Mapping the social landscape: tracking patterns of interpersonal relationships

Cultural technologies for peace may have shaped our social cognition

Peace, the article shows, is achieved by culturally evolved institutions that incentivize positive-sum relationships. We propose that this insight has important consequences for the design of human social cognition. Cues that signal the existence of such institutions should play a prominent role in detecting group membership. We show how this accounts for previous findings and suggest avenues for future research.

Abstract cognitive maps of social network structure aid adaptive inference

Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

Human and animal dominance hierarchies show a pyramidal structure guiding adult and infant social inferences

This study investigates the structure of social hierarchies. We hypothesized that if social dominance relations serve to regulate conflicts over resources, then hierarchies should converge towards pyramidal shapes. Structural analyses and simulations confirmed this hypothesis, revealing a triadic-pyramidal motif across human and non-human hierarchies (114 species). Phylogenetic analyses showed that this pyramidal motif is widespread, with little influence of group size or phylogeny. Furthermore, nine experiments conducted in France found that human adults (N = 120) and infants (N = 120) draw inferences about dominance relations that are consistent with hierarchies' pyramidal motif. By contrast, human participants do not draw equivalent inferences based on a tree-shaped pattern with a similar complexity to pyramids. In short, social hierarchies exhibit a pyramidal motif across a wide range of species and environments. From infancy, humans exploit this regularity to draw systematic inferences about unobserved dominance relations, using processes akin to formal reasoning.

Strengths of social ties modulate brain computations for third-party punishment

Costly punishment of social norm transgressors by third-parties has been considered as a decisive stage in the evolution of human cooperation. An important facet of social relationship knowledge concerns the strength of the social ties between individuals, as measured by social distance. Yet, it is unclear how the enforcement of social norms is influenced by the social distance between a third-party and a norm violator at the behavioral and the brain system levels. Here, we investigated how social distance between punishers and norm-violators influences third-party punishment. Participants as third-party punished norm violators more severely as social distance between them increased. Using model-based fMRI, we disentangled key computations contributing to third-party punishment: inequity aversion, social distance between participant and norm violator and integration of the cost to punish with these signals. Inequity aversion increased activity in the anterior cingulate cortex and bilateral insula, and processing social distance engaged a bilateral fronto-parietal cortex brain network. These two brain signals and the cost to punish were integrated in a subjective value signal of sanctions that modulated activity in the ventromedial prefrontal cortex. Together, our results reveal the neurocomputational underpinnings of third-party punishment and how social distance modulates enforcement of social norms in humans.

Spillover effects of competition outcome on future risky cooperation

There is growing evidence that risky cooperation is regulated by the experience of previous interactions with others. However, it is unclear how the evaluation of outcomes from competitive interactions can affect individuals' subsequent cooperative behavior. To address this issue, we examined how participants cooperated with a partner having just competed with them. While competing, participants (N = 164) were randomly assigned to receive one of four types of outcome feedback regarding their performance (victory vs. defeat vs. uncertain vs. no competition (control)). We found that both the experience of defeats and of uncertainty as competitive outcomes exerted a negative impact on the extent to which participants then engaged in cooperative behavior with their recent opponents. This only occurred when such subsequent cooperative behavior involved a high potential for incurring personal costs but not when there was no risk of incurring personal costs and a positive return. Finally, mediation analysis revealed that the effect of defeat was mediated by participants' level of interpersonal trust and the extent to which participants were willing to cooperate, while the effect of the uncertain competitive outcome was mediated only by the extent to which participants were willing to cooperate. These findings offer novel insights into how risky cooperation is modulated by previous competition.

Neurocomputational mechanism of real-time distributed learning on social networks

Social networks shape our decisions by constraining what information we learn and from whom. Yet, the mechanisms by which network structures affect individual learning and decision-making remain unclear. Here, by combining a real-time distributed learning task with functional magnetic resonance imaging, computational modeling and social network analysis, we studied how humans learn from observing others' decisions on seven-node networks with varying topological structures. We show that learning on social networks can be approximated by a well-established error-driven process for observational learning, supported by an action prediction error encoded in the lateral prefrontal cortex. Importantly, learning is flexibly weighted toward well-connected neighbors, according to activity in the dorsal anterior cingulate cortex, but only insofar as social observations contain secondhand, potentially intertwining, information. These data suggest a neurocomputational mechanism of network-based filtering on the sources of information, which may give rise to biased learning and the spread of misinformation in an interconnected society.

Social navigation modulates the anterior and posterior hippocampal circuits in the resting brain

Social navigation is a dynamic and complex process that requires the collaboration of multiple brain regions. However, the neural networks for navigation in a social space remain largely unknown. This study aimed to investigate the role of hippocampal circuit in social navigation from a resting-state fMRI data. Here, resting-state fMRI data were acquired before and after participants performed a social navigation task. By taking the anterior and posterior hippocampus (HPC) as the seeds, we calculated their connectivity with the whole brain using the seed-based static functional connectivity (sFC) and dynamic FC (dFC) approaches. We found that the sFC and dFC between the anterior HPC and supramarginal gyrus, sFC or dFC between posterior HPC and middle cingulate cortex, inferior parietal gyrus, angular gyrus, posterior cerebellum, medial superior frontal gyrus were increased after the social navigation task. These alterations were related to social cognition of tracking location in the social navigation. Moreover, participants who had more social support or less neuroticism showed a greater increase in hippocampal connectivity. These findings may highlight a more important role of the posterior hippocampal circuit in the social navigation, which is crucial for social cognition.

Neural encoding of novel social networks: evidence that perceivers prioritize others' centrality

Knowledge of someone's friendships can powerfully impact how one interacts with them. Previous research suggests that information about others' real-world social network positions-e.g. how well-connected they are (centrality), 'degrees of separation' (relative social distance)-is spontaneously encoded when encountering familiar individuals. However, many types of information covary with where someone sits in a social network. For instance, strangers' face-based trait impressions are associated with their social network centrality, and social distance and centrality are inherently intertwined with familiarity, interpersonal similarity and memories. To disentangle the encoding of the social network position from other social information, participants learned a novel social network in which the social network position was decoupled from other factors and then saw each person's image during functional magnetic resonance imaging scanning. Using representational similarity analysis, we found that social network centrality was robustly encoded in regions associated with visual attention and mentalizing. Thus, even when considering a social network in which one is not included and where centrality is unlinked from perceptual and experience-based features to which it is inextricably tied in naturalistic contexts, the brain encodes information about others' importance in that network, likely shaping future perceptions of and interactions with those individuals.

Infants infer potential social partners by observing the interactions of their parent with unknown others

Infants are born into networks of individuals who are socially connected. How do infants begin learning which individuals are their own potential social partners? Using digitally edited videos, we showed 12-mo-old infants' social interactions between unknown individuals and their own parents. In studies 1 to 4, after their parent showed affiliation toward one puppet, infants expected that puppet to engage with them. In study 5, infants made the reverse inference; after a puppet engaged with them, the infants expected that puppet to respond to their parent. In each study, infants' inferences were specific to social interactions that involved their own parent as opposed to another infant's parent. Thus, infants combine observation of social interactions with knowledge of their preexisting relationship with their parent to discover which newly encountered individuals are potential social partners for themselves and their families.