Associative learning of social value

Trait reward sensitivity modulates connectivity with the temporoparietal junction and Anterior Insula during strategic decision making

Many decisions happen in social contexts such as negotiations, yet little is understood about how people balance fairness versus selfishness. Past investigations found that activation in brain areas involved in executive function and reward processing was associated with people offering less with no threat of rejection from their partner, compared to offering more when there was a threat of rejection. However, it remains unclear how trait reward sensitivity may modulate activation and connectivity patterns in these situations. To address this gap, we used task-based fMRI to examine the relation between reward sensitivity and the neural correlates of bargaining choices. Participants (N = 54) completed the Sensitivity to Punishment (SP)/Sensitivity to Reward (SR) Questionnaire and the Behavioral Inhibition System/Behavioral Activation System scales. Participants performed the Ultimatum and Dictator Games as proposers and exhibited strategic decisions by being fair when there was a threat of rejection, but being selfish when there was not a threat of rejection. We found that strategic decisions evoked activation in the Inferior Frontal Gyrus (IFG) and the Anterior Insula (AI). Next, we found elevated IFG connectivity with the Temporoparietal junction (TPJ) during strategic decisions. Finally, we explored whether trait reward sensitivity modulated brain responses while making strategic decisions. We found that people who scored lower in reward sensitivity made less strategic choices when they exhibited higher AI-Angular Gyrus connectivity. Taken together, our results demonstrate how trait reward sensitivity modulates neural responses to strategic decisions, potentially underscoring the importance of this factor within social and decision neuroscience.

How adolescents learn to build social bonds: A developmental computational account of social explore-exploit decision-making

Building social bonds is a critical task of adolescence that affords opportunities for learning, identity formation, and social support. Failing to develop close relationships in adolescence hinders adult interpersonal functioning and contributes to problems such as loneliness and depression. During adolescence, increased reward sensitivity and greater social flexibility both contribute to healthy social development, yet we lack a clear theory of how these processes interact to support social functioning. Here, we propose synthesizing these two literatures using a computational reinforcement learning framework that recasts how adolescents pursue and learn from social rewards as a social explore-exploit problem. To become socially skilled, adolescents must balance both their efforts to form individual bonds within specific groups and manage memberships across multiple groups to maximize access to social resources. We draw on insights from sociological studies on social capital in collective networks and neurocognitive research on foraging and cooperation to describe the social explore-exploit dilemma faced by adolescents navigating a modern world with increasing access to diverse resources and group memberships. Our account provides important new directions for examining the dynamics of adolescent behavior in social groups and understanding how social value computations can support positive relationships into adulthood.

Older adults are relatively more susceptible to impulsive social influence than young adults

People differ in their levels of impulsivity and patience, and these preferences are heavily influenced by others. Previous research suggests that susceptibility to social influence may vary with age, but the mechanisms and whether people are more influenced by patience or impulsivity remain unknown. Here, using a delegated inter-temporal choice task and Bayesian computational models, we tested susceptibility to social influence in young (aged 18-36, N = 76) and older (aged 60-80, N = 78) adults. Participants completed a temporal discounting task and then learnt the preferences of two other people (one more impulsive and one more patient) before making their choices again. We used the signed Kullback-Leibler divergence to quantify the magnitude and direction of social influence. We found that, compared to young adults, older adults were relatively more susceptible to impulsive social influence. Factor analyses showed that older adults with higher self-reported levels of affective empathy and emotional motivation were particularly susceptible to impulsive influence. Importantly, older and young adults showed similar learning accuracy about others' preferences, and their baseline impulsivity did not differ. Together, these findings suggest highly affectively empathetic and emotionally motivated older adults may be at higher risk for impulsive decisions, due to their susceptibility to social influence.

Joyful growth vs. compulsive hedonism: A meta-analysis of brain activation on romantic love and addictive disorders

Due to the similarities in behavioral characteristics between romantic love and addictive disorders, the concept of being "addicted to someone" transcends mere literary metaphor, expanding perspectives on the study of romantic love and inspiring interventions for addiction. However, there has been a lack of studies systematically exploring the similarities and differences between romantic love and addiction at the neural level. In this study, we conducted an extensive literature search, incorporating 21 studies on romantic love and 28 on addictive disorders, focusing on fMRI research utilizing the cue reactivity paradigm. Using Activation Likelihood Estimation, we examined the similarities and differences in the neural mechanisms underlying love and addiction. The results showed that the anterior cingulate cortex (ACC) exhibited both shared and distinct activation clusters between romantic love and addictive disorders. Furthermore, ventromedial prefrontal cortex (VMPFC) was more frequently activated in romantic love than in addictive disorders, while greater activation within the posterior cingulate cortex (PCC) was found in addictive disorder compared with romantic love. We discussed that the activation of ACC and VMPFC may symbolize self-expansion, a process that characterizes the development of romantic love, contributing to a more enriched self. Our study suggests that while romantic love and addictive disorders share a common neural foundation, the discernible differences in their neural representations distinguish them as joyful growth versus compulsive hedonism.

Revisiting the role of computational neuroimaging in the era of integrative neuroscience

Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.

Neural populations in macaque anterior cingulate cortex encode social image identities

The anterior cingulate cortex gyrus (ACCg) has been implicated in prosocial behaviors and reasoning about social cues. While this indicates that ACCg is involved in social behavior, it remains unclear whether ACCg neurons also encode social information during goal-directed actions without social consequences. To address this, we assessed how social information is processed by ACCg neurons in a reward localization task. Here we show that neurons in the ACCg of female rhesus monkeys differentiate the identities of conspecifics in task images, even when identity was task-irrelevant. This was in contrast to the prearcuate cortex (PAC), which has not been strongly linked to social behavior, where neurons differentiated identities in both social and nonsocial images. Many neurons in the ACCg also categorically distinguished social from nonsocial trials, but this encoding was only slightly more common in ACCg compared to the PAC. Together, our results suggest that ACCg neurons are uniquely sensitive to social information that differentiates individuals, which may underlie its role in complex social reasoning.

Trait Reward Sensitivity Modulates Connectivity with the Temporoparietal Junction and Anterior Insula during Strategic Decision Making

Many decisions happen in social contexts such as negotiations, yet little is understood about how people balance fairness versus selfishness. Past investigations found that activation in brain areas involved in executive function and reward processing was associated with people offering less with no threat of rejection from their partner, compared to offering more when there was a threat of rejection. However, it remains unclear how trait reward sensitivity may modulate activation and connectivity patterns in these situations. To address this gap, we used task-based fMRI to examine the relation between reward sensitivity and the neural correlates of bargaining choices. Participants (N = 54) completed the Sensitivity to Punishment (SP)/Sensitivity to Reward (SR) Questionnaire and the Behavioral Inhibition System/Behavioral Activation System scales. Participants performed the Ultimatum and Dictator Games as proposers and exhibited strategic decisions by being fair when there was a threat of rejection, but being selfish when there was not a threat of rejection. We found that strategic decisions evoked activation in the Inferior Frontal Gyrus (IFG) and the Anterior Insula (AI). Next, we found elevated IFG connectivity with the Temporoparietal junction (TPJ) during strategic decisions. Finally, we explored whether trait reward sensitivity modulated brain responses while making strategic decisions. We found that people who scored lower in reward sensitivity made less strategic choices when they exhibited higher AI-Angular Gyrus connectivity. Taken together, our results demonstrate how trait reward sensitivity modulates neural responses to strategic decisions, potentially underscoring the importance of this factor within social and decision neuroscience.

Closed-loop microstimulations of the orbitofrontal cortex during real-life gaze interaction enhance dynamic social attention

Neurons from multiple prefrontal areas encode several key variables of social gaze interaction. To explore the causal roles of the primate prefrontal cortex in real-life gaze interaction, we applied weak closed-loop microstimulations that were precisely triggered by specific social gaze events. Microstimulations of the orbitofrontal cortex, but not the dorsomedial prefrontal cortex or the anterior cingulate cortex, enhanced momentary dynamic social attention in the spatial dimension by decreasing the distance of fixations relative to a partner's eyes and in the temporal dimension by reducing the inter-looking interval and the latency to reciprocate the other's directed gaze. By contrast, on a longer timescale, microstimulations of the dorsomedial prefrontal cortex modulated inter-individual gaze dynamics relative to one's own gaze positions. These findings demonstrate that multiple regions in the primate prefrontal cortex may serve as functionally accessible nodes in controlling different aspects of dynamic social attention and suggest their potential for a therapeutic brain interface.

Electrophysiological correlates of (mis)judging social information

Social information can be used to optimize decision-making. However, the simultaneous presentation of multiple sources of advice can lead to a distinction bias in judging the validity of the information. While the involvement of event-related potential (ERP) components in social information processing has been studied, how they are modulated by (mis)judging an advisor's information validity remains unknown. In two experiments participants performed a decision-making task with highly accurate or inaccurate cues. Each experiment consisted of an initial, learning, and test phase. During the learning phase, three advice cues were simultaneously presented and the validity of them had to be assessed. The effect of different cue constellations on ERPs was investigated. In the subsequent test phase, the willingness to follow or oppose an advice cue was tested. Results demonstrated the distinction bias with participants over or underestimating the accuracy of the most uncertain cues. The P2 amplitude was significantly increased during cue presentation when advisors were in disagreement as compared to when all were in agreement, regardless of cue validity. Further, a larger P3 amplitude during outcome presentation was found when advisors were in disagreement and increased with more informative cues. As such, the most uncertain cues were related to the smallest P3 amplitude. The findings hint at the possible role of P3 in judging and learning the predictability of social cues. This study provides novel insights into the role of P2 and P3 components during the judgment of social information validity.

The Impact of Continuous and Partial Reinforcement on the Acquisition and Generalization of Human-Conditioned Fear

Fear over-generalization as a core symptom of anxiety disorders is manifested by fear responses even to safe stimuli that are very dissimilar to the original dangerous stimulus. The present study investigated the effects of two separate conditioned stimuli-unconditioned stimuli (CS-US) pairing procedures on fear acquisition and generalization using a perceptual discrimination fear-conditioning paradigm, with US expectancy ratings and skin conductance response (SCR) as indicators. One group accepted continuous followed by partial CS-US pairings (C-P group); the other group accepted partial followed by continuous CS-US pairings (P-C group). It was found that compared to the P-C group, the C-P group showed stronger perceptual discrimination of CS+ and CS- in the fear acquisition and showed weaker SCRs and stronger extinction of US expectancy in the generalization. These findings emphasize that CS-US pairings significantly influence fear acquisition and generalization and suggest that continuous-following partial CS-US pairings promote individual discrimination of threat and safety signals and inhibit the generalization of conditioned fear. The results of this study have implications for clinical interventions for patients experiencing negative events.

Enhancing human-AI collaboration: The case of colonoscopy

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Attachment and borderline personality disorder as the dance unfolds: A quantitative analysis of a novel paradigm

Current research on personality disorders strives to identify key behavioural and cognitive facets of patient functioning, to unravel the underlying root causes and maintenance mechanisms. This process often involves the application of social paradigms - however, these often only include momentary affective depictions rather than unfolding interactions. This constitutes a limitation in our capacity to probe core symptoms, and leaves potential findings uncovered which could help those who are in close relationships with affected individuals. Here, we deployed a novel task in which subjects interact with four unknown virtual partners in a turn-taking paradigm akin to a dance, and report on their experience with each. The virtual partners embody four combinations of low/high expressivity of positive/negative mood. Higher scores on our symptomatic measures of attachment anxiety, avoidance, and borderline personality disorder (BPD) were all linked to a general negative appraisal of all the interpersonal experiences. Moreover, the negative appraisal of the partner who displayed a high negative/low positive mood was tied with attachment anxiety and BPD symptoms. The extent to which subjects felt responsible for causing partners' distress was most strongly linked to attachment anxiety. Finally, we provide a fully-fledged exploration of move-by-move action latencies and click distances from partners. This analysis underscored slower movement initiation from anxiously attached individuals throughout all virtual interactions. In summary, we describe a novel paradigm for second-person neuroscience, which allowed both the replication of established results and the capture of new behavioural signatures associated with attachment anxiety, and discuss its limitations.

Belief Updating during Social Interactions: Neural Dynamics and Causal Role of Dorsomedial Prefrontal Cortex

In competitive interactions, humans have to flexibly update their beliefs about another person's intentions in order to adjust their own choice strategy, such as when believing that the other may exploit their cooperativeness. Here we investigate both the neural dynamics and the causal neural substrate of belief updating processes in humans. We used an adapted prisoner's dilemma game in which participants explicitly predicted the coplayer's actions, which allowed us to quantify the prediction error between expected and actual behavior. First, in an EEG experiment, we found a stronger medial frontal negativity (MFN) for negative than positive prediction errors, suggesting that this medial frontal ERP component may encode unexpected defection of the coplayer. The MFN also predicted subsequent belief updating after negative prediction errors. In a second experiment, we used transcranial magnetic stimulation (TMS) to investigate whether the dorsomedial prefrontal cortex (dmPFC) causally implements belief updating after unexpected outcomes. Our results show that dmPFC TMS impaired belief updating and strategic behavioral adjustments after negative prediction errors. Taken together, our findings reveal the time course of the use of prediction errors in social decisions and suggest that the dmPFC plays a crucial role in updating mental representations of others' intentions.

Heterogeneity in strategy use during arbitration between experiential and observational learning

To navigate our complex social world, it is crucial to deploy multiple learning strategies, such as learning from directly experiencing action outcomes or from observing other people's behavior. Despite the prevalence of experiential and observational learning in humans and other social animals, it remains unclear how people favor one strategy over the other depending on the environment, and how individuals vary in their strategy use. Here, we describe an arbitration mechanism in which the prediction errors associated with each learning strategy influence their weight over behavior. We designed an online behavioral task to test our computational model, and found that while a substantial proportion of participants relied on the proposed arbitration mechanism, there was some meaningful heterogeneity in how people solved this task. Four other groups were identified: those who used a fixed mixture between the two strategies, those who relied on a single strategy and non-learners with irrelevant strategies. Furthermore, groups were found to differ on key behavioral signatures, and on transdiagnostic symptom dimensions, in particular autism traits and anxiety. Together, these results demonstrate how large heterogeneous datasets and computational methods can be leveraged to better characterize individual differences.

Prosocial emotions predict individual differences in economic decision-making during ultimatum game with dynamic reciprocal contexts

Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states.

Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others

Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.

Dopamine and serotonin in human substantia nigra track social context and value signals during economic exchange

Dopamine and serotonin are hypothesized to guide social behaviours. In humans, however, we have not yet been able to study neuromodulator dynamics as social interaction unfolds. Here, we obtained subsecond estimates of dopamine and serotonin from human substantia nigra pars reticulata during the ultimatum game. Participants, who were patients with Parkinson's disease undergoing awake brain surgery, had to accept or reject monetary offers of varying fairness from human and computer players. They rejected more offers in the human than the computer condition, an effect of social context associated with higher overall levels of dopamine but not serotonin. Regardless of the social context, relative changes in dopamine tracked trial-by-trial changes in offer value-akin to reward prediction errors-whereas serotonin tracked the current offer value. These results show that dopamine and serotonin fluctuations in one of the basal ganglia's main output structures reflect distinct social context and value signals.

How and when social evaluative feedback is processed in the brain: A systematic review on ERP studies

Social evaluative feedback informs the receiver of the other's views, which may contain judgments of personality-related traits and/or the level of likability. Such kinds of social evaluative feedback are of particular importance to humans. Event-related potentials (ERPs) can directly measure where in the processing stream feedback valence, expectancy, or contextual relevance modulate information processing. This review provides an overview and systematization of studies and early, mid-latency, and late ERP effects. Early effects were inconsistently reported for all factors. Feedback valence effects are more consistently reported for specific mid-latency ERPs (Reward Positivity, RewP, and Early Posterior Negativity, EPN) and late positivities (P3 and Late Positive Potential, LPP). Unexpected feedback consistently increased the Feedback Related Negativity (FRN) and, less consistently, decreased P3 amplitudes. Contextual relevance of the sender (e.g., human vs computer sender) or self-relatedness increased mid-latency to late ERPs. Interactions between valence and other factors were less often found, arising during mid-latency stages, where most consistent interactions showed larger EPN and P3 amplitude differences for valent feedback in a more relevant context. The ERP findings highlight that social evaluative feedback is consistently differentiated during mid-latency processing stages. The review discusses the relevance of findings, possible shortcomings of different experimental designs, and open questions. Furthermore, we suggest concrete venues for future research.

Interpersonal neural synchronization during social interactions in close relationships: A systematic review and meta-analysis of fNIRS hyperscanning studies

In recent years, researchers have used hyperscanning techniques to explore how brains interact during various human activities. These studies have revealed a phenomenon called interpersonal neural synchronization (INS), but little research has focused on the overall effect of INS in close relationships. To address this gap, this study aims to synthesize and analyze the existing literature on INS during social interactions in close relationships. We conducted a meta-analysis of 17 functional near-infrared spectroscopy (fNIRS) hyperscanning studies involving 1149 dyads participants, including romantic couples and parent-child dyads. The results revealed robust and consistent INS in the frontal, temporal, and parietal regions of the brain and found similar INS patterns in couples and parent-child studies, providing solid empirical evidence for the attachment theory. Moreover, the age of children and brain areas were significant predictors of the effect size in parent-child research. The developmental stage of children and the mismatched development of brain structures might be the crucial factors for the difference in neural performance in social and cognitive behaviors in parent-child dyads.

Dopamine-independent effect of rewards on choices through hidden-state inference

Dopamine is implicated in adaptive behavior through reward prediction error (RPE) signals that update value estimates. There is also accumulating evidence that animals in structured environments can use inference processes to facilitate behavioral flexibility. However, it is unclear how these two accounts of reward-guided decision-making should be integrated. Using a two-step task for mice, we show that dopamine reports RPEs using value information inferred from task structure knowledge, alongside information about reward rate and movement. Nonetheless, although rewards strongly influenced choices and dopamine activity, neither activating nor inhibiting dopamine neurons at trial outcome affected future choice. These data were recapitulated by a neural network model where cortex learned to track hidden task states by predicting observations, while basal ganglia learned values and actions via RPEs. This shows that the influence of rewards on choices can stem from dopamine-independent information they convey about the world's state, not the dopaminergic RPEs they produce.

Disentangling the contribution of individual and social learning processes in human advice-taking behavior

The study of social learning examines how individuals learn from others by means of observation, imitation, or compliance with advice. However, it still remains largely unknown whether social learning processes have a distinct contribution to behavior, independent from non-social trial-and-error learning that often occurs simultaneously. 153 participants completed a reinforcement learning task, where they were asked to make choices to gain rewards. Advice from an artificial teacher was presented in 60% of the trials, allowing us to compare choice behavior with and without advice. Results showed a strong and reliable tendency to follow advice (test-retest reliability ~0.73). Computational modeling suggested a unique contribution of three distinct learning strategies: (a) individual learning (i.e., learning the value of actions, independent of advice), (b) informed advice-taking (i.e., learning the value of following advice), and (c) non-informed advice-taking (i.e., a constant bias to follow advice regardless of outcome history). Comparing artificial and empirical data provided specific behavioral regression signatures to both informed and non-informed advice taking processes. We discuss the theoretical implications of integrating internal and external information during the learning process.

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Q-learning model of insight problem solving and the effects of learning traits on creativity

Despite the fact that insight is a crucial component of creative thought, the means by which it is cultivated remain unknown. The effects of learning traits on insight, specifically, has not been the subject of investigation in pertinent research. This study quantitatively examines the effects of individual differences in learning traits estimated using a Q-learning model within the reinforcement learning framework and evaluates their effects on insight problem solving in two tasks, the 8-coin and 9-dot problems, which fall under the umbrella term "spatial insight problems." Although the learning characteristics of the two problems were different, the results showed that there was a transfer of learning between them. In particular, performance on the insight tasks improved with increasing experience. Moreover, loss-taking, as opposed to loss aversion, had a significant effect on performance in both tasks, depending on the amount of experience one had. It is hypothesized that loss acceptance facilitates analogical transfer between the two tasks and improves performance. In addition, this is one of the few studies that attempted to analyze insight problems using a computational approach. This approach allows the identification of the underlying learning parameters for insight problem solving.

A frontopolar-temporal circuit determines the impact of social information in macaque decision making

When choosing, primates are guided not only by personal experience of objects but also by social information such as others' attitudes toward the objects. Crucially, both sources of information-personal and socially derived-vary in reliability. To choose optimally, one must sometimes override choice guidance by personal experience and follow social cues instead, and sometimes one must do the opposite. The dorsomedial frontopolar cortex (dmFPC) tracks reliability of social information and determines whether it will be attended to guide behavior. To do this, dmFPC activity enters specific patterns of interaction with a region in the mid-superior temporal sulcus (mSTS). Reversible disruption of dmFPC activity with transcranial ultrasound stimulation (TUS) led macaques to fail to be guided by social information when it was reliable but to be more likely to use it when it was unreliable. By contrast, mSTS disruption uniformly downregulated the impact of social information on behavior.

Flexible Cultural Learning Through Action Coordination

The cultural transmission of technical know-how has proven vital to the success of our species. The broad diversity of learning contexts and social configurations, as well as the various kinds of coordinated interactions they involve, speaks to our capacity to flexibly adapt to and succeed in transmitting vital knowledge in various learning contexts. Although often recognized by ethnographers, the flexibility of cultural learning has so far received little attention in terms of cognitive mechanisms. We argue that a key feature of the flexibility of cultural learning is that both the models and learners recruit cognitive mechanisms of action coordination to modulate their behavior contingently on the behavior of their partner, generating a process of mutual adaptation supporting the successful transmission of technical skills in diverse and fluctuating learning environments. We propose that the study of cultural learning would benefit from the experimental methods, results, and insights of joint-action research and, complementarily, that the field of joint-action research could expand its scope by integrating a learning and cultural dimension. Bringing these two fields of research together promises to enrich our understanding of cultural learning, its contextual flexibility, and joint action coordination.

Normative Processing Needs Multiple Levels of Explanation: From Algorithm to Implementation

Norms are the rules about what is allowed or forbidden by social groups. A key debate for norm psychology is whether these rules arise from mechanisms that are domain-specific and genetically inherited or domain-general and deployed for many other nonnorm processes. Here we argue for the importance of assessing and testing domain-specific and domain-general processes at multiple levels of explanation, from algorithmic (psychological) to implementational (neural). We also critically discuss findings from cognitive neuroscience supporting that social and nonsocial learning processes, essential for accounts of cultural evolution, can be dissociated at these two levels. This multilevel framework can generate new hypotheses and empirical tests of cultural evolution accounts of norm processing against purely domain-specific nativist alternatives.

Reduced neural encoding of utility prediction errors in cocaine addiction

Influential accounts of addiction posit alterations in adaptive behavior driven by deficient dopaminergic prediction errors (PEs), signaling the discrepancy between actual and expected reward. Dopamine neurons encode these error signals in subjective terms, calibrated by individual risk preferences, as "utility" PEs. It remains unclear, however, whether people with drug addiction have PE deficits or their computational source. Here, using an analogous task to prior single-unit studies with known expectancies, we show that fMRI-measured PEs similarly reflect utility PEs. Relative to control participants, people with chronic cocaine addiction demonstrate reduced utility PEs in the dopaminoceptive ventral striatum, with similar trends in orbitofrontal cortex. Dissecting this PE signal into its subcomponent terms attributed these reductions to weaker striatal responses to received reward/utility, whereas suppression of activity with reward expectation was unchanged. These findings support that addiction may fundamentally disrupt PE signaling and reveal an underappreciated role for perceived reward value in this mechanism.

A Neural Mechanism in the Human Orbitofrontal Cortex for Preferring High-Fat Foods Based on Oral Texture

Although overconsumption of high-fat foods is a major driver of weight gain, the neural mechanisms that link the oral sensory properties of dietary fat to reward valuation and eating behavior remain unclear. Here we combine novel food-engineering approaches with functional neuroimaging to show that the human orbitofrontal cortex (OFC) translates oral sensations evoked by high-fat foods into subjective economic valuations that guide eating behavior. Male and female volunteers sampled and evaluated nutrient-controlled liquid foods that varied in fat and sugar ("milkshakes"). During oral food processing, OFC activity encoded a specific oral-sensory parameter that mediated the influence of the foods' fat content on reward value: the coefficient of sliding friction. Specifically, OFC responses to foods in the mouth reflected the smooth, oily texture (i.e., mouthfeel) produced by fatty liquids on oral surfaces. Distinct activity patterns in OFC encoded the economic values associated with particular foods, which reflected the subjective integration of sliding friction with other food properties (sugar, fat, viscosity). Critically, neural sensitivity of OFC to oral texture predicted individuals' fat preferences in a naturalistic eating test: individuals whose OFC was more sensitive to fat-related oral texture consumed more fat during ad libitum eating. Our findings suggest that reward systems of the human brain sense dietary fat from oral sliding friction, a mechanical food parameter that likely governs our daily eating experiences by mediating interactions between foods and oral surfaces. These findings identify a specific role for the human OFC in evaluating oral food textures to mediate preference for high-fat foods.SIGNIFICANCE STATEMENT Fat and sugar enhance the reward value of food by imparting a sweet taste and rich mouthfeel but also contribute to overeating and obesity. Here we used a novel food-engineering approach to realistically quantify the physical-mechanical properties of high-fat liquid foods on oral surfaces and used functional neuroimaging while volunteers sampled these foods and placed monetary bids to consume them. We found that a specific area of the brain's reward system, the orbitofrontal cortex, detects the smooth texture of fatty foods in the mouth and links these sensory inputs to economic valuations that guide eating behavior. These findings can inform the design of low-calorie fat-replacement foods that mimic the impact of dietary fat on oral surfaces and neural reward systems.

Neurocomputational mechanisms of biased impression formation in lonely individuals

Social impressions are fundamental in our daily interactions with other people but forming accurate impressions of our social partners can be biased to different extents. Loneliness has previously been suggested to induce biases that hinder the formation of accurate impressions of others for successful social bonding. Here, we demonstrated that despite counterfactual evidence, negative first impressions bias information weighting, leading to less favorable trustworthiness beliefs. Lonely individuals did not only have more negative expectations of others' social behavior, but they also manifested a stronger weighting bias. Reduced orbitofrontal cortex (OFC) activity was associated with a stronger weighting bias in lonelier individuals and mediated the relationship between loneliness and this weighting bias. Importantly, stronger coupling between OFC and temporoparietal junction compensated for such effects, promoting more positive trustworthiness beliefs especially in lonelier individuals. These findings bear potential for future basic and clinical investigations on social cognition and the development of clinical symptoms linked to loneliness.

Economic and social values in the brain: evidence from lesions to the human ventromedial prefrontal cortex

Making good economic and social decisions is essential for individual and social welfare. Decades of research have provided compelling evidence that damage to the ventromedial prefrontal cortex (vmPFC) is associated with dramatic personality changes and impairments in economic and social decision-making. However, whether the vmPFC subserves a unified mechanism in the social and non-social domains remains unclear. When choosing between economic options, the vmPFC is thought to guide decision by encoding value signals that reflect the motivational relevance of the options on a common scale. A recent framework, the "extended common neural currency" hypothesis, suggests that the vmPFC may also assign values to social factors and principles, thereby guiding social decision-making. Although neural value signals have been observed in the vmPFC in both social and non-social studies, it is yet to be determined whether they have a causal influence on behavior or merely correlate with decision-making. In this review, we assess whether lesion studies of patients with vmPFC damage offer evidence for such a causal role of the vmPFC in shaping economic and social behavior.

Modulation of Trust in Borderline Personality Disorder by Script-Based Imaginal Exposure to Betrayal

Interpersonal and trust-related difficulties are central features of borderline personality disorder (BPD). In this study, we applied script-driven betrayal imagery to evoke mistrustful behavior in a social reinforcement learning task. In 21 BPD and 20 healthy control (HC) participants, we compared this approach to the standard confederate paradigm used in research studies. The script-driven imagery evoked a transient increase in negative affect and also decreased trusting behavior to a similar degree in both groups. Across conditions, we also replicated previously reported between-group differences in negative affect (increased in BPD) and task behavior (more sensitive to social cues in BPD). These results support the validity of script-driven imagery as an alternative social task stimulus. This script-driven imagery approach is appealing for clinical research studies on reinforcement learning because it eliminates deception, scales easily, and evokes disorder-specific states of social difficulty.

Flexible social inference facilitates targeted social learning when rewards are not observable

Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behaviour. We compared our social inference model against simpler heuristics in three studies of human behaviour in a collective-sensing task. Experiment 1 demonstrated that average performance improved as a function of group size at a rate greater than predicted by heuristic models. Experiment 2 introduced artificial agents to evaluate how individuals selectively rely on social information. Experiment 3 generalized these findings to a more complex reward landscape. Taken together, our findings provide insight into the relationship between individual social cognition and the flexibility of collective behaviour.

A calculus of probability or belief? Neural underpinnings of social decision-making in a card game

For decades, the prefrontal cortex (PFC) has been the focus of social neuroscience research, specifically regarding its role in competitive social decision-making. However, the distinct contributions of PFC subregions when making strategic decisions involving multiple types of information (social, non-social, and mixed information) remain unclear. This study investigates decision-making strategies (pure probability calculation vs. mentalizing) and their neural representations using functional near-infrared spectroscopy (fNIRS) data collected during a two-person card game. We observed individual differences in information processing strategy, indicating that some participants relied more on probability than others. Overall, the use of pure probability decreased over time in favor of other types of information (e.g., mixed information), with this effect being more pronounced within-round trials than across rounds. In the brain, (1) the lateral PFC activates when decisions are driven by probability calculations; (2) the right lateral PFC responds to trial difficulty; and (3) the anterior medial PFC is engaged when decision-making involves mentalizing. Furthermore, neural synchrony, which reflects the real-time interplay between individuals' cognitive processes, did not consistently contribute to correct decisions and fluctuated throughout the experiment, suggesting a hierarchical mentalizing mechanism at work.

Brain functional alterations in patients with anorexia nervosa: A meta-analysis of task-based functional MRI studies

The goal of this study was to discern the neural activation patterns associated with anorexia nervosa (AN) in response to tasks related to body-, food-, emotional-, cognitive-, and reward- processing. A meta-analysis was performed on task-based fMRI studies, revealing that patients with AN showed increased activity in the left superior temporal gyrus and bilaterally in the ACC during a reward-related task. During cognitive-related tasks, patients with AN also showed increased activity in the left superior parietal gyrus, right middle temporal gyrus, but decreased activity in the MCC. Additionally, patients with AN showed increased activity bilaterally in the cerebellum, MCC, and decreased activity bilaterally in the bilateral precuneus/PCC, right middle temporal gyrus, left ACC when they viewed food images. During emotion-related tasks, patients with AN showed increased activity in the left cerebellum, but decreased activity bilaterally in the striatum, right mPFC, and right superior parietal gyrus. Patients with AN also showed increased activity in the right striatum and decreased activity in the right inferior temporal gyrus and bilaterally in the mPFC during body-related tasks. The present meta-analysis provides a comprehensive overview of the patterns of brain activity evoked by task stimuli, thereby augmenting the current comprehension of the pathophysiology in AN.

Social overshadowing: Revisiting cue-competition in social interactions

In a large variety of contexts, it is essential to use the available information to extract patterns and behave accordingly. When it comes to social interactions for instance, the information gathered about interaction partners across multiple encounters (e.g., trustworthiness) is crucial in guiding one's own behavior (e.g., approach the trustworthy and avoid the untrustworthy), a process akin to trial-by-trial learning. Building on associative learning and social cognition literatures, the present research adopts a domain-general approach to learning and explores whether the principles underlying associative learning also govern learning in social contexts. In particular, we examined whether overshadowing, a well-established cue-competition phenomenon, impacts learning of the cooperative behaviors of unfamiliar interaction partners. Across three experiments using an adaptation of the iterated Trust Game, we consistently observed a 'social overshadowing' effect, that is, a better learning about the cooperative tendencies of partners presented alone compared to those presented in a pair. This robust effect was not modulated by gender stereotypes or beliefs about the internal communication dynamics within a pair of partners. Drawing on these results, we argue that examining domain-general learning processes in social contexts is a useful approach to understanding human social cognition.

How we learn social norms: a three-stage model for social norm learning

As social animals, humans are unique to make the world function well by developing, maintaining, and enforcing social norms. As a prerequisite among these norm-related processes, learning social norms can act as a basis that helps us quickly coordinate with others, which is beneficial to social inclusion when people enter into a new environment or experience certain sociocultural changes. Given the positive effects of learning social norms on social order and sociocultural adaptability in daily life, there is an urgent need to understand the underlying mechanisms of social norm learning. In this article, we review a set of works regarding social norms and highlight the specificity of social norm learning. We then propose an integrated model of social norm learning containing three stages, i.e., pre-learning, reinforcement learning, and internalization, map a potential brain network in processing social norm learning, and further discuss the potential influencing factors that modulate social norm learning. Finally, we outline a couple of future directions along this line, including theoretical (i.e., societal and individual differences in social norm learning), methodological (i.e., longitudinal research, experimental methods, neuroimaging studies), and practical issues.

A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders

Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.

Hierarchical inference as a source of human biases

The finding that human decision-making is systematically biased continues to have an immense impact on both research and policymaking. Prevailing views ascribe biases to limited computational resources, which require humans to resort to less costly resource-rational heuristics. Here, we propose that many biases in fact arise due to a computationally costly way of coping with uncertainty-namely, hierarchical inference-which by nature incorporates information that can seem irrelevant. We show how, in uncertain situations, Bayesian inference may avail of the environment's hierarchical structure to reduce uncertainty at the cost of introducing bias. We illustrate how this account can explain a range of familiar biases, focusing in detail on the halo effect and on the neglect of base rates. In each case, we show how a hierarchical-inference account takes the characterization of a bias beyond phenomenological description by revealing the computations and assumptions it might reflect. Furthermore, we highlight new predictions entailed by our account concerning factors that could mitigate or exacerbate bias, some of which have already garnered empirical support. We conclude that a hierarchical inference account may inform scientists and policy makers with a richer understanding of the adaptive and maladaptive aspects of human decision-making.

Teachers recruit mentalizing regions to represent learners' beliefs

Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers' decisions about what information to communicate. Participants (N = 28) played the role of teachers while being scanned using fMRI; their task was to select examples that would teach learners how to answer abstract multiple-choice questions. Participants' examples were best described by a model that selects evidence that maximizes the learner's belief in the correct answer. Consistent with this idea, participants' predictions about how well learners would do closely tracked the performance of an independent sample of learners (N = 140) who were tested on the examples they had provided. In addition, regions that play specialized roles in processing social information, namely the bilateral temporoparietal junction and middle and dorsal medial prefrontal cortex, tracked learners' posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.

Anxiety and depression related abnormalities in socio-affective learning

Affective distress (as observed in anxiety and depression) has been observed to be related to insufficient sensitivity to changing reinforcement during operant learning. Whether such findings are specific to anxiety or depression is unclear given a wider literature relating negative affect to abnormal learning and the possibility that relationships are not consistent across incentive types (i.e., punishment and reward) and outcomes (i.e., positive or negative). In two separate samples (n = 100; n = 88), participants completed an operant learning task with positive or negative, and neutral socio-affective feedback, designed to assess adaptive responses to changing environmental volatility. Individual parameter estimates were generated with hierarchical Bayesian modelling. Effects of manipulations were modelled by decomposing parameters into a linear combination of effects on the logit scale. While effects tended to support prior work, neither general affective distress nor anxiety or depression were consistently related to a decrease in the adaptive adjustment of learning-rates in response to changing environmental volatility (Sample 1: β_{α:volatility} = -0.01, 95 % HDI = -0.14, 0.13; Sample 2: β_{α:volatility} = -0.15, 95 % HDI = -0.37, 0.05). Interaction effects in Sample 1 suggested that while distress was associated with decrements in adaptive learning under punishment-maximisation, it was associated with improvements under reward-maximisation. While our results are broadly consistent with prior work, they suggest that the role of anxiety or depression in volatility learning, if present, is subtle and difficult to detect. Inconsistencies between our samples, along with issues of parameter identifiability complicated interpretation.

Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments

Humans are able to adapt to the fast-changing world by estimating statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the computational and neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation learning task, during which we simultaneously manipulated emotional valence (fearful/neutral expressions of the cue) and environmental volatility (frequent/infrequent reversals of reward probabilities). Across 2 experiments, computational modeling consistently revealed a higher learning rate for the environment with frequent versus infrequent reversals following neutral cues. In contrast, this flexible adjustment was absent in the environment with fearful cues, suggesting a suppressive role of fear in adaptation to environmental volatility. This suppressive effect was underpinned by activity of the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC) as well as increased functional connectivity between the dACC and temporal-parietal junction (TPJ) for fear with environmental volatility. Dynamic causal modeling identified that the driving effect was located in the TPJ and was associated with dACC activation, suggesting that the suppression of fear on adaptive behaviors occurs at the early stage of bottom-up processing. These findings provide a neuro-computational account of how fear interferes with adaptation to volatility during dynamic environments.

Human nutritional intelligence underestimated? Exposing sensitivities to food composition in everyday dietary decisions

The social and cultural significance of food is woven into every aspect of our dietary behaviour, and it contributes to our complex interaction with food. To find order within this complexity scientists often look for dietary 'universals' - phenomena or basic principles that guide our food choice and meal size, irrespective of wider context. One such idea is that taste characteristics provide a signal for dietary composition (e.g., sweet taste signals carbohydrate). Others have suggested that behaviour is guided by learning and is based on associations that form between the flavour of a food and its post-ingestive effects. Despite a large body of research, evidence supporting both processes is equivocal, leading some to conclude that humans are largely indifferent to food composition. Here, we argue that human abilities to gauge the nutritional composition or value of food have been underestimated, and that they can be exposed by embracing alternative methods, including cross-cultural comparisons, large nutrition surveys, and the use of virtual portion-selection tools. Our group has focused on assessments of food choice and expected satiety, and how comparisons across everyday foods can reveal non-linear relationships with food energy density, and even the potential for sensitivity to micronutrient composition. We suggest that these abilities might reflect a complex form of social learning, in which flavour-nutrient associations are not only formed but communicated and amplified across individuals in the form of a cuisine. Thus, rather than disregarding sociocultural influences as extraneous, we might reimagine their role as central to a process that creates and imbues a 'collective dietary wisdom.' In turn, this raises questions about whether rapid dietary, technological, and cultural change disrupts a fundamental process, such that it no longer guarantees a 'nutritional intelligence' that confers benefits for health.

A boost in self-esteem after positive social evaluation predicts social and non-social learning

Fluctuations in self-esteem resulting from social acceptance and rejection could guide social behaviour by putting us in a state that is more or less open to social experiences. However, it remains unclear whether social acceptance and rejection may shape learning from social information depending on individual differences in self-esteem changes. Here we used a social feedback paradigm to manipulate social acceptance and rejection in a between-subjects design. Subsequently, we administered a behavioural task that enables the assessment of how well individuals learn on the basis of own experiences versus social information. Participants receiving positive (N = 43) versus negative (N = 44) social evaluation demonstrated an increase in subjective self-esteem. Importantly, the effect of the social evaluation on social learning was moderated by self-esteem changes. Specifically, an increase in self-esteem, as induced by positive evaluation, was associated with increased learning from social, but decreased learning from individual information. A decrease in self-esteem in response to negative evaluation was associated with decreased learning from individual information. These data suggest that increases in self-esteem in response to positive evaluation can induce a shift in the inclination to use social versus non-social information and may open one up to constructive learning from others.

Anticipating social feedback involves basal forebrain and mesolimbic functional connectivity

The mesolimbic system and basal forebrain (BF) are implicated in processing rewards and punishment, but their interplay and functional properties of subregions with respect to future social outcomes remain unclear. Therefore, this study investigated regional responses and interregional functional connectivity of the lateral (l), medial (m), and ventral (v) Substantia Nigra (SN), Nucleus Accumbens (NAcc), Nucleus basalis of Meynert (NBM), and Medial Septum/Diagonal Band (MS/DB) during reward and punishment anticipation in a social incentive delay task with neutral, positive, and negative feedback using high-resolution fMRI (1.5mm³). Neuroimaging data (n=36 healthy humans) of the anticipation phase was analyzed using mass-univariate, functional connectivity, and multivariate-pattern analysis. As expected, participants responded faster when anticipating positive and negative compared to neutral social feedback. At the neural level, anticipating social information engaged valence-related and valence-unrelated functional connectivity patterns involving the BF and mesolimbic areas. Precisely, valence-related connectivity between the lSN and NBM was associated with anticipating neutral social feedback, while connectivity between the vSN and NBM was associated with anticipating positive social feedback. A more complex pattern was observed for anticipating negative social feedback, including connectivity between the lSN and MS/DB, lSN and NAcc, as well as mSN and NAcc. To conclude, behavioral responses are modulated by the possibility to obtain positive and avoid negative social feedback. The neural processing of feedback anticipation relies on functional connectivity patterns between the BF and mesolimbic areas associated with the emotional valence of the social information. As such, our findings give novel insights into the underlying neural processes of social information processing.

l-DOPA and oxytocin influence the neurocomputational mechanisms of self-benefitting and prosocial reinforcement learning

Humans learn through reinforcement, particularly when outcomes are unexpected. Recent research suggests similar mechanisms drive how we learn to benefit other people, that is, how we learn to be prosocial. Yet the neurochemical mechanisms underlying such prosocial computations remain poorly understood. Here, we investigated whether pharmacological manipulation of oxytocin and dopamine influence the neurocomputational mechanisms underlying self-benefitting and prosocial reinforcement learning. Using a double-blind placebo-controlled cross-over design, we administered intranasal oxytocin (24 IU), dopamine precursor l-DOPA (100 mg + 25 mg carbidopa), or placebo over three sessions. Participants performed a probabilistic reinforcement learning task with potential rewards for themselves, another participant, or no one, during functional magnetic resonance imaging. Computational models of reinforcement learning were used to calculate prediction errors (PEs) and learning rates. Participants behavior was best explained by a model with different learning rates for each recipient, but these were unaffected by either drug. On the neural level, however, both drugs blunted PE signaling in the ventral striatum and led to negative signaling of PEs in the anterior mid-cingulate cortex, dorsolateral prefrontal cortex, inferior parietal gyrus, and precentral gyrus, compared to placebo, and regardless of recipient. Oxytocin (versus placebo) administration was additionally associated with opposing tracking of self-benefitting versus prosocial PEs in dorsal anterior cingulate cortex, insula and superior temporal gyrus. These findings suggest that both l-DOPA and oxytocin induce a context-independent shift from positive towards negative tracking of PEs during learning. Moreover, oxytocin may have opposing effects on PE signaling when learning to benefit oneself versus another.

Home alone: A population neuroscience investigation of brain morphology substrates

As a social species, ready exchange with peers is a pivotal asset - our "social capital". Yet, single-person households have come to pervade metropolitan cities worldwide, with unknown consequences in the long run. Here, we systematically explore the morphological manifestations associated with singular living in ∼40,000 UK Biobank participants. The uncovered population-level signature spotlights the highly associative default mode network, in addition to findings such as in the amygdala central, cortical and corticoamygdaloid nuclei groups, as well as the hippocampal fimbria and dentate gyrus. Both positive effects, equating to greater gray matter volume associated with living alone, and negative effects, which can be interpreted as greater gray matter associations with not living alone, were found across the cortex and subcortical structures Sex-stratified analyses revealed male-specific neural substrates, including somatomotor, saliency and visual systems, while female-specific neural substrates centered on the dorsomedial prefrontal cortex. In line with our demographic profiling results, the discovered neural pattern of living alone is potentially linked to alcohol and tobacco consumption, anxiety, sleep quality as well as daily TV watching. The persistent trend for solitary living will require new answers from public-health decision makers. SIGNIFICANCE STATEMENT: Living alone has profound consequences for mental and physical health. Despite this, there has been a rapid increase in single-person households worldwide, with the long-term consequences yet unknown. In the largest study of its kind, we investigate how the objective lack of everyday social interaction, through living alone, manifests in the brain. Our population neuroscience approach uncovered a gray matter signature that converged on the 'default network', alongside targeted subcortical, sex and demographic profiling analyses. The human urge for social relationships is highlighted by the evolving COVID-19 pandemic. Better understanding of how social isolation relates to the brain will influence health and social policy decision-making of pandemic planning, as well as social interventions in light of global shifts in houseful structures.

How social information impacts action in rodents and humans: the role of the prefrontal cortex and its connections

Day-to-day choices often involve social information and can be influenced by prior social experience. When making a decision in a social context, a subject might need to: 1) recognize the other individual or individuals, 2) infer their intentions and emotions, and 3) weigh the values of all outcomes, social and non-social, prior to selecting an action. These elements of social information processing all rely, to some extent, on the medial prefrontal cortex (mPFC). Patients with neuropsychiatric disorders often have disruptions in prefrontal cortical function, likely contributing to deficits in social reasoning and decision making. To better understand these deficits, researchers have turned to rodents, which have revealed prefrontal cortical mechanisms for contending with the complex information processing demands inherent to making decisions in social contexts. Here, we first review literature regarding social decision making, and the information processing underlying it, in humans and patient populations. We then turn to research in rodents, discussing current procedures for studying social decision making, and underlying neural correlates.

Neural representations of vicarious rewards are linked to interoception and prosocial behaviour

Every day we constantly observe other people receiving rewards. Theoretical accounts posit that vicarious reward processing might be linked to people's sensitivity to internal body states (interoception) and facilitates a tendency to act prosocially. However, the neural processes underlying the links between vicarious reward processing, interoception, and prosocial behaviour are poorly understood. Previous research has linked vicarious reward processing to the anterior cingulate gyrus (ACCg) and the anterior insula (AI). Can we predict someone's propensity to be prosocial or to be aware of interoceptive signals from variability in how the ACCg and AI process rewards? Here, participants monitored rewards being delivered to themselves or a stranger during functional magnetic resonance imaging. Later, they performed a task measuring their willingness to exert effort to obtain rewards for others, and a task measuring their propensity to be aware and use interoceptive respiratory signals. Using multivariate similarity analysis, we show that people's willingness to be prosocial is predicted by greater similarity between self and other representations in the ACCg. Moreover, greater dissimilarity in self-other representations in the AI is linked to interoceptive propensity. These findings highlight that vicarious reward is linked to bodily signals in AI, and foster prosocial tendencies through the ACCg.

An fMRI-study of leading and following using rhythmic tapping

Leading and following is about synchronizing and joining actions in accordance with the differences that the leader and follower roles provide. The neural reactivity representing these roles was measured in an explorative fMRI-study, where two persons lead and followed each other in finger tapping using simple, individual, pre-learnt rhythms. All participants acted both as leader and follower. Neural reactivity for both lead and follow related to social awareness and adaptation distributed over the lateral STG, STS and TPJ. Reactivity for follow contrasted with lead mostly reflected sensorimotor and rhythmic processing in cerebellum IV, V, somatosensory cortex and SMA. During leading, as opposed to following, neural reactivity was observed in the insula and bilaterally in the superior temporal gyrus, pointing toward empathy, sharing of feelings, temporal coding and social engagement. Areas for continuous adaptation, in the posterior cerebellum and Rolandic operculum, were activated during both leading and following. This study indicated mutual adaptation of leader and follower during tapping and that the roles gave rise to largely similar neuronal reactivity. The differences between the roles indicated that leading was more socially focused and following had more motoric- and temporally related neural reactivity.

Stimulating human prefrontal cortex increases reward learning

Work in computational psychiatry suggests that mood disorders may stem from aberrant reinforcement learning processes. Specifically, it has been proposed that depressed individuals believe that negative events are more informative than positive events, resulting in higher learning rates from negative outcomes (Pulcu and Browning, 2019). In this proof-of-concept study, we investigated whether transcranial direct current stimulation (tDCS) applied to dorsolateral prefrontal cortex, as commonly used in depression treatment trials, might change learning rates for affective outcomes. Healthy adults completed an established reinforcement learning task (Pulcu and Browning, 2017) in which the information content of reward and loss outcomes was manipulated by varying the volatility of stimulus-outcome associations. Learning rates on the tasks were quantified using computational models. Stimulation over dorsolateral prefrontal cortex (DLPFC) but not motor cortex (M1) increased learning rates specifically for reward outcomes. The effects of prefrontal tDCS were cognitive state-dependent: tDCS applied during task performance increased learning rates for wins; tDCS applied before task performance decreased both win and loss learning rates. A replication study confirmed the key finding that tDCS to DLPFC during task performance increased learning rates specifically for rewards. Taken together, these findings demonstrate the potential of tDCS for modulating computational parameters of reinforcement learning that are relevant to mood disorders.