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.

Cognitive maps of social features enable flexible inference in social networks.

In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to acquire firsthand knowledge of all relations within a network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. Across three studies (n = 328), we show that people can encode information about social features (e.g., hobbies, clubs) and subsequently deploy this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that could support such inferences. We find that people's ability to successfully generalize depends on two representational strategies: a simple but inflexible similarity heuristic that leverages homophily, and a complex but flexible cognitive map that encodes the statistical relationships between social features and friendships. Together, our studies reveal that people can build cognitive maps encoding arbitrary patterns of latent relations in many abstract feature spaces, allowing social networks to be represented in a flexible format. Moreover, these findings shed light on open questions across disciplines about how people learn and represent social networks and may have implications for generating more human-like link prediction in machine learning algorithms.

Intolerance of uncertainty modulates brain-to-brain synchrony during politically polarized perception.

Political partisans see the world through an ideologically biased lens. What drives political polarization? Although it has been posited that polarization arises because of an inability to tolerate uncertainty and a need to hold predictable beliefs about the world, evidence for this hypothesis remains elusive. We examined the relationship between uncertainty tolerance and political polarization using a combination of brain-to-brain synchrony and intersubject representational similarity analysis, which measured committed liberals' and conservatives' (n = 44) subjective interpretation of naturalistic political video material. Shared ideology between participants increased neural synchrony throughout the brain during a polarizing political debate filled with provocative language but not during a neutrally worded news clip on polarized topics or a nonpolitical documentary. During the political debate, neural synchrony in mentalizing and valuation networks was modulated by one's aversion to uncertainty: Uncertainty-intolerant individuals experienced greater brain-to-brain synchrony with politically like-minded peers and lower synchrony with political opponents-an effect observed for liberals and conservatives alike. Moreover, the greater the neural synchrony between committed partisans, the more likely that two individuals formed similar, polarized attitudes about the debate. These results suggest that uncertainty attitudes gate the shared neural processing of political narratives, thereby fueling polarized attitude formation about hot-button issues.

Conceptual representations of uncertainty predict risky decision-making.

Decisions made under uncertainty often are considered according to their perceived subjective value. We move beyond this traditional framework to explore the hypothesis that conceptual representations of uncertainty influence risky choice. Results reveal that uncertainty concepts are represented along a dimension that jointly captures probabilistic and valenced features of the conceptual space. These uncertainty representations predict the degree to which an individual engages in risky decision-making. Moreover, we find that most individuals have two largely distinct representations: one for uncertainty and another for certainty. In contrast, a minority of individuals exhibit substantial overlap between their representations of uncertainty and certainty. Together, these findings reveal the relationship between the conceptualization of uncertainty and risky decisions.

Hippocampus Guides Adaptive Learning during Dynamic Social Interactions.

How do we evaluate whether someone will make a good friend or collaborative peer? A hallmark of human cognition is the ability to make adaptive decisions based on information garnered from limited prior experiences. Using an interactive social task measuring adaptive choice (deciding who to reengage or avoid) in male and female participants, we find the hippocampus supports value-based social choices following single-shot learning. These adaptive choices elicited a suppression signal in the hippocampus, revealing sensitivity for the subjective perception of a person and how well they treat you during choice. The extent to which the hippocampus was suppressed was associated with flexibly interacting with prior generous individuals and avoiding selfish individuals. Further, we found that hippocampal signals during decision-making were related to subsequent memory for a person and the offer they made before. Consistent with the hippocampus leveraging previously executed choices to solidify a reliable neural signature for future adaptive behavior, we also observed a later hippocampal enhancement. These findings highlight the hippocampus playing a multifaceted role in socially adaptive learning.SIGNIFICANCE STATEMENT Adaptively navigating social interactions requires an integration of prior experiences with information gleaned from the current environment. While most research has focused on striatal-based feedback learning, open questions remain regarding the role of hippocampal-based episodic memory systems. Here, we show that during social decisions based on prior experience, hippocampal suppression signals were sensitive to adaptive choice, while hippocampal enhancements was related to subsequent memory for the original social interaction. These findings highlight the hippocampus playing a multifaceted role in socially adaptive learning.

Stimulus generalization as a mechanism for learning to trust.

How do humans learn to trust unfamiliar others? Decisions in the absence of direct knowledge rely on our ability to generalize from past experiences and are often shaped by the degree of similarity between prior experience and novel situations. Here, we leverage a stimulus generalization framework to examine how perceptual similarity between known individuals and unfamiliar strangers shapes social learning. In a behavioral study, subjects play an iterative trust game with three partners who exhibit highly trustworthy, somewhat trustworthy, or highly untrustworthy behavior. After learning who can be trusted, subjects select new partners for a second game. Unbeknownst to subjects, each potential new partner was parametrically morphed with one of the three original players. Results reveal that subjects prefer to play with strangers who implicitly resemble the original player they previously learned was trustworthy and avoid playing with strangers resembling the untrustworthy player. These decisions to trust or distrust strangers formed a generalization gradient that converged toward baseline as perceptual similarity to the original player diminished. In a second imaging experiment we replicate these behavioral gradients and leverage multivariate pattern similarity analyses to reveal that a tuning profile of activation patterns in the amygdala selectively captures increasing perceptions of untrustworthiness. We additionally observe that within the caudate adaptive choices to trust rely on neural activation patterns similar to those elicited when learning about unrelated, but perceptually familiar, individuals. Together, these findings suggest an associative learning mechanism efficiently deploys moral information encoded from past experiences to guide future choice.

Emotional responses to prosocial messages increase willingness to self-isolate during the COVID-19 pandemic.

The COVID-19 pandemic may be one of the greatest modern societal challenges that requires widespread collective action and cooperation. While a handful of actions can help reduce pathogen transmission, one critical behavior is to self-isolate. Public health messages often use persuasive language to change attitudes and behaviors, which can evoke a wide range of negative and positive emotional responses. In a U.S. representative sample (N = 955), we presented two messages that leveraged either threatening or prosocial persuasive language, and measured self-reported emotional reactions and willingness to self-isolate. Although emotional responses to the interventions were highly heterogeneous, personality traits known to be linked with distinct emotional experiences (extraversion and neuroticism) explained significant variance in the arousal response. While results show that both types of appeals increased willingness to self-isolate (Cohen's d = 0.41), compared to the threat message, the efficacy of the prosocial message was more dependent on the magnitude of the evoked emotional response on both arousal and valence dimensions. Together, these results imply that prosocial appeals have the potential to be associated with greater compliance if they evoke highly positive emotional responses.

Clarifying the link between music and social bonding by measuring prosociality in context.

To corroborate the music and social bonding hypothesis, we propose that future investigations isolate specific components of social bonding and consider the influence of context. We deconstruct and operationalize social bonding through the lens of social psychology and provide examples of specific measures that can be used to assess how the link between music and sociality varies by context.

Anxiety Impedes Adaptive Social Learning Under Uncertainty.

Very little is known about how individuals learn under uncertainty when other people are involved. We propose that humans are particularly tuned to social uncertainty, which is especially noisy and ambiguous. Individuals exhibiting less tolerance for uncertainty, such as those with anxiety, may have greater difficulty learning in uncertain social contexts and therefore provide an ideal test population to probe learning dynamics under uncertainty. Using a dynamic trust game and a matched nonsocial task, we found that healthy subjects (n = 257) were particularly good at learning under negative social uncertainty, swiftly figuring out when to stop investing in an exploitative social partner. In contrast, subjects with anxiety (n = 97) overinvested in exploitative partners. Computational modeling attributed this pattern to a selective reduction in learning from negative social events and a failure to enhance learning as uncertainty rises-two mechanisms that likely facilitate adaptive social choice.

The Functional Roles of the Amygdala and Prefrontal Cortex in Processing Uncertainty.

Decisions under uncertainty distinguish between those made under risk (known probabilities) and those made under ambiguity (unknown probabilities). Despite widespread interest in decisions under uncertainty and the successful documentation that these distinct psychological constructs profoundly-and differentially-impact behavior, research has not been able to systematically converge on which brain regions are functionally involved in processing risk and ambiguity. We merge a lesion approach with computational modeling and simultaneous measurement of the arousal response to investigate the impact the medial prefrontal cortex (mPFC), lateral prefrontal cortex (lPFC), and amygdala have on decisions under uncertainty. Results reveal that the lPFC acts as a unitary system for processing uncertainty: Lesions to this region disrupted the relationship between arousal and choice, broadly increasing both risk and ambiguity seeking. In contrast, the mPFC and amygdala appeared to play no role in processing risk, and the mPFC only had a tenuous relationship with ambiguous uncertainty. Together, these findings reveal that only the lPFC plays a global role in processing the highly aversive nature of uncertainty.

Associative Learning of Social Value in Dynamic Groups.

Although humans live in societies that regularly demand engaging with multiple people simultaneously, little is known about social learning in group settings. In two experiments, we combined a Pavlovian learning framework with dyadic economic games to test whether blocking mechanisms support value-based social learning in the gain (altruistic dictators) and loss (greedy robbers) domains. Subjects first learned about an altruistic dictator, who subsequently made altruistic splits collectively with a partner. Results revealed that because the presence of the dictator already predicted the outcome, subjects did not learn to associate value with the partner. This social blocking effect was not observed in the loss domain: A kind robber's partner, who could steal all the subjects' money but stole little, acquired highly positive value-which biased subjects' subsequent behavior. These findings reveal how Pavlovian mechanisms support efficient social learning, while also demonstrating that violations of social expectations can attenuate how readily these mechanisms are recruited.

Empathic concern drives costly altruism.

Why do we self-sacrifice to help others in distress? Two competing theories have emerged, one suggesting that prosocial behavior is primarily motivated by feelings of empathic other-oriented concern, the other that we help mainly because we are egoistically focused on reducing our own discomfort. Here we explore the relationship between costly altruism and these two sub-processes of empathy, specifically drawing on the caregiving model to test the theory that trait empathic concern (e.g. general tendency to have sympathy for another) and trait personal distress (e.g. predisposition to experiencing aversive arousal states) may differentially drive altruistic behavior. We find that trait empathic concern--and not trait personal distress--motivates costly altruism, and this relationship is supported by activity in the ventral tegmental area, caudate and subgenual anterior cingulate, key regions for promoting social attachment and caregiving. Together, this data helps identify the behavioral and neural mechanisms motivating costly altruism, while demonstrating that individual differences in empathic concern-related brain responses can predict real prosocial choice.

The Effects of Social Context and Acute Stress on Decision Making Under Uncertainty.

Uncertainty preferences are typically studied in neutral, nonsocial contexts. This approach, however, fails to capture the dynamic factors that influence choices under uncertainty in the real world. Our goal was twofold: to test whether uncertainty valuation is similar across social and nonsocial contexts, and to investigate the effects of acute stress on uncertainty preferences. Subjects completed matched gambling and trust games following either a control or a stress manipulation. Those who were not under stress exhibited no differences between the amount of money gambled and the amount of money entrusted to partners. In comparison, stressed subjects gambled more money but entrusted less money to partners. We further found that irrespective of stress, subjects were highly attuned to irrelevant feedback in the nonsocial, gambling context, believing that every loss led to a greater chance of winning (the gamblers' fallacy). However, when deciding to trust a stranger, control subjects behaved rationally, treating each new interaction as independent. Stress compromised this adaptive behavior, increasing sensitivity to irrelevant social feedback.

Neural activity associated with monitoring the oscillating threat value of a tarantula.

Phylogenetic threats such as spiders evoke our deepest primitive fears. When close or looming, such threats engage evolutionarily conserved monitoring systems and defense reactions that promote self-preservation. With the use of a modified behavioral approach task within functional MRI, we show that, as a tarantula was placed closer to a subject's foot, increased experiences of fear coincided with augmented activity in a cascade of fear-related brain networks including the periaqueductal gray, amygdala, and bed nucleus of the stria terminalis. Activity in the amygdala was also associated with underprediction of the tarantula's threat value and, in addition to the bed nucleus of the stria terminalis, with monitoring the tarantula's threat value as indexed by its direction of movement. Conversely, the orbitofrontal cortex was engaged as the tarantula grew more distant, suggesting that this region emits safety signals or expels fear. Our findings fractionate the neurobiological mechanisms associated with basic fear and potentially illuminate the perturbed reactions that characterize clinical phobias.

Prefrontal cortex state representations shape human credit assignment.

People learn adaptively from feedback, but the rate of such learning differs drastically across individuals and contexts. Here, we examine whether this variability reflects differences in what is learned. Leveraging a neurocomputational approach that merges fMRI and an iterative reward learning task, we link the specificity of credit assignment-how well people are able to appropriately attribute outcomes to their causes-to the precision of neural codes in the prefrontal cortex (PFC). Participants credit task-relevant cues more precisely in social compared vto nonsocial contexts, a process that is mediated by high-fidelity (i.e., distinct and consistent) state representations in the PFC. Specifically, the medial PFC and orbitofrontal cortex work in concert to match the neural codes from feedback to those at choice, and the strength of these common neural codes predicts credit assignment precision. Together this work provides a window into how neural representations drive adaptive learning.

Shared neural representations and temporal segmentation of political content predict ideological similarity.

Despite receiving the same sensory input, opposing partisans often interpret political content in disparate ways. Jointly analyzing controlled and naturalistic functional magnetic resonance imaging data, we uncover the neurobiological mechanisms explaining how these divergent political viewpoints arise. Individuals who share an ideology have more similar neural representations of political words, experience greater neural synchrony during naturalistic political content, and temporally segment real-world information into the same meaningful units. In the striatum and amygdala, increasing intersubject similarity in neural representations of political concepts during a word reading task predicts enhanced synchronization of blood oxygen level-dependent time courses when viewing real-time, inflammatory political videos, revealing that polarization can arise from differences in the brain's affective valuations of political concepts. Together, this research shows that political ideology is shaped by semantic representations of political concepts processed in an environment free of any polarizing agenda and that these representations bias how real-world political information is construed into a polarized perspective.

Uncertainty aversion predicts the neural expansion of semantic representations.

Correctly identifying the meaning of a stimulus requires activating the appropriate semantic representation among many alternatives. One way to reduce this uncertainty is to differentiate semantic representations from each other, thereby expanding the semantic space. Here, in four experiments, we test this semantic-expansion hypothesis, finding that uncertainty-averse individuals exhibit increasingly differentiated and separated semantic representations. This effect is mirrored at the neural level, where uncertainty aversion predicts greater distances between activity patterns in the left inferior frontal gyrus when reading words, and enhanced sensitivity to the semantic ambiguity of these words in the ventromedial prefrontal cortex. Two direct tests of the behavioural consequences of semantic expansion further reveal that uncertainty-averse individuals exhibit reduced semantic interference and poorer generalization. Together, these findings show that the internal structure of our semantic representations acts as an organizing principle to make the world more identifiable.

A probabilistic map of emotional experiences during competitive social interactions.

Theories of emotion and decision-making argue that negative, high arousing emotions-such as anger-motivate competitive social choice (e.g., punishing and defecting). However, given the long-standing challenge of quantifying emotion and the narrow framework in which emotion is traditionally examined, it remains unclear which emotions are actually associated with motivating these types of choices. To address this gap, we combine machine learning algorithms with a measure of affect that is agnostic to any specific emotion label. The result is a probabilistic map of emotion that is used to classify the specific emotions experienced by participants in a variety of social interactions (Ultimatum Game, Prisoner's Dilemma, and Public Goods Game). Our results reveal that punitive and uncooperative choices are linked to a diverse array of negative, neutrally arousing emotions, such as sadness and disappointment, while only weakly linked to anger. These findings stand in contrast to the commonly held assumption that anger drives decisions to punish, defect, and freeride-thus, offering new insight into the role of emotion in motiving social choice.

A generalizable framework for assessing the role of emotion during choice.

The study of emotion has been plagued by several challenges that have left the field fractionated. To date, there is no dominant method for measuring the nebulous and often ill-defined experience of emotion. Here, we offer a new way forward, one that marries numerically precise measurements of affect with current models of human behavior, to more deeply understand the role of emotion during choice, and in particular, during social decision-making. This tool can be combined with multiple other measures that capture different features and levels of the emotional experience, making it particularly flexible to be used in any number of contexts. By operationalizing the classic circumplex model of affect so that it can deliver fine-grained, continuous measurements as affect evolves overtime, our goal is to provide a generalizable and flexible framework for computing affect to infer emotions so that we can assess their impact on human behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A tale of two perspectives: Reply to Schneider (2022).

Although Schneider (2022) suggests integrating our cognitive-mechanistic opinion piece with existentialist-humanistic perspectives, the two approaches answer fundamentally different questions about psychology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).