Neurocomputational evidence that conflicting prosocial motives guide distributive justice

A computational account of multiple motives guiding context-dependent prosocial behavior

Prosocial behaviors play a pivotal role for human societies, shaping critical domains such as healthcare, education, taxation, and welfare. Despite the ubiquity of norms that prescribe prosocial actions, individuals do not adhere to them consistently but often behave selfishly, thereby harming the collective good. Interventions to promote prosociality would therefore be beneficial but are often ineffective because we lack a thorough understanding of the various motives that govern prosocial behavior across different contexts. Here we present a computational and experimental framework to identify motives behind individual prosocial choices and to characterize how these vary across contexts with changing social norms. Using a series of experiments in which 575 participants either judge the normative appropriateness of different prosocial actions or choose between prosocial and selfish actions themselves, we first show that while most individuals are consistent in their judgements about behavior appropriateness, the actual prosocial behavior varies strongly across people. We used computational decision models to quantify the conflicting motives underlying the prosocial judgements and decisions, combined with a clustering approach to characterize different types of individuals whose judgements and choices reflect different motivational profiles. We identified four such types: Unconditionally selfish participants never behave prosocially, Cost-sensitive participants behave selfishly when prosocial actions are costly, Efficiency-sensitive participants choose actions that maximize total wealth, and Harm-sensitive participants prioritize avoiding harming others. When these four types of participants were exposed to different social environments where norms were judged or followed more or less by the group, they responded in fundamentally different ways to this change in others' behavior. Our approach helps us better understand the origins of heterogeneity in prosocial judgments and behaviors and may have implications for policy making and the design of behavioral interventions.

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 toward prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity toward high and low merit recipients by influencing other- 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 toward 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.

Neurostructural correlates of harm action/outcome aversion: The role of empathy

Harm aversion is essential for normal human functioning; however, the neuroanatomical mechanisms underlying harm aversion remain unclear. To explore this issue, we examined the brain structures associated with the two distinct dimensions of harm aversion (harm action/outcome aversion) and the potential mediating role of the four aspects of empathy: fantasy, perspective-taking, empathic concern, and personal distress. A sample of 214 healthy young adults underwent structural magnetic resonance imaging. Voxel-based morphometry was used to assess regional gray matter volume (rGMV) and regional gray matter density (rGMD). Whole-brain multiple regression analysis revealed significant correlations between harm action aversion and rGMV/rGMD in various brain regions, including the inferior frontal gyrus (IFG) and precuneus for both rGMV and rGMD, the cerebellum for rGMV, and the superior frontal gyrus for rGMD. The rGMV/rGMD in the IFG and the rGMD in the primary somatosensory cortex (S1) were correlated with harm outcome aversion. Utilizing 10-fold balanced cross-validation analysis, we confirmed the robustness of these significant associations between rGMV/rGMD in these brain regions and harm action/outcome aversion. Importantly, mediation analysis revealed that empathic concern mediated the relationship between rGMV/rGMD in the precuneus and harm action aversion. Additionally, empathic concern, personal distress, and total empathy mediated the relationship between rGMD in the S1 and harm outcome aversion. These findings enhance our understanding of the neural mechanism of harm aversion by integrating insights from the brain structure, harm aversion, and the personality hierarchy models while also extending the frontal asymmetry model of Emotion.

Competing fairness ideals underlie wealth inequality across decision contexts

Wealth inequality is one of the most profound challenges confronting society today. However, an important issue in addressing inequality lies in formalizing the diversity of individual perspectives regarding what constitutes a fair distribution of resources. We tackle this topic by simulating wealth inequality through the allocation of bonus endowments in both Dictator Game (DG) and Ultimatum Game (UG) settings and capturing distributive decisions. By integrating a computational model, we quantify individual differences in the interplay between financial self-interest and competing pro-social motivations that emerge in the context of pre-existing wealth inequity. Our behavioral results show that, on average, pre-existing wealth influences distributive preferences across both allocations and proposals. Yet, inequality elicits non-uniform fairness concerns. Using a hierarchical clustering approach, we objectively categorise participants' behavior elucidating four distinct decision strategies: 'Pro-Self', 'Table Egalitarianism', 'Total Egalitarianism', and 'Moral Opportunism'. A balanced distribution of strategies is observed during allocations (DG), whereas Table Egalitarianism prevails in strategic proposals (UG), highlighting the influence of strategic considerations on decision strategy. Furthermore, we demonstrate an association between strategies across decision contexts. Our findings thus contribute a principled framework to formalize distributive preferences, revealing that, with respect to both altruistic allocations and strategic proposals, competing ideals of fairness underlie distributive preferences under wealth inequality.

Computational modeling of social decision-making

Social decision-making is guided by a complex set of social norms. Computational modeling can play a significant role in enriching our understanding of these norms and how precisely they direct social choices. Here, we highlight three major advantages to using computational modeling, particularly models derived from Utility Theory, in the study of social norms. We illustrate how such models can help generate detailed processes of decision-making, enforce theoretical precision by delineating abstract concepts, and unpack when, and why, people adhere to specific social norms. For each benefit, we discuss a recent study which has employed modeling in the service of assessing the role of norms in decision-making, collectively revealing how computational modeling enables better prediction, description, and explanation of important social choices.

The motive cocktail in altruistic behaviors

Prosocial motives such as social equality and efficiency are key to altruistic behaviors. However, predicting the range of altruistic behaviors in varying contexts and individuals proves challenging if we limit ourselves to one or two motives. Here we demonstrate the numerous, interdependent motives in altruistic behaviors and the possibility to disentangle them through behavioral experimental data and computational modeling. In one laboratory experiment (N = 157) and one preregistered online replication (N = 1,258), across 100 different situations, we found that both third-party punishment and third-party helping behaviors (that is, an unaffected individual punishes the transgressor or helps the victim) aligned best with a model of seven socioeconomic motives, referred to as a motive cocktail. For instance, the inequality discounting motives imply that individuals, when confronted with costly interventions, behave as if the inequality between others barely exists. The motive cocktail model also provides a unified explanation for the differences in intervention willingness between second parties (victims) and third parties, and between punishment and helping.

Self-interest overrides rank-reversal aversion in resource distribution

The equitable allocation of resources has long been a central concern for humanity, prompting extensive research into various motivations that drive the pursuit of distributive justice. In contrast to one of the most fundamental motives, inequality aversion, a conflicting motive has been proposed: rank-reversal aversion. However, it remains unclear whether this rank-reversal aversion persists in the presence of self-rank. Here we provide evidence of rank-reversal aversion in the first-party context and explore diverse moral strategies for distribution. In a modified version of the redistribution game involving 55 online-recruited participants, we observed rank-reversal aversion only when one's rank was maintained. When participants' self-rank was altered, they tended to base their behavior on their new ranks. This behavioral tendency varied among individuals, revealing three distinct moral strategies, all incorporating considerations of rank-reversal. Our findings suggest that rank-reversal aversion can indeed influence one's distribution behavior, although the extent of its impact may vary among individuals, especially when self-rank is a factor. These insights can be extended to political and economic domains, contributing to a deeper understanding of the underlying mechanisms of distributive justice.

Common and distinct equity preferences in children and adults

Fairness plays a crucial role in children's social life and has garnered considerable attention. However, previous research and theories primarily examined the development of children's fairness behaviors in the conflict between self-interest motivation and fairness-complying motivation, neglecting the influence of advantage-seeking motivation. Moreover, despite the well-established role of gain/loss frame in human decision-making, it remains largely unclear whether the framing effect modulates fairness behaviors in children. It was hypothesized that children would exhibit advantage-seeking motivation resulting in more selfish behaviors in the loss context. To examine the hypothesis, we combined an adapted dictator game and computational modeling to investigate various motivations underlying fairness behaviors of children in both loss and gain contexts and to explore the developmental directions by contrasting children and adults. In addition, the current design enabled the dissociation between fairness knowledge and behaviors by asking participants to decide for themselves (the first-party role) or for others (the third-party role). This study recruited a total of 34 children (9-10 years, Mage = 9.82, SDage = 0.38, 16 females) and 31 college students (Mage = 19.81, SDage = 1.40, 17 females). The behavioral results indicated that children behaved more selfishly in first-party and more fairly in third-party than adults, without any significant framing effects. The computational results revealed that both children and adults exhibited aversion to advantageous and disadvantageous inequity in third-party. However, they showed distinct preferences for advantageous inequity in first-party, with advantage-seeking preferences among children and aversion to advantageous inequity among adults. These findings contribute to a deeper understanding of children's social preferences and their developmental directions.

The science of justice: The neuropsychology of social punishment

The social punishment (SP) of norm violations has received much attention across multiple disciplines. However, current models of SP fail to consider the role of motivational processes, and none can explain the observed behavioral and neuropsychological differences between the two recognized forms of SP: second-party punishment (2PP) and third-party punishment (3PP). After reviewing the literature giving rise to the current models of SP, we propose a unified model of SP which integrates general psychological descriptions of decision-making as a confluence of affect, cognition, and motivation, with evidence that SP is driven by two main factors: the amount of harm (assessed primarily in the salience network) and the norm violator's intention (assessed primarily in the default-mode and central-executive networks). We posit that motivational differences between 2PP and 3PP, articulated in mesocorticolimbic pathways, impact final SP by differentially impacting the assessments of harm and intention done in these domain-general large-scale networks. This new model will lead to a better understanding of SP, which might even improve forensic, procedural, and substantive legal practices.

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.