be decoded in areas involved in social cognition. Our results suggest that individually distinctive information associated with familiar faces is embedded in a neural code that is shared across brains.

Processes evoked by seeing a personally familiar face encompass recognition of visual appearance and activation of social and person knowledge. Whereas visual appearance is the same for all viewers, social and person knowledge may be more idiosyncratic. Using between-subject multivariate decoding of hyperaligned functional magnetic resonance imaging data, we investigated whether representations of personally familiar faces in different parts of the distributed neural system for face perception are shared across individuals who know the same people. We found that the identities of both personally familiar and merely visually familiar faces were decoded accurately across brains in the core system for visual processing, but only the identities of personally familiar faces could be decoded across brains in the extended system for processing nonvisual information associated with faces. Our results show that personal interactions with the same individuals lead to shared neural representations of both the seen and unseen features that distinguish their identities.

Social Relationship Strength Modulates the Similarity of Brain-to-Brain Representations of Group Members

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.

Computational approaches to the neuroscience of social perception

Across multiple domains of social perception-including social categorization, emotion perception, impression formation and mentalizing-multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and discuss how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception.

Being the Gatekeeper: How Thinking about Sharing Affects Neural Encoding of Information

Information transmission in a society depends on individuals' intention to share or not. Yet, little is known about whether being the gatekeeper shapes the brain's processing of incoming information. Here, we examine how thinking about sharing affects neural encoding of information, and whether this effect is moderated by the person's real-life social network position. In an functional magnetic resonance imaging study, participants rated abstracts of news articles on how much they wanted to read for themselves (read) or-as information gatekeepers-to share with a specific other (narrowcast) or to post on their social media feed (broadcast). In all conditions, consistent spatial blood oxygen level-dependent patterns associated with news articles were observed across participants in brain regions involved in perceptual and language processing as well as higher-order processes. However, when thinking about sharing, encoding consistency decreased in higher-order processing areas (e.g., default mode network), suggesting that the gatekeeper role involves more individualized processing in the brain, that is, person- and context-specific. Moreover, participants whose social networks had high ego-betweenness centrality (i.e., more likely to be information gatekeeper in real life) showed more individualized encoding when thinking about broadcasting. This study reveals how gatekeeping shapes our brain's processing of incoming information.

FFA and OFA Encode Distinct Types of Face Identity Information

Faces of different people elicit distinct fMRI patterns in several face-selective regions of the human brain. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). In a sample of 30 human participants (22 females, 8 males), we used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. We built diverse candidate models, ranging from low-level image-computable properties (pixel-wise, GIST, and Gabor-Jet dissimilarities), through higher-level image-computable descriptions (OpenFace deep neural network, trained to cluster faces by identity), to complex human-rated properties (perceived similarity, social traits, and gender). We found marked differences in the information represented by the FFA and OFA. Dissimilarities between face identities in FFA were accounted for by differences in perceived similarity, Social Traits, Gender, and by the OpenFace network. In contrast, representational distances in OFA were mainly driven by differences in low-level image-based properties (pixel-wise and Gabor-Jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information.SIGNIFICANCE STATEMENT Recent studies using fMRI have shown that several face-responsive brain regions can distinguish between different face identities. It is however unclear whether these different face-responsive regions distinguish between identities in similar or different ways. We used representational similarity analysis to investigate the computations within three brain regions in response to naturalistically varying videos of face identities. Our results revealed that two regions, the fusiform face area and the occipital face area, encode distinct identity information about faces. Although identity can be decoded from both regions, identity representations in fusiform face area primarily contained information about social traits, gender, and high-level visual features, whereas occipital face area primarily represented lower-level image features.

Getting to Know Someone: Familiarity, Person Recognition, and Identification in the Human Brain

In our everyday life, we continuously get to know people, dominantly through their faces. Several neuroscientific experiments showed that familiarization changes the behavioral processing and underlying neural representation of faces of others. Here, we propose a model of the process of how we actually get to know someone. First, the purely visual familiarization of unfamiliar faces occurs. Second, the accumulation of associated, nonsensory information refines person representation, and finally, one reaches a stage where the effortless identification of very well-known persons occurs. We offer here an overview of neuroimaging studies, first evaluating how and in what ways the processing of unfamiliar and familiar faces differs and, second, by analyzing the fMRI adaptation and multivariate pattern analysis results we estimate where identity-specific representation is found in the brain. The available neuroimaging data suggest that different aspects of the information emerge gradually as one gets more and more familiar with a person within the same network. We propose a novel model of familiarity and identity processing, where the differential activation of long-term memory and emotion processing areas is essential for correct identification.

Decoding individual identity from brain activity elicited in imagining common experiences

Everyone experiences common events differently. This leads to personal memories that presumably provide neural signatures of individual identity when events are reimagined. We present initial evidence that these signatures can be read from brain activity. To do this, we progress beyond previous work that has deployed generic group-level computational semantic models to distinguish between neural representations of different events, but not revealed interpersonal differences in event representations. We scanned 26 participants' brain activity using functional Magnetic Resonance Imaging as they vividly imagined themselves personally experiencing 20 common scenarios (e.g., dancing, shopping, wedding). Rather than adopting a one-size-fits-all approach to generically model scenarios, we constructed personal models from participants' verbal descriptions and self-ratings of sensory/motor/cognitive/spatiotemporal and emotional characteristics of the imagined experiences. We demonstrate that participants' neural representations are better predicted by their own models than other peoples'. This showcases how neuroimaging and personalized models can quantify individual-differences in imagined experiences.

Temporal Dynamics of the Neural Representation of Social Relationships

Humans can rapidly encode information from faces to support social judgments and facilitate interactions with others. We can also recall complex knowledge about those individuals, such as their social relationships with others, but the time course of this process has not been examined in detail. This study addressed the temporal dynamics of emerging visual and social relationship information using EEG and representational similarity analysis. Participants (female = 23, male = 10) became familiar with a 10-person social network, and were then shown faces of that network's members while EEG was recorded. To examine the temporal dynamics of the cognitive processes related to face perception, we compared the similarity structure of neural pattern responses to models of visual processing, face shape similarity, person identity, and social relationships. We found that all types of information are associated with neural patterns after a face is seen. Visual models became significant early after image onset, and identity across a change in facial expression was uniquely associated with neural patterns at several points throughout the time course. Additionally, a model reflecting perceived frequency of social interaction was present beginning at ∼110 ms, even in the absence of an explicit task to think about the relationships among the network members. This study highlights the speed and salience of social information relating to group dynamics that are present in the brain during person perception.SIGNIFICANCE STATEMENT We live our lives in social groups where complex relationships form among and around us. It is likely that some of the information about social relationships that we observe is integral during person perception, to better help us interact in differing situations with a variety of people. However, when exactly this information becomes relevant has been unclear. In this study, we present evidence that information reflecting observed relationships among a social network is spontaneously represented in whole-brain patterns shortly following presentation of a face. These results are consistent with neuroimaging studies showing spontaneous spatial representation of social network characteristics, and contribute novel insights into the timing of these neural processes.

Self-Other Representation in the Social Brain Reflects Social Connection

Social connection is critical to well-being, yet how the brain reflects our attachment to other people remains largely unknown. We combined univariate and multivariate brain imaging analyses to assess whether and how the brain organizes representations of others based on how connected they are to our own identity. During an fMRI scan, female and male human participants (N = 43) completed a self- and other-reflection task for 16 targets: the self, five close others, five acquaintances, and five celebrities. In addition, they reported their subjective closeness to each target and their own trait loneliness. We examined neural responses to the self and others in a brain region that has been associated with self-representation (mPFC) and across the whole brain. The structure of self-other representation in the mPFC and across the social brain appeared to cluster targets into three social categories: the self, social network members (including close others and acquaintances), and celebrities. Moreover, both univariate activation in mPFC and multivariate self-other similarity in mPFC and across the social brain increased with subjective self-other closeness ratings. Critically, participants who were less socially connected (i.e., lonelier) showed altered self-other mapping in social brain regions. Most notably, in mPFC, loneliness was associated with reduced representational similarity between the self and others. The social brain apparently maintains information about broad social categories as well as closeness to the self. Moreover, these results point to the possibility that feelings of chronic social disconnection may be mirrored by a "lonelier" neural self-representation.SIGNIFICANCE STATEMENT Social connection is critical to well-being, yet how the brain reflects our attachment to people remains unclear. We found that the social brain stratifies neural representations of people based on our subjective connection to them, separately clustering people who are and are not in our social network. Moreover, the people we feel closest to are represented most closely to ourselves. Finally, lonelier individuals also appeared to have a "lonelier" neural self-representation in the mPFC, as loneliness attenuated the similarity between self and other neural representations in this region. The social brain appears to map our interpersonal ties, and alterations in this map may help explain why lonely individuals endorse statements such as "people are around me but not with me."

Default network interactivity during mentalizing about known others is modulated by age and social closeness

In young adults, mentalizing about known others engages the default network, with differential brain response modulated by social closeness. While the functional integrity of the default network changes with age, few studies have investigated how these changes impact the representation of known others, across levels of closeness. Young (N = 29, 16 females) and older (N = 27, 12 females) adults underwent functional magnetic resonance imaging (fMRI) scanning while making trait judgments for social others varying in closeness. Multivariate analyses (partial least squares) identified default network activation for trait judgments across both age cohorts. For young adults, romantic partner and self-judgments differed from other levels of social closeness and were associated with activity in default and salience networks. In contrast, default network interactivity was not modulated by social closeness for older adults. In two functional connectivity analyses, both age groups demonstrated connectivity between dorsal and ventral medial prefrontal cortex and other default network regions during trait judgments. However older, but not young, adults also showed increased functional coupling between medial and lateral prefrontal brain regions that did not vary by category of known other. Mentalizing about others engages default and frontal brain regions in older adulthood, and this coupling is poorly modulated by social closeness.

An attachment theoretical perspective for the neural representation of close others

Recent investigations in neuroscience elucidate the neural basis of close other cognitive representations, which serve functions central to our health and happiness. Yet, there are persistent barriers to this research, including disparate research methods and the absence of a common theoretical background. The present review connects neuroimaging and attachment theory within a novel social, cognitive and affective framework. We apply attachment theory to understand why we would expect cognitive representations of close others to be different from other social neural representations. Developing reliable markers of attachment is a critical step in mapping close other neural representations. We then examine existing neuroimaging literature on close other representations, highlighting the recruitment of neural systems supporting reward, motivation and distress alleviation, in addition to the mirror neuron system, default network and salience network. We then review the methodologies of past studies, revealing a diverse array of self-report measures assessing `closeness' and social cognitive tasks that, taken together, preclude meaningful synthesis of findings. Lastly, we discuss specific behavioral measures of attachment and closeness with recommendations for the field. This attachment framework integrates brain and behavioral sciences and unites theoretical principles with empirical methods to further our understanding of how the brain represents close others.

Decoding the neural representation of self and person knowledge with multivariate pattern analysis and data-driven approaches

Multivariate pattern analysis and data-driven approaches to understand how the human brain encodes sensory information and higher level conceptual knowledge have become increasingly dominant in visual and cognitive neuroscience; however, it is only in recent years that these methods have been applied to the domain of social information processing. This review examines recent research in the field of social cognitive neuroscience focusing on how multivariate pattern analysis (e.g., pattern classification, representational similarity analysis) and data-driven methods (e.g., reverse correlation, intersubject correlation) have been used to decode and characterize high-level information about the self, other persons, and social groups. We begin with a review of what is known about how self-referential processing and person perception are represented in the medial prefrontal cortex based on conventional activation-based neuroimaging approaches. This is followed by a nontechnical overview of current multivariate pattern-based and data-driven neuroimaging methods designed to characterize and/or decode neural representations. The remainder of the review focuses on examining how these methods have been applied to the topic of self, person perception, and the perception of social groups. In this review, we highlight recent trends (e.g., analysis of social networks, decoding race and social groups, and the use of naturalistic stimuli) and discuss several theoretical challenges that arise from the application of these new methods to the question of how the brain represents knowledge about the self and others. This article is categorized under: Neuroscience > Cognition.

Modeling the Predictive Social Mind

The social mind is tailored to the problem of predicting the mental states and actions of other people. However, social cognition researchers have only scratched the surface of the predictive social mind. We discuss here a new framework for explaining how people organize social knowledge and use it for social prediction. Specifically, we propose a multilayered framework of social cognition in which two hidden layers - the mental states and traits of others - support predictions about the observable layer - the actions of others. A parsimonious set of psychological dimensions structures each layer, and proximity within and across layers guides social prediction. This simple framework formalizes longstanding intuitions from social cognition, and in doing so offers a generative model for deriving new hypotheses about predictive social cognition.