Search

To act adaptively in a complex and dynamic social landscape, humans must continually make inferences about who might be connected to whom. How do they solve this fundamental problem of social link prediction: inferring the existence of unobserved or potential relationships in their social network from noisy, limited information? We propose that people generate principled inferences by learning cognitive maps that systematically abstract over direct relations (friends) and multistep relations (e.g., friends-of-friends). We show that such abstracted cognitive maps enable a flexible solution for link prediction and provide a natural explanation for a variety of otherwise puzzling empirical observations in social psychology. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social link prediction and presents a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

Existing research into the psychological roots of political polarization centers around two main approaches: one studying cognitive traits that predict susceptibility to holding polarized beliefs and one studying contextual influences that spread and reinforce polarized attitudes. Although both accounts have made valuable progress, political polarization is neither a purely cognitive trait nor a contextual issue. We argue that a new approach aiming to uncover interactions between cognition and context will be fruitful for understanding how polarization arises. Furthermore, recent developments in neuroimaging methods can overcome long-standing issues of measurement and ecological validity to critically help identify in which psychological processing steps-e.g., attention, semantic understanding, emotion-polarization takes hold. This interdisciplinary research agenda can thereby provide new avenues for interventions against the political polarization that plagues democracies around the world. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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. 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 behavioral consequences of semantic-expansion further reveal that uncertainty averse individuals exhibit reduced semantic interference and poorer generalization. Together, these findings demonstrate that the internal structure of our semantic representations is shaped in a principled manner: aversion to uncertainty acts as an organizing principle to make the world more identifiable.

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.

The complex reward structure of the social world and the uncertainty endemic to social contexts poses a challenge for modeling. For example, during social interactions, the actions of one person influence the internal states of another. These social dependencies make it difficult to formalize social learning problems in a mathematically tractable way. While it is tempting to dispense with these complexities, they are a defining feature of social life. Because the structure of social interactions challenges the simplifying assumptions often made in models, they make an ideal testbed for computational models of cognition. By adopting a framework that embeds existing social knowledge into the model, we can go beyond explaining behaviors in laboratory tasks to explaining those observed in the wild.

Predicting the behaviour of others is an essential part of social cognition. Despite its ubiquity, social prediction poses a poorly understood generalization problem: we cannot assume that others will repeat past behaviour in new settings or that their future actions are entirely unrelated to the past. We demonstrate that humans solve this challenge using a structure learning mechanism that uncovers other people’s latent, unobservable motives, such as greed and risk aversion. In four studies, participants (N = 501) predicted other players’ decisions across four economic games, each with different social tensions (for example, Prisoner’s Dilemma and Stag Hunt). Participants achieved accurate social prediction by learning the stable motivational structure underlying a player’s changing actions across games. This motive-based abstraction enabled participants to attend to information diagnostic of the player’s next move and disregard irrelevant contextual cues. Participants who successfully learned another’s motives were more strategic in a subsequent competitive interaction with that player in entirely new contexts, reflecting that social structure learning supports adaptive social behaviour. How do we predict other peoples’ behaviour across a dizzying array of social settings? Van Baar et al. propose a structure learning mechanism that uncovers the hidden motives of others, which allows us to adaptively respond to their future actions.

Uncertainty is a fundamental feature of human life that can be fractioned into two distinct psychological constructs: risk (known probabilistic outcomes) and ambiguity (unknown probabilistic outcomes). Although risk and ambiguity are known to powerfully bias nonsocial decision-making, their influence on prosocial behavior remains largely unexplored. Here we show that ambiguity attitudes, but not risk attitudes, predict prosocial behavior: the greater an individual’s ambiguity tolerance, the more they engage in costly prosocial behaviors, both during decisions to cooperate (experiments 1 and 3) and choices to trust (experiment 2). Once the ambiguity associated with another’s actions is sufficiently resolved, this relationship between ambiguity tolerance and prosocial choice is eliminated (experiment 3). Taken together, these results provide converging evidence that attitudes toward ambiguity are a robust predictor of one’s willingness to engage in costly social behavior, which suggests a mechanism for the underlying motivations of prosocial action., Ambiguous uncertainty refers to situations where the likelihood of specific outcomes are not known. Here, the authors show that people tolerant to ambiguous uncertainty are more likely to make costly decisions to cooperate with or trust others.

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 STATEMENTAdaptively 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.

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.

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.

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.

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a new method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N=1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviors challenge standard decision-making models that have focused solely on reward.

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.

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.

Fear and anxiety about COVID-19 have swept across the globe. Understanding the factors that contribute to increased emotional distress regarding the pandemic is paramount—especially as experts warn about rising cases. Despite large amounts of data, it remains unclear which variables are essential for predicting who will be most affected by the distress of future waves. We collected cross-sectional data on a multitude of socio-psychological variables from a sample of 948 United States participants during the early stages of the pandemic. Using a cross-validated hybrid stepwise procedure, we developed a descriptive model of COVID-19 emotional distress. Results reveal that trait anxiety, gender, and social (but not government) media consumption were the strongest predictors of increasing emotional distress. In contrast, commonly associated variables, such as age and political ideology, exhibited much less unique explanatory power. Together, these results can help public health officials identify which populations will be especially vulnerable to experiencing COVID-19-related emotional distress.

Political partisans see the world through an ideologically biased lens. What drives political polarization? It has been posited that polarization arises because holding extreme political views satisfies a need for certain and stable beliefs about the world. We examined the relationship between uncertainty tolerance and political polarization using brain-to-brain synchrony analysis, which measured committed liberals’ and conservatives’ subjective interpretation of a continuous political narrative. Participants (N=44) watched a political debate while undergoing fMRI. Shared ideology between participants increased neural synchrony in many brain areas including key regions of the valuation and theory-of-mind networks (e.g. temporoparietal junction). The degree of neural synchrony was modulated by uncertainty aversion: Uncertainty-intolerant individuals experienced greater brain-to-brain synchrony with politically like-minded peers and lower synchrony with political opponents. This effect was observed for liberals and conservatives alike. Moreover, increasing neural synchrony between committed partisans predicted subsequent polarized attitude formation about the debate after the scanning session. These results suggest that uncertainty attitudes gate the shared neural processing of political narratives, thereby fueling polarized attitude formation about hot-button issues.

There is little consensus about how moral values are learned. Using a novel social learning task, we examine whether vicarious learning impacts moral values-specifically fairness preferences-during decisions to restore justice. In both laboratory and Internet-based experimental settings, we employ a dyadic justice game where participants receive unfair splits of money from another player and respond resoundingly to the fairness violations by exhibiting robust nonpunitive, compensatory behavior (baseline behavior). In a subsequent learning phase, participants are tasked with responding to fairness violations on behalf of another participant (a receiver) and are given explicit trial-by-trial feedback about the receiver's fairness preferences (e.g., whether they prefer punishment as a means of restoring justice). This allows participants to update their decisions in accordance with the receiver's feedback (learning behavior). In a final test phase, participants again directly experience fairness violations. After learning about a receiver who prefers highly punitive measures, participants significantly enhance their own endorsement of punishment during the test phase compared with baseline. Computational learning models illustrate the acquisition of these moral values is governed by a reinforcement mechanism, revealing it takes as little as being exposed to the preferences of a single individual to shift one's own desire for punishment when responding to fairness violations. Together this suggests that even in the absence of explicit social pressure, fairness preferences are highly labile. (PsycINFO Database Record

While decades of research demonstrate that people punish unfair treatment, recent work illustrates that alternative, non-punitive responses may also be preferred. Across five studies (N = 1,010) we examine non-punitive methods for restoring justice. We find that in the wake of a fairness violation, compensation is preferred to punishment, and once maximal compensation is available, punishment is no longer the favored response. Furthermore, compensating the victim—as a method for restoring justice—also generalizes to judgments of more severe crimes: participants allocate more compensation to the victim as perceived severity of the crime increases. Why might someone refrain from punishing a perpetrator? We investigate one possible explanation, finding that punishment acts as a conduit for different moral signals depending on the social context in which it arises. When choosing partners for social exchange, there are stronger preferences for those who previously punished as third-party observers but not those who punished as victims. This is in part because third-parties are perceived as relatively more moral when they punish, while victims are not. Together, these findings demonstrate that non-punitive alternatives can act as effective avenues for restoring justice, while also highlighting that moral reputation hinges on whether punishment is enacted by victims or third-parties.

Justice systems delegate punishment decisions to groups in the belief that the aggregation of individuals’ preferences facilitates judiciousness. However, group dynamics may also lead individuals to relinquish moral responsibility by conforming to the majority’s preference for punishment. Across five experiments (N = 399), we find Victims and Jurors tasked with restoring justice become increasingly punitive (by as much as 40%) as groups express a desire to punish, with every additional punisher augmenting an individual’s punishment rates. This influence is so potent that knowing about a past group’s preference continues swaying decisions even when they cannot affect present outcomes. Using computational models of decision-making, we test long-standing theories of how groups influence choice. We find groups induce conformity by making individuals less cautious and more impulsive, and by amplifying the value of punishment. However, compared to Victims, Jurors are more sensitive to moral violation severity and less readily swayed by the group. Conformity to a group’s punitive preference also extends to weightier moral violations such as assault and theft. Our results demonstrate that groups can powerfully shift an individual’s punitive preference across a variety of contexts, while additionally revealing the cognitive mechanisms by which social influence alters moral values.

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.

Individuals high in socioeconomic status (SES) are often viewed as valuable members of society. However, the appeal of high-SES people exists in tension with our aversion to inequity. Little experimental work has directly examined how people rectify inequitable distributions between two individuals varying in SES. The objective of the present study was to examine how disinterested third parties adjudicate inequity in the context of concrete financial allocations between a selfish allocator and a recipient who was the victim of the allocator’s selfish offer. Specifically, this study focused on whether knowing the SES of the victim or the allocator affected the participant’s decisions to punish the selfish allocator. In two experiments (N = 999), participants completed a modified third-party Ultimatum Game in which they arbitrated inequitable exchanges between an allocator and a recipient. Although participants generally preferred to redistribute inequitable exchanges without punishing players who made unfair allocations, we observed an increased preference for punitive solutions as offers became increasingly selfish. This tendency was especially pronounced when the victim was low in SES or when the perpetrator was high in SES, suggesting a tendency to favor the disadvantaged even among participants reporting high subjective SES. Finally, punitive responses were especially likely when the context emphasized the allocator’s privileged status rather than the recipient’s underprivileged status. These findings inform our understanding of how SES biases retributive justice even in non-judicial contexts that minimize the salience of punishment.

A complex web of social and moral norms governs many everyday human behaviors, acting as the glue for social harmony. The existence of moral norms helps elucidate the psychological motivations underlying a wide variety of seemingly puzzling behavior, including why humans help or trust total strangers. In this review, we examine four widespread moral norms: Fairness, altruism, trust, and cooperation, and consider how a single social instrument—reciprocity—underpins compliance to these norms. Using a game theoretic framework, we examine how both context and emotions moderate moral standards, and by extension, moral behavior. We additionally discuss how a mechanism of reciprocity facilitates the adherence to, and enforcement of, these moral norms through a core network of brain regions involved in processing reward. In contrast, violating this set of moral norms elicits neural activation in regions involved in resolving decision conflict and exerting cognitive control. Finally, we review how a reinforcement mechanism likely governs learning about morally normative behavior. Together, this review aims to explain how moral norms are deployed in ways that facilitate flexible moral choices.

Consider the range of social behaviours we engage in every day. In each case, there are a multitude of unknowns, reflecting the many sources of uncertainty inherent to social inference. We describe how uncertainty manifests in social environments (the thoughts and intentions of others are largely hidden, making it difficult to predict a person's behaviour) and why people are motivated to reduce the aversive feelings generated by uncertainty. We propose a three-part model whereby social uncertainty is initially reduced through automatic modes of inference (such as impression formation) before more control-demanding modes of inference (such as perspective-taking) are deployed to narrow one's predictions even more. Finally, social uncertainty is attenuated further through learning processes that update these predictions based on new information. Our framework integrates research across fields to offer an account of the mechanisms motivating social cognition and action, laying the groundwork for future experiments that can illuminate the impact of uncertainty on social cognition.

Significance Humans can learn to trust through direct social experiences. In our everyday lives, however, we constantly meet new people where judgments of trustworthiness are blind to reputation. In these cases, what drives decisions to trust? We find a simple learning mechanism observed across species—stimulus generalization—is deployed in complex social learning environments: Individuals distrust strangers who implicitly resemble those known to be untrustworthy. These behavioral findings were mirrored at the neural level, revealing that the amygdala and caudate selectively encode the transfer of social value during moral learning. The results demonstrate a mechanism that draws on prior learning to reduce the uncertainty associated with strangers, ultimately facilitating potentially adaptive decisions to trust, or withhold trust from, unfamiliar others., 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.

Despite the risks, people enjoy giving advice. One explanation is that giving beneficial advice can result in reflected glory, ego boosts or reputation enhancement. However, giving poor advice can be socially harmful (being perceived as incompetent or untrustworthy). In both circumstances, we have a vested interest in the advice follower's success or failure, especially when it reflects specifically on us compared with when it is diffused between multiple advisors. We examined these dynamics using an Advisor-Advisee Game, where subjects acted as an Advisor to a confederate Advisee who selected one of the three options when trying to win money: accept the subject's advice, accept the advice of a second confederate Advisor or accept both Advisors' advice. Results showed that having one's advice accepted, compared with being rejected, resulted in activity in the ventral striatum--a core reward area. Furthermore, the ventral striatum was only active when the subject's advice led to the advisee winning, and not when the advisee won based on the confederate's advice. Finally, the medial prefrontal cortex (MPFC) was more active when the Advisee won or lost money based solely on the subject's advice compared with when the second Advisor's advice was accepted. One explanation for these findings is that the MPFC monitors self-relevant social information, while the ventral striatum is active when others accept advice and when their success leads to reflected glory.

Item does not contain fulltext 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.

Moral perceptions of harm and fairness are instrumental in guiding how an individual navigates moral challenges. Classic research documents that the gender of a target can affect how people deploy these perceptions of harm and fairness. Across multiple studies, we explore the effect of an individual’s moral orientations (their considerations of harm and justice) and a target’s gender on altruistic behavior. Results reveal that a target’s gender can bias one’s readiness to engage in harmful actions and that a decider’s considerations of harm—but not fairness concerns—modulate costly altruism. Together, these data illustrate that moral choices are conditional on the social nature of the moral dyad: Even under the same moral constraints, a target’s gender and a decider’s gender can shift an individual’s choice to be more or less altruistic, suggesting that gender bias and harm considerations play a significant role in moral cognition.

Prior research illustrates that memory can guide value-based decision-making. For example, previous work has implicated both working memory and procedural memory (i.e., reinforcement learning) in guiding choice. However, other types of memories, such as episodic memory, may also influence decision-making. Here we test the role for episodic memory—specifically item versus associative memory—in supporting value-based choice. Participants completed a task where they first learned the value associated with trial unique lotteries. After a short delay, they completed a decision-making task where they could choose to re-engage with previously encountered lotteries, or new never before seen lotteries. Finally, participants completed a surprise memory test for the lotteries and their associated values. Results indicate that participants chose to re-engage more often with lotteries that resulted in high versus low rewards. Critically, participants not only formed detailed, associative memories for the reward values coupled with individual lotteries, but also exhibited adaptive decision-making only when they had intact associative memory. We further found that the relationship between adaptive choice and associative memory generalized to more complex, ecologically valid choice behavior, such as social decision-making. However, individuals more strongly encode experiences of social violations—such as being treated unfairly, suggesting a bias for how individuals form associative memories within social contexts. Together, these findings provide an important integration of episodic memory and decision-making literatures to better understand key mechanisms supporting adaptive behavior.

Highlights ► We show people are unable to appropriately judge outcomes of moral behaviour. ► Moral beliefs have weaker impact when there is a presence of significant self-gain. ► People make highly self-serving choices in real moral situations. ► Real moral choices contradict responses to simple hypothetical moral probes. ► Enhancing context can cause hypothetical decisions to mirror real moral decisions., Moral ideals are strongly ingrained within society and individuals alike, but actual moral choices are profoundly influenced by tangible rewards and consequences. Across two studies we show that real moral decisions can dramatically contradict moral choices made in hypothetical scenarios (Study 1). However, by systematically enhancing the contextual information available to subjects when addressing a hypothetical moral problem—thereby reducing the opportunity for mental simulation—we were able to incrementally bring subjects’ responses in line with their moral behaviour in real situations (Study 2). These results imply that previous work relying mainly on decontextualized hypothetical scenarios may not accurately reflect moral decisions in everyday life. The findings also shed light on contextual factors that can alter how moral decisions are made, such as the salience of a personal gain.

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.

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.

Research has illustrated that the brain regions implicated in moral cognition comprise a robust and broadly distributed network. However, understanding how these brain regions interact and give rise to the complex interplay of cognitive processes underpinning human moral cognition is still in its infancy. We used functional magnetic resonance imaging to examine patterns of activation for 'difficult' and 'easy' moral decisions relative to matched non-moral comparators. This revealed an activation pattern consistent with a relative functional double dissociation between the temporoparietal junction (TPJ) and ventro-medial prefrontal cortex (vmPFC). Difficult moral decisions activated bilateral TPJ and deactivated the vmPFC and OFC. In contrast, easy moral decisions revealed patterns of activation in the vmPFC and deactivation in bilateral TPJ and dorsolateral PFC. Together these results suggest that moral cognition is a dynamic process implemented by a distributed network that involves interacting, yet functionally dissociable networks.

Classic social psychology studies demonstrate that people can behave in ways that contradict their intentions--especially within the moral domain. We measured brain activity while subjects decided between financial self-benefit (earning money) and preventing physical harm (applying an electric shock) to a confederate under both real and hypothetical conditions. We found a shared neural network associated with empathic concern for both types of decisions. However, hypothetical and real moral decisions also recruited distinct neural circuitry: hypothetical moral decisions mapped closely onto the imagination network, while real moral decisions elicited activity in the bilateral amygdala and anterior cingulate--areas essential for social and affective processes. Moreover, during real moral decision-making, distinct regions of the prefrontal cortex (PFC) determined whether subjects make selfish or pro-social moral choices. Together, these results reveal not only differential neural mechanisms for real and hypothetical moral decisions but also that the nature of real moral decisions can be predicted by dissociable networks within the PFC.

Classic psychology and economic studies argue that punishment is the standard response to violations of fairness norms. Typically, individuals are presented with the option to punish the transgressor or not. However, such a narrow choice set may fail to capture stronger alternative preferences for restoring justice. Here we show, in contrast to the majority of findings on social punishment, that other forms of justice restoration (for example, compensation to the victim) are strongly preferred to punitive measures. Furthermore, these alternative preferences for restoring justice depend on the perspective of the deciding agent. When people are the recipient of an unfair offer, they prefer to compensate themselves without seeking retribution, even when punishment is free. Yet when people observe a fairness violation targeted at another, they change their decision to the most punitive option. Together these findings indicate that humans prefer alternative forms of justice restoration to punishment alone.