A behavioral signature for quantifying the social value of interpersonal relationships with specific others

The idea that individuals ascribe value to social phenomena, broadly construed, is well-established. Despite the ubiquity of this concept, defining social value in the context of interpersonal relationships remains elusive. This is notable because while prominent theories of human social behavior acknowledge the role of value-based processes, they mostly emphasize the value of individual actions an agent may choose to take in a given environment. Comparatively little is known about how humans value their interpersonal relationships. To address this, we devised a method for engineering a behavioral signature of social value in several independent samples (total N = 1111). Incorporating the concept of opportunity cost from economics and data-driven quantitative methods, we derived this signature by sourcing and weighting a range of social behaviors based on how likely individuals are to prioritize them in the face of limited resources. We examined how strongly the signature was expressed in self-reported social behaviors with specific relationship partners (a parent, close friend, and acquaintance). Social value scores track with other aspects of these relationships (e.g., relationship quality, aversion to losing relationship partners), are predictive of decision preferences on a range of tasks, and display good psychometric properties. These results provide greater mechanistic specificity in delineating human value-based behavior in social contexts and help parse the motivational relevance of the different facets that comprise interpersonal relationships.

Reinforcement learning of altruistic punishment differs between cultures and across the lifespan

Altruistic punishment is key to establishing cooperation and maintaining social order, yet its developmental trends across cultures remain unclear. Using computational reinforcement learning models, we provided the first evidence of how social feedback dynamically influences group-biased altruistic punishment across cultures and the lifespan. Study 1 (n = 371) found that Chinese participants exhibited higher learning rates than Americans when socially incentivized to punish unfair allocations. Additionally, Chinese adults showed slower learning and less exploration when punishing ingroups than outgroups, a pattern absent in American counterparts, potentially reflecting a tendency towards ingroup favoritism that may contribute to reinforcing collectivist values. Study 2 (n = 430, aged 12-52) further showed that such ingroup favoritism develops with age. Chinese participants' learning rates for ingroup punishment decreased from adolescence into adulthood, while outgroup rates stayed constant, implying a process of cultural learning. Our findings highlight cultural and age-related variations in altruistic punishment learning, with implications for social reinforcement learning and culturally sensitive educational practices promoting fairness and altruism.

Dynamic mutual predictions during social learning: A computational and interbrain model

During social interactions, we constantly learn about the thoughts, feelings, and personality traits of our interaction partners. Learning in social interactions is critical for bond formation and acquiring knowledge. Importantly, this type of learning is typically bi-directional, as both partners learn about each other simultaneously. Here we review the literature on social learning and propose a new computational and neural model characterizing mutual predictions that take place within and between interactions. According to our model, each partner in the interaction attempts to minimize the prediction error of the self and the interaction partner. In most cases, these inferential models become similar over time, thus enabling mutual understanding to develop. At the neural level, this type of social learning may be supported by interbrain plasticity, defined as a change in interbrain coupling over time in neural networks associated with social learning, among them the mentalizing network, the observation-execution system, and the hippocampus. The mutual prediction model constitutes a promising means of providing empirically verifiable accounts of how relationships develop over time.

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.

A stenography of empathy: Toward a consensual model of the empathic process

Empathy has gained popularity in the general population and the scientific world during the past decade. Recently, several researchers found a significant decrease in empathy scores of healthcare students (notably medical students) and recommend promoting empathy skills in several fields of education. The current paper presents a new model of the empathic process: a stenography of empathy compelling scientific data and contemporary conceptions. Indeed, we combined all pioneer researchers' conceptions of empathy (Davis, Decety, Batson, Preston & de Waal) into an integrative model. This model is centered on the empathizer (i.e., a person observing a target experiencing emotions) and displays how all empathy components are articulated, explaining the individuals' general functioning and how the process might become dysfunctional. We illustrated applications of the model with three clinical examples (i.e., burnout, psychopathy, and borderline personality disorders) to display how empathy is related to psychopathological symptoms. We believe this new dynamic and sequential model would be helpful in explaining how empathy works, which is of great interest to healthcare students, clinicians, researchers, and academics.

The power of the unexpected: Prediction errors enhance stereotype-based learning

Stereotyping is a ubiquitous feature of social cognition, yet surprisingly little is known about how group-related beliefs influence the acquisition of person knowledge. Accordingly, in combination with computational modeling (i.e., Reinforcement Learning Drift Diffusion Model analysis), here we used a probabilistic selection task to explore the extent to which gender stereotypes impact instrumental learning. Several theoretically interesting effects were observed. First, reflecting the impact of cultural socialization on person construal, an expectancy-based preference for stereotype-consistent (vs. stereotype-inconsistent) responses was observed. Second, underscoring the potency of unexpected information, learning rates were faster for counter-stereotypic compared to stereotypic individuals, both for negative and positive prediction errors. Collectively, these findings are consistent with predictive accounts of social perception and have implications for the conditions under which stereotyping can potentially be reduced.

Incorporating social knowledge structures into computational models

To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture the complexities of social learning processes are currently lacking. In this study, we specify and test potential strategies that humans can employ for learning about others. Standard Rescorla-Wagner (RW) learning models only capture parts of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalize two social knowledge structures and implement them in hybrid RW models to test their usefulness across multiple social learning tasks. We name these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results from model comparisons and statistical analyses indicate that participants efficiently combine the concepts of granularity and reference points-with the specific combinations in models depending on the people and traits that participants learned about. Overall, our experiments demonstrate that variants of RW algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T

The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversal-learning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With high-resolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

Social learning across adolescence: A Bayesian neurocognitive perspective

Adolescence is a period of social re-orientation in which we are generally more prone to peer influence and the updating of our beliefs based on social information, also called social learning, than in any other stage of our life. However, how do we know when to use social information and whose information to use and how does this ability develop across adolescence? Here, we review the social learning literature from a behavioral, neural and computational viewpoint, focusing on the development of brain systems related to executive functioning, value-based decision-making and social cognition. We put forward a Bayesian reinforcement learning framework that incorporates social learning about value associated with particular behavior and uncertainty in our environment and experiences. We discuss how this framework can inform us about developmental changes in social learning, including how the assessment of uncertainty and the ability to adaptively discriminate between information from different social sources change across adolescence. By combining reward-based decision-making in the domains of both informational and normative influence, this framework explains both negative and positive social peer influence in adolescence.

Meta-analysis of human prediction error for incentives, perception, cognition, and action

Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species and has inspired the construction of artificial intelligence that can outperform humans. However, there is still much to learn. Here, we leverage the wealth of human PE data acquired in the functional neuroimaging setting in service of a deeper understanding, using an MKDA (multi-level kernel-based density) meta-analysis. Studies were identified with Google Scholar, and we included studies with healthy adult participants that reported activation coordinates corresponding to PEs published between 1999-2018. Across 264 PE studies that have focused on reward, punishment, action, cognition, and perception, consistent with domain-general theoretical models of prediction error we found midbrain PE signals during cognitive and reward learning tasks, and an insula PE signal for perceptual, social, cognitive, and reward prediction errors. There was evidence for domain-specific error signals--in the visual hierarchy during visual perception, and the dorsomedial prefrontal cortex during social inference. We assessed bias following prior neuroimaging meta-analyses and used family-wise error correction for multiple comparisons. This organization of computation by region will be invaluable in building and testing mechanistic models of cognitive function and dysfunction in machines, humans, and other animals. Limitations include small sample sizes and ROI masking in some included studies, which we addressed by weighting each study by sample size, and directly comparing whole brain vs. ROI-based results.

The computational challenge of social learning

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.

Information about others' choices selectively alters risk tolerance and medial prefrontal cortex activation across adolescence and young adulthood

Adolescence is associated with major changes in the cognitive, emotional and social domains. One domain in which these processes intersect is decision-making. Previous research has shown that individuals' attitudes towards risk and ambiguity shape their decision-making, and information about others' choices can influence individuals' decisions. However, it is currently unknown how information about others' choices influences risk and ambiguity attitudes separately, and the degree to which others' choices shape decision-making differentially across development from adolescence to young adulthood. The current study used a computational modeling framework to test how information about others' choices influences these attitudes. Participants, aged 14-22 years, made a series of risky and ambiguous choices while undergoing fMRI scanning. On some trials, they viewed risky or safe choices of others. Results showed that participants aligned their choices toward the choice preferences of others. Moreover, the tendency to align choices was expressed in changes in risk attitude, but not ambiguity attitude. The change in risk attitude was positively related to neural activation in the medial prefrontal cortex. Results did not show age related differences in behavior and corresponding neural activation, indicating that the manner in which adolescents are influenced by peers is not ubiquitous but rather, is highly context-dependent.

Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases

Social feedback can selectively enhance learning in diverse domains. Relevant neurocognitive mechanisms have been studied mainly in healthy persons, yielding correlational findings. Neurodegenerative lesion models, coupled with multimodal brain measures, can complement standard approaches by revealing direct multidimensional correlates of the phenomenon.

To this end, we assessed socially reinforced and non-socially reinforced learning in 40 healthy participants as well as persons with behavioural variant frontotemporal dementia (n = 21), Parkinson’s disease (n = 31) and Alzheimer’s disease (n = 20). These conditions are typified by predominant deficits in social cognition, feedback-based learning and associative learning, respectively, although all three domains may be partly compromised in the other conditions. We combined a validated behavioural task with ongoing EEG signatures of implicit learning (medial frontal negativity) and offline MRI measures (voxel-based morphometry).

In healthy participants, learning was facilitated by social feedback relative to non-social feedback. In comparison with controls, this effect was specifically impaired in behavioural variant frontotemporal dementia and Parkinson’s disease, while unspecific learning deficits (across social and non-social conditions) were observed in Alzheimer’s disease. EEG results showed increased medial frontal negativity in healthy controls during social feedback and learning. Such a modulation was selectively disrupted in behavioural variant frontotemporal dementia. Neuroanatomical results revealed extended temporo-parietal and fronto-limbic correlates of socially reinforced learning, with specific temporo-parietal associations in behavioural variant frontotemporal dementia and predominantly fronto-limbic regions in Alzheimer’s disease. In contrast, non-socially reinforced learning was consistently linked to medial temporal/hippocampal regions. No associations with cortical volume were found in Parkinson’s disease. Results are consistent with core social deficits in behavioural variant frontotemporal dementia, subtle disruptions in ongoing feedback-mechanisms and social processes in Parkinson’s disease and generalized learning alterations in Alzheimer’s disease. This multimodal approach highlights the impact of different neurodegenerative profiles on learning and social feedback.

Our findings inform a promising theoretical and clinical agenda in the fields of social learning, socially reinforced learning and neurodegeneration.

Computational modelling of social cognition and behaviour-a reinforcement learning primer

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning

Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing complex activation via univariate analysis. Therefore, we employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning. Twenty‐four healthy subjects participated in 2 × 2 matrix‐based sequential‐move games. Searchlight‐based multivoxel patterns were used as input for a support vector machine using nested cross‐validation to distinguish game conditions, and identified voxels were validated via the regression of the behavioral data with bootstrapping. The left anterior insula (accuracy = 78.5%) was associated with competition, and middle frontal gyrus (75.1%) was associated with predictive reasoning. The inferior/superior parietal lobules (84.8%) and middle frontal gyrus (84.7%) were associated with competition, particularly in trials with a predictive opponent. The visual/motor areas were related to response time as a proxy for visual attention and task difficulty. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.

The role of right temporoparietal junction in processing social prediction error across relationship contexts

How do people update their impressions of close others? Although people may be motivated to maintain their positive impressions, they may also update their impressions when their expectations are violated (i.e. prediction error). Combining neuroimaging and computational modeling, we test the hypothesis that brain regions associated with theory of mind, especially right temporoparietal junction (rTPJ), underpin both motivated impression maintenance and impression updating evoked by prediction error. Participants had money either given to or taken away from them by a friend or a stranger and were then asked to rate each partner on trustworthiness and closeness across trials. Overall, participants engaged in less impression updating for friends vs strangers. Decreased rTPJ activity in response to a friend’s negative behavior (taking money) was associated with reduced negative updating and increased positive ratings of the friend. However, to the extent that participants did update their impressions (more negative ratings) of friends, this behavioral pattern was explained by greater prediction error and greater rTPJ activity. These findings suggest that rTPJ recruitment represents the integration of prediction error signals and the capacity to overcome people’s motivation to maintain positive impressions of friends in the face of conflicting evidence.

The neural basis of shared preference learning

During our daily lives, we often learn about the similarity of the traits and preferences of others to our own and use that information during our social interactions. However, it is unclear how the brain represents similarity between the self and others. One possible mechanism is to track similarity to oneself regardless of the identity of the other (Similarity account); an alternative is to track each other person in terms of consistency of their choice similarity with respect to the choices they have made before (consistency account). Our study combined functional Magnetic Resonance Imaging (fMRI) and computational modelling of reinforcement learning (RL) to investigate the neural processes that underlie learning about preference similarity. Participants chose which of two pieces of artwork they preferred and saw the choices of one agent who usually shared their preference and another agent who usually did not. We modelled neural activation with RL models based on the similarity and consistency accounts. Our results showed that activity in brain areas linked to reward and social cognition followed the consistency account. Our findings suggest that impressions of other people can be calculated in a person-specific manner, which assumes that each individual behaves consistently with their past choices.

Smiling as negative feedback affects social decision-making and its neural underpinnings

A crucial aspect of social decision-making is the ability to learn from the outcomes of preceding decisions. In particular, learning might be influenced by the expectedness of feedback and its valence. Expectedness has largely been operationalized as the frequency of stimulus occurrence and not in terms of its social context. Therefore, we investigated the influence of socially unexpected feedback, i.e., smiling upon adverse events, on behavioral and neural responses. We used a modified version of the ultimatum game, a commonly used paradigm for economic decision-making, by implementing different proposer identities with a distinct reaction pattern towards accepted and rejected monetary offers. We could show that an identity, who reacted with a smile towards rejected offers, evoked lower acceptance rates compared to identities, who reward acceptance with a smile. Electrophysiological correlates indicate N170 effects for emotional identities compared to a neutral control identity. Regarding FRN and P3 brain potentials, we detected a particular function of the smiling face when used as a socially unexpected, negative feedback stimulus. Hence, individuals seek an unexpected smile despite the associated monetary loss, which is accompanied by distinct neural patterns.

Model-Based and Model-Free Social Cognition: Investigating the Role of Habit in Social Attitude Formation and Choice

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions - computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each type of learning was expressed in both advisor choices and post-task self-reported liking of advisors. Specifically, participants preferred advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Although participants relied more heavily on model-based learning overall, they varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

Neural mechanisms of face emotion processing in youths and adults with bipolar disorder

OBJECTIVES

Little is known about potential differences in the pathophysiology of bipolar disorder (BD) across development. The present study aimed to characterize age-related neural mechanisms of BD.

METHODS

Youths and adults with and without BD (N = 108, age range = 9.8-55.9 years) completed an emotional face labeling task during fMRI acquisition. We leveraged three different fMRI analytic tools to identify age-related neural mechanisms of BD, investigating (a) change in neural responses over the course of the task, (b) neural activation averaged across the entire task, and (c) amygdala functional connectivity.

RESULTS

We found converging Age Group × Diagnosis patterns across all three analytic methods. Compared to healthy youths vs adults, youths vs adults with BD show an altered pattern in response to repeated presentation of emotional faces in medial prefrontal, amygdala, and temporoparietal regions, as well as amygdala-temporoparietal connectivity. Specifically, medial prefrontal and lingual activation decreases over the course of repeated emotional face presentations in healthy youths vs adults but increases in youths with BD compared to adults with BD. Moreover, youths vs adults with BD show less medial prefrontal activation and amygdala-temporoparietal junction connectivity averaged over the task, but this difference is not found for healthy youths vs adults.

CONCLUSION

Although longitudinal confirmation and replication will be necessary, these findings suggest that neural development may be aberrant in BD and that some neural mechanisms mediating BD may differ in adults vs children with the illness.

Incidental ostracism emerges from simple learning mechanisms

Ostracism, or social exclusion, is widespread and associated with a range of detrimental psychological and social outcomes. Ostracism is typically explained as instrumental punishment of free-riders or deviants. However, this instrumental account fails to explain many of the features of real-world ostracism, including its prevalence. Here we hypothesized that ostracism can emerge incidentally (non-instrumentally) when people choose partners in social interactions, and that this process is driven by simple learning mechanisms. We tested this hypothesis in four experiments (n = 456) with economic games in dynamic social networks. Contrary to the instrumental account of ostracism, we find that the targets of ostracism are not primarily free-riders. Instead, incidental initial variability in choosing partners for social interactions predicts later ostracism better than the instrumental account. Using computational modelling, we show that simple reinforcement learning mechanisms explain the incidental emergence of ostracism, and that they do so better than a formalization of the instrumental account. Finally, we leveraged these reinforcement learning mechanisms to experimentally reduce incidental ostracism. Our results demonstrate that ostracism is more incidental than previously assumed and can arise from basic forms of learning. They also show that the same mechanisms that result in incidental ostracism can help to reduce its emergence.

Computational neuroscience approaches to social cognition

How do we form impressions of people and groups and use these representations to guide our actions? From its inception, social neuroscience has sought to illuminate such complex forms of social cognition, and recently these efforts have been invigorated by the use of computational modeling. Computational modeling provides a framework for delineating specific processes underlying social cognition and relating them to neural activity and behavior. We provide a primer on the computational modeling approach and describe how it has been used to elucidate psychological and neural mechanisms of impression formation, social learning, moral decision making, and intergroup bias.

Motivation alters impression formation and related neural systems

Observers frequently form impressions of other people based on complex or conflicting information. Rather than being objective, these impressions are often biased by observers’ motives. For instance, observers often downplay negative information they learn about ingroup members. Here, we characterize the neural systems associated with biased impression formation. Participants learned positive and negative information about ingroup and outgroup social targets. Following this information, participants worsened their impressions of outgroup, but not ingroup, targets. This tendency was associated with a failure to engage neural structures including lateral prefrontal cortex, dorsal anterior cingulate cortex, temporoparietal junction, Insula and Precuneus when processing negative information about ingroup (but not outgroup) targets. To the extent that participants engaged these regions while learning negative information about ingroup members, they exhibited less ingroup bias in their impressions. These data are consistent with a model of ‘effortless bias’, under which perceivers fail to process goal-inconsistent information in order to maintain desired conclusions.

Brain and Social Networks: Fundamental Building Blocks of Human Experience

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models.

Moral learning: Psychological and philosophical perspectives

The past 15years occasioned an extraordinary blossoming of research into the cognitive and affective mechanisms that support moral judgment and behavior. This growth in our understanding of moral mechanisms overshadowed a crucial and complementary question, however: How are they learned? As this special issue of the journal Cognition attests, a new crop of research into moral learning has now firmly taken root. This new literature draws on recent advances in formal methods developed in other domains, such as Bayesian inference, reinforcement learning and other machine learning techniques. Meanwhile, it also demonstrates how learning and deciding in a social domain-and especially in the moral domain-sometimes involves specialized cognitive systems. We review the contributions to this special issue and situate them within the broader contemporary literature. Our review focuses on how we learn moral values and moral rules, how we learn about personal moral character and relationships, and the philosophical implications of these emerging models.

Endogenous opioids regulate social threat learning in humans

Many fearful expectations are shaped by observation of aversive outcomes to others. Yet, the neurochemistry regulating social learning is unknown. Previous research has shown that during direct (Pavlovian) threat learning, information about personally experienced outcomes is regulated by the release of endogenous opioids, and activity within the amygdala and periaqueductal gray (PAG). Here we report that blockade of this opioidergic circuit enhances social threat learning through observation in humans involving activity within the amygdala, midline thalamus and the PAG. In particular, anticipatory responses to learned threat cues (CS) were associated with temporal dynamics in the PAG, coding the observed aversive outcomes to other (observational US). In addition, pharmacological challenge of the opioid receptor function is classified by distinct brain activity patterns during the expression of conditioned threats. Our results reveal an opioidergic circuit that codes the observed aversive outcomes to others into threat responses and long-term memory in the observer.

Though humans often learn about negative outcomes from observing the response of others, the neurochemistry underlying this learning is unknown. Here, authors show that blocking opioid receptors enhances social threat learning and describe the brain regions underlying this effect.

Social anxiety is characterized by biased learning about performance and the self

People learn about their self from social information, and recent work suggests that healthy adults show a positive bias for learning self-related information. In contrast, social anxiety disorder (SAD) is characterized by a negative view of the self, yet what causes and maintains this negative self-view is not well understood. Here the authors use a novel experimental paradigm and computational model to test the hypothesis that biased social learning regarding self-evaluation and self-feelings represents a core feature that distinguishes adults with SAD from healthy controls. Twenty-one adults with SAD and 35 healthy controls (HCs) performed a speech in front of 3 judges. They subsequently evaluated themselves and received performance feedback from the judges and then rated how they felt about themselves and the judges. Affective updating (i.e., change in feelings about the self over time, in response to feedback from the judges) was modeled using an adapted Rescorla-Wagner learning model. HCs demonstrated a positivity bias in affective updating, which was absent in SAD. Further, self-performance ratings revealed group differences in learning from positive feedback-a difference that endured at an average of 1 year follow up. These findings demonstrate the presence and long-term endurance of positively biased social learning about the self among healthy adults, a bias that is absent or reversed among socially anxious adults. (PsycINFO Database Record

Anterior Temporal Lobe Tracks the Formation of Prejudice

Despite advances in understanding the brain structures involved in the expression of stereotypes and prejudice, little is known about the brain structures involved in their acquisition. Here, we combined fMRI, a task involving learning the valence of different social groups, and modeling of the learning process involved in the development of biases in thinking about social groups that support prejudice. Participants read descriptions of valenced behaviors performed by members of novel social groups, with majority groups being more frequently encountered during learning than minority groups. A model-based fMRI analysis revealed that the anterior temporal lobe tracked the trial-by-trial changes in the valence associated with each group encountered in the task. Descriptions of behavior by group members that deviated from the group average (i.e., prediction errors) were associated with activity in the left lateral PFC, dorsomedial PFC, and lateral anterior temporal cortex. Minority social groups were associated with slower acquisition rates and more activity in the ventral striatum and ACC/dorsomedial PFC compared with majority groups. These findings provide new insights into the brain regions that (a) support the acquisition of prejudice and (b) detect situations in which an individual's behavior deviates from the prejudicial attitude held toward their group.