The oxytocinergic system and racial ingroup bias in empathic neural activity

Studies have indicated that the human brain exhibits a more robust neural empathic response towards individuals of the same racial ingroup than those of the outgroup. However, the impact of the oxytocinergic system on the dynamic connectivity between brain regions involved in racial ingroup bias in empathy (RIBE) and its implications for real-life social interaction intention remains unclear. To address this gap, we employed functional magnetic resonance imaging (fMRI) to investigate RIBE-modulated neural activities and the influence of the oxytocinergic system at both neural and behavioral levels. Participants homozygous for the A/A and G/G genotypes of the oxytocin receptor gene (OXTR) rs53576 polymorphism underwent scanning while making judgments about painful versus non-painful stimuli in same-race versus other-race scenarios following either oxytocin (OT) or placebo treatment. The results revealed greater activity in the anterior cingulate cortex (ACC) and anterior insula (AI) in response to same-race compared to other-race models in the G/G group but not in the A/A group. RIBE also modulated the connections between bilateral AI and the ACC, and the effect of OT on this modulatory effect was moderated by genotype rs53576 and interpersonal trust. Moreover, more extensive changes in AI-ACC connections were associated with higher levels of revenge intention in the low interpersonal trust group. Overall, our findings suggest a pivotal role of the oxytocinergic system in the RIBE-modulated neural activities and revenge intention in human interactions with the modulatory effect of interpersonal trust.

Neural mechanisms of altruistic decision-making: EEG functional connectivity network analysis

Altruism is an enigmatic form of prosocial behavior, characterized by diverse motivations and significant interindividual differences. Studying neural mechanisms of altruism is crucial to identify objective markers of pro- and antisocial tendencies in behavior. This study was designed to delve into the mechanisms of altruism by analyzing EEG-based functional connectivity patterns within the framework of the network approach. To experimentally induce a situation of altruistic decision-making, we employed the Pain versus Gain (PvsG) task, which implies making choices concerning financial self-benefit and pain of the other. Our results reveal that the behavioral measure of altruism in the experiment correlated with emotional empathy, which is in line with the "empathy-altruism" hypothesis. Applying the network approach to EEG functional connectivity analysis, we discovered that the very process of decision-making in the PvsG is characterized by the synchronous activity of structures in the right hemisphere, which are involved in empathy for pain. The prosociality of decisions was reflected in functional connectivity between the rostral ACC and orbital IFG in the left hemisphere and the overall network centrality of the caudal ACC. This finding additionally points to the distinct functional roles of the ACC subregions in altruistic decision-making. The proposed neural mechanisms of altruism can further be used to identify neurophysiological markers of prosociality in behavior.

MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data

Dynamic effective connectivity (DEC) is the accumulation of effective connectivity in the time dimension, which can describe the continuous neural activities in the brain. Recently, learning DEC from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data has attracted the attention of neuroinformatics researchers. However, the current methods fail to consider the gap between the fMRI and EEG modality, which can not precisely learn the DEC network from multimodal data. In this paper, we propose a multimodal causal adversarial network for DEC learning, named MCAN. The MCAN contains two modules: multimodal causal generator and multimodal causal discriminator. First, MCAN employs a multimodal causal generator with an attention-guided layer to produce a posterior signal and output a set of DEC networks. Then, the proposed method uses a multimodal causal discriminator to unsupervised calculate the joint gradient, which directs the update of the whole network. The experimental results on simulated data sets show that MCAN is superior to other state-of-the-art methods in learning the network structure of DEC and can effectively estimate the brain states. The experimental results on real data sets show that MCAN can better reveal abnormal patterns of brain activity and has good application potential in brain network analysis.

Empathy incites a stable prosocial decision bias

Empathy toward suffering individuals serves as potent driver for prosocial behavior. However, it remains unclear whether prosociality induced by empathy for another person's pain persists once that person's suffering diminishes. To test this, participants underwent functional magnetic resonance imaging while performing a binary social decision task that involved allocation of points to themselves and another person. In block one, participants completed the task after witnessing frequent painful stimulation of the other person, and in block two, after observing low frequency of painful stimulation. Drift-diffusion modeling revealed an increased initial bias toward making prosocial decisions in the first block compared with baseline that persisted in the second block. These results were replicated in an independent behavioral study. An additional control study showed that this effect may be specific to empathy as stability was not evident when prosocial decisions were driven by a social norm such as reciprocity. Increased neural activation in dorsomedial prefrontal cortex was linked to empathic concern after witnessing frequent pain and to a general prosocial decision bias after witnessing rare pain. Altogether, our findings show that empathy for pain elicits a stable inclination toward making prosocial decisions even as their suffering diminishes.

Intracranial EEG signals disentangle multi-areal neural dynamics of vicarious pain perception

Empathy enables understanding and sharing of others' feelings. Human neuroimaging studies have identified critical brain regions supporting empathy for pain, including the anterior insula (AI), anterior cingulate (ACC), amygdala, and inferior frontal gyrus (IFG). However, to date, the precise spatio-temporal profiles of empathic neural responses and inter-regional communications remain elusive. Here, using intracranial electroencephalography, we investigated electrophysiological signatures of vicarious pain perception. Others' pain perception induced early increases in high-gamma activity in IFG, beta power increases in ACC, but decreased beta power in AI and amygdala. Vicarious pain perception also altered the beta-band-coordinated coupling between ACC, AI, and amygdala, as well as increased modulation of IFG high-gamma amplitudes by beta phases of amygdala/AI/ACC. We identified a necessary combination of neural features for decoding vicarious pain perception. These spatio-temporally specific regional activities and inter-regional interactions within the empathy network suggest a neurodynamic model of human pain empathy.

Mindfulness-based intervention reduce interference of negative stimuli to working memory in individuals with subclinical depression: A randomized controlled fMRI study

Background

Individuals with subclinical depression are prone to major depression and experience emotional responses and attentional biases to negative stimuli.

Method

In a randomized controlled study (N = 42) using functional magnetic resonance imaging (fMRI), we examined the neurocognitive mechanisms behind mindfulness-based cognitive therapy (MBCT) combining loving-kindness meditation (LKM) on a group with subclinical depression compared with the relaxation group across emotional face n-back (EFNBACK) tasks and resting state. We also collected behavioral and self-reported data to confirm neurocognitive results.

Results

During EFNBACK, the MBCT+LKM group showed greater activation in the left lingual gyrus and right inferior lateral occipital cortex. During rest, the MBCT+LKM group demonstrated increased connectivity of the anterior cingulate cortex and right inferior lateral occipital cortex, right anterior insula and left precentral gyrus. From amplitude of low frequency fluctuation (ALFF) data, activity in brain regions associated with cognitive control decreased and activity in brain regions associated with sensorimotor increased.

Conclusion

These results suggest that MBCT+LKM alleviate depression for subclinical individuals through improving executive function when they face negative stimuli.

The social transmission of empathy relies on observational reinforcement learning

Theories of moral development propose that empathy is transmitted across individuals. However, the mechanisms through which empathy is socially transmitted remain unclear. Here, we combine computational learning models and functional MRI to investigate whether, and if so, how empathic and non-empathic responses observed in others affect the empathy of female observers. The results of three independent studies showed that watching empathic or non-empathic responses generates a learning signal that respectively increases or decreases empathy ratings of the observer. A fourth study revealed that the learning-related transmission of empathy is stronger when observing human rather than computer demonstrators. Finally, we show that the social transmission of empathy alters empathy-related responses in the anterior insula, i.e., the same region that correlated with empathy baseline ratings, as well as its functional connectivity with the temporoparietal junction. Together, our findings provide a computational and neural mechanism for the social transmission of empathy that accounts for changes in individual empathic responses in empathic and non-empathic social environments.

The psychological, computational, and neural foundations of indebtedness

Receiving a favor from another person may induce a negative feeling of indebtedness for the beneficiary. In this study, we explore these hidden costs by developing and validating a conceptual model of indebtedness across three studies that combine a large-scale online questionnaire, an interpersonal game, computational modeling, and neuroimaging. Our model captures how individuals perceive the altruistic and strategic intentions of the benefactor. These inferences produce distinct feelings of guilt and obligation that together comprise indebtedness and motivate reciprocity. Perceived altruistic intentions convey care and communal concern and are associated with activity in insula, ventromedial prefrontal cortex and dorsolateral prefrontal cortex, while inferred strategic intentions convey expectations of future reciprocity and are associated with activation in temporal parietal junction and dorsomedial prefrontal cortex. We further develop a neural utility model of indebtedness using multivariate patterns of brain activity that captures the tradeoff between these feelings and reliably predicts reciprocity behavior.

Decoding Depression Severity From Intracranial Neural Activity

BACKGROUND

Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis.

METHODS

We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings.

RESULTS

Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression.

CONCLUSIONS

The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.

Can monetary incentives overturn fairness-based decisions?

Fairness norms and resulting behaviours are an important prerequisite for cooperation in human societies. At the same time, financial incentives are commonly used to motivate social behaviours, yet it remains unclear how financial incentives affect fairness-based behaviours. Combining a decision paradigm from behavioural economics with hierarchical drift-diffusion modelling, we investigated the effect of different financial incentives on two types of fairness-based decisions in four experimental groups. In two groups, participants divided points between themselves and a disadvantaged person, inciting fairness-based compensation behaviour, in two other groups they divided points between themselves and a fairness violator, inciting fairness-based punishment behaviour. In addition, each group received financial incentives that were either aligned or in conflict with the respective fairness-based behaviour. This design allowed us to directly investigate how different incentives shape the cognitive mechanism of fairness-based decisions and whether these effects are comparable across different fairness domains (fairness-based punishment versus fairness-based compensation). Results showed that offering conflicting incentives diminished fairness-congruent decision behaviour and rendered the fairness-congruent decision process less efficient. These findings demonstrate that financial incentives can undermine fairness-based behaviour, and thus are relevant for the development of incentive schemes aimed at fostering cooperative behaviour.

A Survey on Brain Effective Connectivity Network Learning

Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.

Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict

Learning to predict action outcomes in morally conflicting situations is essential for social decision-making but poorly understood. Here we tested which forms of Reinforcement Learning Theory capture how participants learn to choose between self-money and other-shocks, and how they adapt to changes in contingencies. We find choices were better described by a reinforcement learning model based on the current value of separately expected outcomes than by one based on the combined historical values of past outcomes. Participants track expected values of self-money and other-shocks separately, with the substantial individual difference in preference reflected in a valuation parameter balancing their relative weight. This valuation parameter also predicted choices in an independent costly helping task. The expectations of self-money and other-shocks were biased toward the favored outcome but fMRI revealed this bias to be reflected in the ventromedial prefrontal cortex while the pain-observation network represented pain prediction errors independently of individual preferences.

A unified neural account of contextual and individual differences in altruism

Altruism is critical for cooperation and productivity in human societies but is known to vary strongly across contexts and individuals. The origin of these differences is largely unknown, but may in principle reflect variations in different neurocognitive processes that temporally unfold during altruistic decision making (ranging from initial perceptual processing via value computations to final integrative choice mechanisms). Here, we elucidate the neural origins of individual and contextual differences in altruism by examining altruistic choices in different inequality contexts with computational modeling and electroencephalography (EEG). Our results show that across all contexts and individuals, wealth distribution choices recruit a similar late decision process evident in model-predicted evidence accumulation signals over parietal regions. Contextual and individual differences in behavior related instead to initial processing of stimulus-locked inequality-related value information in centroparietal and centrofrontal sensors, as well as to gamma-band synchronization of these value-related signals with parietal response-locked evidence-accumulation signals. Our findings suggest separable biological bases for individual and contextual differences in altruism that relate to differences in the initial processing of choice-relevant information.

Valence framing induces cognitive bias

Valence framing effects refer to inconsistent choice preferences in response to positive versus negative formulation of mathematically equivalent outcomes. Here, we manipulate valence framing in a two-alternative forced choice dictator game using gains and losses as frames to investigate the cognitive mechanisms underlying valence framing. We applied a Drift-Diffusion Model (DDM) to examine whether gain (i.e., “take” money) and loss (i.e., “give” money) frames evoke a cognitive bias as previous research did not consistently reveal framing effects using reaction times and response frequency as dependent variables. DDMs allow decomposing the decision process into separate cognitive mechanisms, whereby a cognitive bias was repeatedly associated with a shift in the starting point of the model. Conducting both a laboratory (N = 62) and an online study (N = 109), female participants allocated money between themselves and another person in a prosocial or selfish way. In each study, one group was instructed to give money (give frame), the other to take money (take frame). Consistent with previous studies, no differences were found in response times and response frequencies. However, in both studies, substantial bias towards the selfish option was found in the take frame groups, captured by the starting point of the DDM. Thus, our results suggest that valence framing induces a cognitive bias in decision processing in women, even when no behavioral differences are present.

The bright and dark sides of egoism

Despite its negative reputation, egoism - the excessive concern for one's own welfare - can incite prosocial behavior. So far, however, egoism-based prosociality has received little attention. Here, we first provide an overview of the conditions under which egoism turns into a prosocial motive, review the benefits and limitations of egoism-based prosociality, and compare them with empathy-driven prosocial behavior. Second, we summarize studies investigating the neural processing of egoism-based prosocial decisions, studies investigating the neural processing of empathy-based prosocial decisions, and the small number of studies that compared the neural processing of prosocial decisions elicited by the different motives. We conclude that there is evidence for differential neural networks involved in egoism and empathy-based prosocial decisions. However, this evidence is not yet conclusive, because it is mainly based on the comparison of different experimental paradigms which may exaggerate or overshadow the effect of the different motivational states. Finally, we propose paradigms and research questions that should be tackled in future research that could help to specify how egoism can be used to enhance other prosocial behavior and motivation, and the how it could be tamed.

A cultural evolutionary theory that explains both gradual and punctuated change

Cumulative cultural evolution (CCE) occurs among humans who may be presented with many similar options from which to choose, as well as many social influences and diverse environments. It is unknown what general principles underlie the wide range of CCE dynamics and whether they can all be explained by the same unified paradigm. Here, we present a scalable evolutionary model of discrete choice with social learning, based on a few behavioural science assumptions. This paradigm connects the degree of transparency in social learning to the human tendency to imitate others. Computer simulations and quantitative analysis show the interaction of three primary factors-information transparency, popularity bias and population size-drives the pace of CCE. The model predicts a stable rate of evolutionary change for modest degrees of popularity bias. As popularity bias grows, the transition from gradual to punctuated change occurs, with maladaptive subpopulations arising on their own. When the popularity bias gets too severe, CCE stops. This provides a consistent framework for explaining the rich and complex adaptive dynamics taking place in the real world, such as modern digital media.

Financial incentives facilitate stronger neural computation of prosocial decisions in lower empathic adult females

Financial incentives are commonly used to motivate behaviors. However, there is also evidence that incentives can impede the behavior they are supposed to foster, for example, documented by a decrease in blood donations if a financial incentive is offered. Based on these findings, previous studies assumed that prosocial motivation is shaped by incentives. However, so far, there is no direct evidence showing an interaction between financial incentives and a specific prosocial motive. Combining drift-diffusion modeling and fMRI, we investigated the effect of financial incentives on empathy, i.e., one of the key motives driving prosocial decisions. In the empathy-alone condition, participants made prosocial decisions based on empathy. In the empathy-bonus condition, they were offered a financial bonus for prosocial decisions, in addition to empathy induction. On average, the bonus enhanced the information accumulation in empathy-based decisions. On the neural level, this enhancement was related to the anterior insula, the same region that also correlated with empathy ratings. Moreover, the effect of the financial incentive on anterior insula activation was stronger the lower a person scored on empathy. These findings show that financial incentives enhance prosocial motivation in the absence of empathy.

Psychological Selfishness

Selfishness is central to many theories of human morality, yet its psychological nature remains largely overlooked. Psychologists often draw on classical conceptions of selfishness from evolutionary biology (i.e., selfish gene theory), economics (i.e., rational self-interest), and philosophy (i.e., psychological egoism), but such characterizations offer limited insight into the psychology of selfishness. To address this gap, we propose a novel framework in which selfishness is recast as a psychological construction. From this view, selfishness is perceived in ourselves and others when we detect a situation-specific desire to benefit the self that disregards others' desires and prevailing social expectations for the situation. We argue that detecting and deterring such psychological selfishness in both oneself and others is crucial in social life-facilitating the maintenance of social cohesion and close relationships. In addition, we show how using this psychological framework offers a richer understanding of the nature of human social behavior. Delineating a psychological construct of selfishness can promote coherence in interdisciplinary research on selfishness and provide insights for interventions to prevent or remediate the negative effects of selfishness.

The neural computation of human prosocial choices in complex motivational states

Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants' choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors.

Oxytocin and the Neurobiology of Prosocial Behavior

Humans are an unusually prosocial species, who engage in social behaviors that include altruism-whereby an individual engages in costly or risky acts to improve the welfare of another person-care, and cooperation. Current perspectives on the neurobiology of human prosociality suggest that it is deeply rooted in the neuroendocrine architecture of the social brain and emphasize the modulatory role of the neuropeptide hormone oxytocin. In this review, we provide a conceptual overview of the neurobiology of prosocial behavior with a focus on oxytocin's modulatory role in human prosociality. Specifically, we aim to encourage a better understanding of the peptide's susceptibility to diverse factors that produce heterogeneity in outcomes and the resulting methodological implications for measuring the behavioral effects of oxytocin in humans. After providing an overview of the state-of-the-art research on oxytocin's exogenous use, we elaborate on the peptide's modulatory role in the context of care-based altruism, cooperation, and conflict and discuss its potential for therapeutic interventions in psychiatric disorders characterized by social dysfunction.

Birth order and prosociality in the early adolescent brain

Birth order is a crucial environmental factor for child development. For example, later-born children are relatively unlikely to feel secure due to sibling competition or diluted parental resources. The positive effect of being earlier-born on cognitive intelligence is well-established. However, whether birth order is linked to social behavior remains controversial, and the neural correlates of birth order effects in adolescence when social cognition develops remain unknown. Here, we explored the birth order effect on prosociality using a large-scale population-based adolescent cohort. Next, since the amygdala is a key region for sociality and environmental stress, we examined amygdala substrates of the association between birth order and prosociality using a subset neuroimaging cohort. We found enhanced prosociality in later-born adolescents (N = 3160), and observed the mediating role of larger amygdala volume (N = 208) and amygdala-prefrontal functional connectivity with sex-selective effects (N = 183). We found that birth order, a non-genetic environmental factor, affects adolescent social development via different neural substrates. Our findings may indicate the later-born people’s adaptive survival strategy in stressful environments.

Reduced frontal cortical tracking of conflict between self-beneficial versus prosocial motives in Narcissistic Personality Disorder

Narcissistic Personality Disorder (NPD) entails severe impairments in interpersonal functioning that are likely driven by self-beneficial and exploitative behavior. Here, we investigate the underlying motivational and neural mechanisms of prosocial decision-making by experimentally manipulating motivational conflict between self-beneficial and prosocial incentives. One group of patients diagnosed with NPD and a group of healthy controls (CTL) were scanned using functional magnetic resonance imaging while performing a prosocial decision-making task. In this task, we systematically varied the level of conflict between self-beneficial and prosocial options on each trial. We analyzed choice behavior, response times, and neural activity in regions associated with conflict monitoring to test how motivational conflict drives prosocial choice behavior. Participants in the NPD group behaved less prosocially than the CTL group overall. Varying degrees of motivational conflict between self-beneficial and prosocial options induced response variability in both groups, but more so in the CTL group. The NPD group responded faster than the CTL group, unless choosing prosocially, which slowed response times to a level comparable to the CTL group. Additionally, neural activity tracking motivational conflict in dorsomedial prefrontal cortex was reduced in the NPD group. Collectively, low generosity in NPD appears to arise from reduced consideration of prosocial motives, which obviates motivational conflict with self-beneficial motives and entails reduced activity in neural conflict monitoring systems. Yet, our data also indicate that NPD is not marked by an absolute indifference to others' needs. This points to potentials for improving interpersonal relationships, effectively supporting the well-being of patients and their peers.

Development of functional network architecture explains changes in children's altruistically motivated helping

Childhood is marked by profound changes in prosocial behaviour. The underlying motivational mechanisms remain poorly understood. We investigated the development of altruistically motivated helping in middle childhood and the neurocognitive and -affective mechanisms driving this development. One-hundred and twenty seven 6-12 year-old children performed a novel gustatory costly helping task designed to measure altruistic motivations of helping behaviour. Neurocognitive and -affective mechanisms including emotion regulation, emotional clarity and attentional reorienting were assessed experimentally through an extensive task-battery while functional brain activity and connectivity were measured during an empathy for taste paradigm and during rest. Altruistically motivated helping increased with age. Out of all mechanisms probed for, only emotional clarity increased with age and accounted for altruistically motivated helping. This was associated with greater functional integration of the empathy-related network with fronto-parietal brain regions at rest. We isolate a highly specific neuroaffective mechanism as the crucial driver of altruistically motivated helping during child development.

Neural mechanisms of modulations of empathy and altruism by beliefs of others' pain

Perceived cues signaling others' pain induce empathy which in turn motivates altruistic behavior toward those who appear suffering. This perception-emotion-behavior reactivity is the core of human altruism but does not always occur in real-life situations. Here, by integrating behavioral and multimodal neuroimaging measures, we investigate neural mechanisms underlying modulations of empathy and altruistic behavior by beliefs of others' pain (BOP). We show evidence that lack of BOP reduces subjective estimation of others' painful feelings and decreases monetary donations to those who show pain expressions. Moreover, lack of BOP attenuates neural responses to their pain expressions within 200 ms after face onset and modulates neural responses to others' pain in the insular, post-central, and frontal cortices. Our findings suggest that BOP provide a cognitive basis of human empathy and altruism and unravel the intermediate neural mechanisms.

Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions

This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms-pertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.

EEG based functional connectivity analysis of human pain empathy towards humans and robots

Humans can show emotional reactions toward humanoid robots, such as empathy. Previous neuroimaging studies have indicated that neural responses of empathy for others' pain are modulated by an early automatic emotional sharing and a late controlled cognitive evaluation process. Recent studies about pain empathy for robots found humans present similar empathy process towards humanoid robots under painful stimuli as well as to humans. However, the whole-brain functional connectivity and the spatial dynamics of neural activities underlying empathic processes are still unknown. In the present study, the functional connectivity was investigated for ERPs recorded from 18 healthy adults who were presented with pictures of human hand and robot hand under painful and non-painful situations. Functional brain networks for both early and late empathy responses were constructed and a new parameter, empathy index (EI), was proposed to represent the empathy ability of humans quantitatively. We found that the mutual dependences between early ERP components was significantly decreased, but for the late components, there were no significant changes. The mutual dependences for human hand stimuli were larger than to robot hand stimuli for early components, but not for late components. The connectivity weights for early components were larger than late components. EI value shows significant difference between painful and non-painful stimuli, indicating it is a good indicator to represent the empathy of humans. This study enriches our understanding of the neurological mechanisms implicated in human empathy, and provides evidence of functional connectivity for both early and late responses of pain empathy towards humans and robots.

Late Frontal Negativity Discriminates Outcomes and Intentions in Trust-Repayment Behavior

Altruism (a costly action that benefits others) and reciprocity (the repayment of acts in kind) differ in that the former expresses preferences about the outcome of a social interaction, whereas the latter requires, in addition, ascribing intentions to others. Interestingly, an individual's behavior and neurophysiological activity under outcome- versus intention-based interactions has not been compared directly using different endowments in the same subject and during the same session. Here, we used a mixed version of the Dictator and the Investment games, together with electroencephalography, to uncover a subject's behavior and brain activity when challenged with endowments of different sizes in contexts that call for an altruistic (outcome-based) versus a reciprocal (intention-based) response. We found that subjects displayed positive or negative reciprocity (reciprocal responses greater or smaller than that for altruism, respectively) depending on the amount of trust they received. Furthermore, a subject's late frontal negativity differed between conditions, predicting responses to trust in intentions-based trials. Finally, brain regions related with mentalizing and cognitive control were the cortical sources of this activity. Thus, our work disentangles the behavioral components present in the repayment of trust, and sheds light on the neural activity underlying the integration of outcomes and perceived intentions in human economic interactions.

The Age-related Neural Strategy Alterations in Decision Making Under Risk

Previous studies have shown that aging is associated with changes in decision behavior. However, the neural mechanisms that underpin such age differences are inadequately understood. In this study, we aim to characterize the optimal neural model underlying a dynamic decision making task in both young and older adults, and further examine the age differences from the perspective of effective connectivity. Twenty-five young and 23 older adults performed a dynamic risk taking task, i.e., the balloon analogue risk task, in the functional magnetic resonance imaging scanner. The dynamic causal modeling analysis, with the coupling between the ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC) and anterior insula (AI) that were identified in our task-related activation and psychophysiological interaction analysis, was performed to address the best fitting neural model and characterize age differences. Although both age groups adopted the same optimal model with bidirectional connection between the VMPFC and DLPFC, older adults exhibited up-regulation in several connections and among which the increased modulatory effect of AI-to-VMPFC subserving their decision quality. Our finding suggests that older adults might utilize different neural strategy via compensation to counteract the impact of advanced age in risk taking process.

Group Cooperation, Carrying-Capacity Stress, and Intergroup Conflict

Peaceful intergroup relations deteriorate when individuals engage in parochial cooperation and parochial competition. To understand when and why intergroup relations change from peaceful to violent, we present a theoretical framework mapping out the different interdependence structures between groups. According to this framework, cooperation can lead to group expansion and ultimately to carrying-capacity stress. In such cases of endogenously created carrying-capacity stress, intergroup relations are more likely to become negatively interdependent, and parochial competition can emerge as a response. We discuss the cognitive, neural, and hormonal building blocks of parochial cooperation, and conclude that conflict between groups can be the inadvertent consequence of human preparedness - biological and cultural - to solve cooperation problems within groups.

Breaking human social decision making into multiple components and then putting them together again

Most of our waking time as human beings is spent interacting with other individuals. In order to make good decisions in this social milieu, it is often necessary to make inferences about the internal states, traits and intentions of others. Recently, some progress has been made toward uncovering the neural computations underlying human social decision-making by combining functional magnetic resonance neuroimaging (fMRI) with computational modeling of behavior. Modeling of behavioral data allows us to identify the key computations necessary for social decision-making and to determine how these computations are integrated. Furthermore, by correlating these variables against neuroimaging data, it has become possible to elucidate where in the brain various computations are implemented. Here we review the current state of knowledge in the domain of social computational neuroscience. Findings to date have emphasized that social decisions are driven by multiple computations conducted in parallel, and implemented in distinct brain regions. We suggest that further progress is going to depend on identifying how and where such variables get integrated in order to yield a coherent behavioral output.

Altruistic behaviors relieve physical pain

Engaging in altruistic behaviors is costly, but it contributes to the health and well-being of the performer of such behaviors. The present research offers a take on how this paradox can be understood. Across 2 pilot studies and 3 experiments, we showed a pain-relieving effect of performing altruistic behaviors. Acting altruistically relieved not only acutely induced physical pain among healthy adults but also chronic pain among cancer patients. Using functional MRI, we found that after individuals performed altruistic actions brain activity in the dorsal anterior cingulate cortex and bilateral insula in response to a painful shock was significantly reduced. This reduced pain-induced activation in the right insula was mediated by the neural activity in the ventral medial prefrontal cortex (VMPFC), while the activation of the VMPFC was positively correlated with the performer's experienced meaningfulness from his or her altruistic behavior. Our findings suggest that incurring personal costs to help others may buffer the performers from unpleasant conditions.

Converging Resting State Networks Unravels Potential Remote Effects of Transcranial Magnetic Stimulation for Major Depression

Despite being a commonly used protocol to treat major depressive disorder (MDD), the underlying mechanism of repetitive transcranial magnetic stimulation (rTMS) on dorsolateral prefrontal cortex (DLPFC) remains unclear. In the current study, we investigated the resting-state fMRI data of 100 healthy subjects by exploring three overlapping functional networks associated with the psychopathologically MDD-related areas (the nucleus accumbens, amygdala, and ventromedial prefrontal cortex). Our results showed that these networks converged at the bilateral DLPFC, which suggested that rTMS over DLPFC might improve MDD by remotely modulating the MDD-related areas synergistically. Additionally, they functionally converged at the DMPFC and bilateral insula which are known to be associated with MDD. These two areas could also be potential targets for rTMS treatment. Dynamic causal modelling (DCM) and Granger causality analysis (GCA) revealed that all pairwise connections among bilateral DLPFC, DMPFC, bilateral insula, and three psychopathologically MDD-related areas contained significant causality. The DCM results also suggested that most of the functional interactions between MDD-related areas and bilateral DLPFC, DMPFC, and bilateral insula can predominantly be explained by the effective connectivity from the psychopathologically MDD-related areas to the rTMS stimulation sites. Finally, we found the conventional functional connectivity to be a more representative measure to obtain connectivity parameters compared to GCA and DCM analysis. Our research helped inspecting the convergence of the functional networks related to a psychiatry disorder. The results identified potential targets for brain stimulation treatment and contributed to the optimization of patient-specific brain stimulation protocols.

Differential benefits of mental training types for attention, compassion, and theory of mind

Mindfulness- and, more generally, meditation-based interventions increasingly gain popularity, effectively promoting cognitive, affective, and social capacities. It is unclear, however, if different types of practice have the same or specific effects on mental functioning. Here we tested three consecutive three-month training modules aimed at cultivating either attention, socio-affective qualities (such as compassion), or socio-cognitive skills (such as theory of mind), in three training cohorts and a retest control cohort (N = 332). While attentional performance improved most consistently after attention training, compassion increased most after socio-affective training and theory of mind partially improved after socio-cognitive training. These results show that specific mental training practices are needed to induce plasticity in different domains of mental functioning, providing a foundation for evidence-based development of more targeted interventions adapted to the needs of different education, labor, and health settings.

Prosocial behavior relates to the rate and timing of cortical thinning from adolescence to young adulthood

Prosocial behavior, or voluntary actions that intentionally benefit others, relate to desirable developmental outcomes such as peer acceptance, while lack of prosocial behavior has been associated with several neurodevelopmental disorders. Mapping the biological foundations of prosociality may thus aid our understanding of both normal and abnormal development, yet how prosociality relates to cortical development is largely unknown. Here, relations between prosociality, as measured by the Strengths and Difficulties Questionnaire (self-report), and changes in thickness across the cortical mantle were examined using mixed-effects models. The sample consisted of 169 healthy individuals (92 females) aged 12-26 with repeated MRI from up to 3 time points, at approximately 3-year intervals (301 scans). In regions associated with social cognition and behavioral control, higher prosociality was associated with greater cortical thinning during early-to-middle adolescence, followed by attenuation of this process during the transition to young adulthood. Comparatively, lower prosociality was related to initially slower thinning, followed by comparatively protracted thinning into the mid-twenties. This study showed that prosocial behavior is associated with regional development of cortical thickness in adolescence and young adulthood. The results suggest that the rate of thinning in these regions, as well as its timing, may be factors related to prosocial behavior.

Degree and centrality-based approaches in network-based variable selection: Insights from the Singapore Longitudinal Aging Study

We describe a network-based method to obtain a subset of representative variables from clinical data of subjects of the second Singapore Longitudinal Aging Study (SLAS-2), while preserving to a good extent the predictive performance of the full set with regards to a multi-faceted index of successful aging, SAGE. To examine differences in predictive performance of high-degree nodes (“hubs”) and high-centrality ones (“cores”), we implement four subsetting strategies (two degree-based, two centrality-based) and obtain four surrogate sets of variables, which we use as input features for machine learning models to predict the SAGE index of subjects. All four models have variables belonging to the physical, cardiovascular, cognitive and immunological domains among their fifteen most important predictors. A fifth domain (leisure-time activities, LTA) is also present in some form. From a comparison of the surrogate sets’ size and predictive performance, a centrality-based approach (selection of the most central variable-nodes within each cluster) yielded the smallest-sized surrogate set, while having high prediction accuracy (measured by its model’s area-under-curve, AUC) in comparison to its analogous degree-based strategy (selection of the highest-degree nodes per cluster). Inclusion of the next most-central variables yielded negligible changes in predictive performance while more than doubling the surrogate set size. The centrality-based approach thus yields a surrogate set which offers a good balance between number of variables and prediction performance, and can act as a representative subset of the SLAS-2 clinical dataset.

Computing Social Value Conversion in the Human Brain

Social signals play powerful roles in shaping self-oriented reward valuation and decision making. These signals activate social and valuation/decision areas, but the core computation for their integration into the self-oriented decision machinery remains unclear. Here, we study how a fundamental social signal, social value (others' reward value), is converted into self-oriented decision making in the human brain. Using behavioral analysis, modeling, and neuroimaging, we show three-stage processing of social value conversion from the offer to the effective value and then to the final decision value. First, a value of others' bonus on offer, called offered value, was encoded uniquely in the right temporoparietal junction (rTPJ) and also in the left dorsolateral prefrontal cortex (ldlPFC), which is commonly activated by offered self-bonus value. The effective value, an intermediate value representing the effective influence of the offer on the decision, was represented in the right anterior insula (rAI), and the final decision value was encoded in the medial prefrontal cortex (mPFC). Second, using psychophysiological interaction and dynamic causal modeling analyses, we demonstrated three-stage feedforward processing from the rTPJ and ldPFC to the rAI and then from rAI to the mPFC. Further, we showed that these characteristics of social conversion underlie distinct sociobehavioral phenotypes. We demonstrate that the variability in the conversion underlies the difference between prosocial and selfish subjects, as seen from the differential strength of the rAI and ldlPFC coupling to the mPFC responses, respectively. Together, these findings identified fundamental neural computation processes for social value conversion underlying complex social decision making behaviors.SIGNIFICANCE STATEMENT In daily life, we make decisions based on self-interest, but also in consideration for others' status. These social influences modulate valuation and decision signals in the brain, suggesting a fundamental process called value conversion that translates social information into self-referenced decisions. However, little is known about the conversion process and its underlying brain mechanisms. We investigated value conversion using human fMRI with computational modeling and found three essential stages in a progressive brain circuit from social to empathic and decision areas. Interestingly, the brain mechanism of conversion differed between prosocial and individualistic subjects. These findings reveal how the brain processes and merges social information into the elemental flow of self-interested decision making.

The computational and neural substrates of moral strategies in social decision-making

Individuals employ different moral principles to guide their social decision-making, thus expressing a specific ‘moral strategy’. Which computations characterize different moral strategies, and how might they be instantiated in the brain? Here, we tackle these questions in the context of decisions about reciprocity using a modified Trust Game. We show that different participants spontaneously and consistently employ different moral strategies. By mapping an integrative computational model of reciprocity decisions onto brain activity using inter-subject representational similarity analysis of fMRI data, we find markedly different neural substrates for the strategies of ‘guilt aversion’ and ‘inequity aversion’, even under conditions where the two strategies produce the same choices. We also identify a new strategy, ‘moral opportunism’, in which participants adaptively switch between guilt and inequity aversion, with a corresponding switch observed in their neural activation patterns. These findings provide a valuable view into understanding how different individuals may utilize different moral principles.

The authors show that individuals apply different ‘moral strategies’ in interpersonal decision-making. These strategies are linked to distinct patterns of neural activity, even when they produce the same choice outcomes, illuminating how distinct moral principles can guide social behavior.

Oxytocin modulates social value representations in the amygdala

Humans exhibit considerable variation in how they value their own interest relative to the interests of others. Deciphering the neural codes representing potential rewards for self and others is crucial for understanding social decision-making. Here we integrate computational modeling with functional magnetic resonance imaging to investigate the neural representation of social value and the modulation by oxytocin, a nine-amino acid neuropeptide, in participants evaluating monetary allocations to self and other (self-other allocations). We found that an individual's preferred self-other allocation serves as a reference point for computing the value of potential self-other allocations. In more prosocial participants, amygdala activity encoded a social-value-distance signal; that is, the value dissimilarity between potential and preferred allocations. Intranasal oxytocin administration amplified this amygdala representation and increased prosocial behavior in more individualistic participants but not in more prosocial ones. Our results reveal a neurocomputational mechanism underlying social-value representations and suggest that oxytocin may promote prosociality by modulating social-value representations in the amygdala.

The neural circuitry of affect-induced distortions of trust

Aversive affect is likely a key source of irrational human decision-making, but still, little is known about the neural circuitry underlying emotion-cognition interactions during social behavior. We induced incidental aversive affect via prolonged periods of threat of shock, while 41 healthy participants made investment decisions concerning another person or a lottery. Negative affect reduced trust, suppressed trust-specific activity in the left temporoparietal junction (TPJ), and reduced functional connectivity between the TPJ and emotion-related regions such as the amygdala. The posterior superior temporal sulcus (pSTS) seems to play a key role in mediating the impact of affect on behavior: Functional connectivity of this brain area with left TPJ was associated with trust in the absence of negative affect, but aversive affect disrupted this association between TPJ-pSTS connectivity and behavioral trust. Our findings may be useful for a better understanding of the neural circuitry of affective distortions in healthy and pathological populations.

The Role of One's Motive in Meditation Practices and Prosociality

No individual exists without exhibiting prosociality in one or another situation during their lifetime. The argument however, is to what extent? Does it arise spontaneously, out of true empathy and compassion for others, or it is goal-oriented with some hidden motive? Here, our primary intention is to convey that, though various meditation-based interventions can be utilized for different purposes like cultivating prosocial behaviors such as compassion, empathy etc., one's underlying motive and intent seems to play a crucial role in an individual's development. Most of the studies exploring prosociality, in the context of meditation, usually do not consider the role of hidden or underlying motivation in one's prosocial expression. By considering an example of how mindfulness may sometimes lead to the wrong consequences, we try to analyze why it is important to include the aspect of inner motivation in future studies exploring the effects of meditation on prosociality. We also propose that while practicing meditation one may need traditional assistance and ethical/moral teachings in addition to those merely isolated techniques.