A Feature-Based Network Analysis and fMRI Meta-Analysis Reveal Three Distinct Types of Prosocial Decisions

Individual differences in the neural representation of cooperation and competition

Much evidence links the Big Five's agreeableness to a propensity for cooperation and aggressiveness to a propensity for competition. However, the neural basis for these associations is unknown. In this functional magnetic resonance imaging study, using multivariate pattern analysis of data recorded during a computer game in which participants were required to construct target patterns either in cooperation or in competition with another person, we sought to determine how individual differences in neural representations of cooperative and competitive behavior relate to individual differences in agreeableness and aggressiveness. During cooperation, agreeableness was positively correlated with the consistency of spatial patterns of neural activation in the left temporoparietal junction (TPJ) and showed positive correlations with inter-subject similarity in the dynamics of neural responses in the posterior default mode network hub and areas involved in the regulation of attention, movement planning, and visual perception. During competition, aggressiveness was positively correlated with the consistency of spatial patterns in the left and right TPJ and showed positive correlations with neural dynamics in visual processing and movement regulation areas. These results are consistent with the assumption that agreeable individuals are more involved in cooperative interactions with others, whereas aggression-prone individuals are more involved in competitive interactions.

Neural asymmetry in aligning with generous versus selfish descriptive norms in a charitable donation task

Social alignment is supported by the brain's reward system (ventral striatum), presumably because attaining synchrony generates feelings of connectedness. However, this may hold only for aligning with generous others, while aligning with selfishness might threaten social connectedness. We investigated this postulated asymmetry in an incentivized fMRI charitable donation task. Participants decided how much of their endowment to donate to real charities, and how much to keep for themselves. Compared to a baseline condition, donations significantly increased or decreased in function of the presence of descriptive norms. The fMRI data reveal that processing selfish norms (more than generous ones) recruited the amygdala and anterior insula. Aligning with selfish norms correlated on average with reduced activity in the lateral prefrontal cortex (LPFC) and, at the individual level, with decreasing activity in the ventral striatum (VS). Conversely, as participants aligned more with generous norms, they showed increasing activity in the LPFC and, on average, increased activity in the VS. This increase occurred beyond the increased VS activity which was also observed in the baseline condition. Taken together, this suggests that aligning with generosity, while effortful, provides a "warm glow of herding" associated with collective giving, but that aligning with selfishness does not.

A neurocomputational account of the link between social perception and social action

People selectively help others based on perceptions of their merit or need. Here, we develop a neurocomputational account of how these social perceptions translate into social choice. Using a novel fMRI social perception task, we show that both merit and need perceptions recruited the brain's social inference network. A behavioral computational model identified two non-exclusive mechanisms underlying variance in social perceptions: a consistent tendency to perceive others as meritorious/needy (bias) and a propensity to sample and integrate normative evidence distinguishing high from low merit/need in other people (sensitivity). Variance in people's merit (but not need) bias and sensitivity independently predicted distinct aspects of altruism in a social choice task completed months later. An individual's merit bias predicted context-independent variance in people's overall other-regard during altruistic choice, biasing people towards prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity towards high and low merit recipients by influencing other-regard and self-regard during altruistic decision-making. This context-sensitive perception-action link was associated with activation in the right temporoparietal junction. Together, these findings point towards stable, biologically based individual differences in perceptual processes related to abstract social concepts like merit, and suggest that these differences may have important behavioral implications for an individual's tendency toward favoritism or discrimination in social settings.

Acute stress reduces effortful prosocial behaviour

Acute stress can change our cognition and emotions, but what specific consequences this has for human prosocial behaviour is unclear. Previous studies have mainly investigated prosociality with financial transfers in economic games and produced conflicting results. Yet a core feature of many types of prosocial behaviour is that they are effortful. We therefore examined how acute stress changes our willingness to exert effort that benefits others. Healthy male participants - half of whom were put under acute stress - made decisions whether to exert physical effort to gain money for themselves or another person. With this design, we could independently assess the effects of acute stress on prosocial, compared to self-benefitting, effortful behaviour. Compared to controls (n = 45), participants in the stress group (n = 46) chose to exert effort more often for self- than for other-benefitting rewards at a low level of effort. Additionally, the adverse effects of stress on prosocial effort were particularly pronounced in more selfish participants. Neuroimaging combined with computational modelling revealed a putative neural mechanism underlying these effects: more stressed participants showed increased activation to subjective value in the dorsal anterior cingulate cortex and anterior insula when they themselves could benefit from their exerted effort relative to when someone else could. By using an effort-based task that better approximates real-life prosocial behaviour and incorporating trait differences in prosocial tendencies, our study provides important insights into how acute stress affects prosociality and its associated neural mechanisms.

Prosocial decision-making under time pressure: Behavioral and neural mechanisms

The present study employed a novel paradigm and functional magnetic resonance imaging (fMRI) to uncover the specific regulatory mechanism of time pressure and empathy trait in prosocial decision-making, compared to self-decision making. Participants were instructed to decide whether to spend their own monetary interest to alleviate themselves (or another person) from unpleasant noise threats under high and low time pressures. On the behavioral level, results showed that high time pressure had a significant effect on reducing participants' willingness to spend money on relieving themselves from the noise, while there is a similar but not significant trend in prosocial decision-making. On the neural level, for self-concerned decision-making, low time pressure activated the bilateral insula more strongly than high time pressure. For prosocial decision-making, high time pressure suppressed activations in multiple brain regions related to empathy (temporal pole, middle temporal gyrus, and inferior frontal gyrus), valuation (medial orbitofrontal cortex), and emotion (putamen). The functional connectivity strength among these regions, especially the connectivity between the medial orbitofrontal cortex and putamen, significantly predicted the effect of time pressure on prosocial decision-making at the behavioral level. Additionally, we discovered the activation of the medial orbitofrontal cortex partially mediated the effect of empathy trait scores on prosocial decision-making. These findings suggest that (1) there are different neural underpinnings for the modulation of time pressure for self and prosocial decision-making, and (2) the empathy trait plays a crucial role in the latter.

Neural responses underlying extraordinary altruists' generosity for socially distant others

Most people are much less generous toward strangers than close others, a bias termed social discounting. But people who engage in extraordinary real-world altruism, like altruistic kidney donors, show dramatically reduced social discounting. Why they do so is unclear. Some prior research suggests reduced social discounting requires effortfully overcoming selfishness via recruitment of the temporoparietal junction. Alternatively, reduced social discounting may reflect genuinely valuing strangers' welfare more due to how the subjective value of their outcomes is encoded in regions such as rostral anterior cingulate cortex (ACC) and amygdala. We tested both hypotheses in this pre-registered study. We also tested the hypothesis that a loving-kindness meditation (LKM) training intervention would cause typical adults' neural and behavioral patterns to resemble altruists. Altruists and matched controls (N = 77) completed a social discounting task during functional magnetic resonance imaging; 25 controls were randomized to complete LKM training. Neither behavioral nor imaging analyses supported the hypothesis that altruists' reduced social discounting reflects effortfully overcoming selfishness. Instead, group differences emerged in social value encoding regions, including rostral ACC and amygdala. Activation in these regions corresponded to the subjective valuation of others' welfare predicted by the social discounting model. LKM training did not result in more generous behavioral or neural patterns, but only greater perceived difficulty during social discounting. Our results indicate extraordinary altruists' generosity results from the way regions involved in social decision-making encode the subjective value of others' welfare. Interventions aimed at promoting generosity may thus succeed to the degree they can increase the subjective valuation of others' welfare.

Towards a neurocomputational account of social controllability: From models to mental health

Controllability, or the influence one has over their surroundings, is crucial for decision-making and mental health. Traditionally, controllability is operationalized in sensorimotor terms as one's ability to exercise their actions to achieve an intended outcome (also termed "agency"). However, recent social neuroscience research suggests that humans also assess if and how they can exert influence over other people (i.e., their actions, outcomes, beliefs) to achieve desired outcomes ("social controllability"). In this review, we will synthesize empirical findings and neurocomputational frameworks related to social controllability. We first introduce the concepts of contextual and perceived controllability and their respective relevance for decision-making. Then, we outline neurocomputational frameworks that can be used to model social controllability, with a focus on behavioral economic paradigms and reinforcement learning approaches. Finally, we discuss the implications of social controllability for computational psychiatry research, using delusion and obsession-compulsion as examples. Taken together, we propose that social controllability could be a key area of investigation in future social neuroscience and computational psychiatry research.

Unselfish traits and social decision-making patterns characterize six populations of real-world extraordinary altruists

Acts of extraordinary, costly altruism, in which significant risks or costs are assumed to benefit strangers, have long represented a motivational puzzle. But the features that consistently distinguish individuals who engage in such acts have not been identified. We assess six groups of real-world extraordinary altruists who had performed costly or risky and normatively rare (<0.00005% per capita) altruistic acts: heroic rescues, non-directed and directed kidney donations, liver donations, marrow or hematopoietic stem cell donations, and humanitarian aid work. Here, we show that the features that best distinguish altruists from controls are traits and decision-making patterns indicating unusually high valuation of others' outcomes: high Honesty-Humility, reduced Social Discounting, and reduced Personal Distress. Two independent samples of adults who were asked what traits would characterize altruists failed to predict this pattern. These findings suggest that theories regarding self-focused motivations for altruism (e.g., self-enhancing reciprocity, reputation enhancement) alone are insufficient explanations for acts of real-world self-sacrifice.

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.

An Optimization Analysis Model of Tourism Specialized Villages Based on Neural Network and System Dynamics

With the rapid development of tourism, professional tourism villages emerge one after another, which has become the focus of the tourism industry. At present, there are some problems in tourism professional villages, such as imperfect management and inaccurate prediction of future development, which affect the rational allocation of tourism resources. In order to improve the distribution of tourism resources and better predict the development of tourism professional villages, it is necessary to make comprehensive judgment and analysis, especially the analysis of indicators such as the prediction and development judgment of tourism professional villages. This paper discusses the optimization analysis of the agglomeration of tourism specialized villages by backpropagation (BP) neural network and system dynamics model, analyzes the system structure of the agglomeration factors of tourism specialized villages, and promotes the intelligent integration of the agglomeration factors. The development of clusters of professional villages promotes data integration among resources, economy, society, and other elements and presents the characteristics of big data. As the level of concentration of professional villages increases, the complexity of the associated factors also increases, which increases the difficulty and effectiveness of tourism analysis. In view of this situation, taking mountain tourism as the research object, this paper proposes an improved system dynamics model based on BP, extracts features from cross factor (resource, economic, and social) data, and optimizes the relationship between professional village agglomeration and various factors. The MATLAB simulation results show that based on the improved system dynamics analysis, the simplification rate of (resources, economy, and society) data can be controlled at more than 24%, the degree of agglomeration is more than 95%, and the construction time of the relationship map of related factors is less than 40 s. Therefore, the analysis method proposed in this paper is suitable for the calculation of the agglomeration of tourism professional villages in the mountain area and can meet the needs of the development of tourism professional villages in the mountain area.