A feature-based network analysis and fMRI meta-analysis reveal three distinct types of prosocial decisions

Abstract

Tasks that measure correlates of prosocial decision-making share one common feature: agents can make choices that increase the welfare of a beneficiary. However, prosocial decisions vary widely as a function of other task features. The diverse ways that prosociality is defined and the heterogeneity of prosocial decisions have created challenges for interpreting findings across studies and identifying their neural correlates. To overcome these challenges, we aimed to organize the prosocial decision-making task-space of neuroimaging studies. We conducted a systematic search for studies in which participants made decisions to increase the welfare of others during fMRI. We identified shared and distinct features of these tasks and employed an unsupervised graph-based approach to assess how various forms of prosocial decision-making are related in terms of their low-level components (e.g., task features like potential cost to the agent or potential for reciprocity). Analyses uncovered three clusters of prosocial decisions, which we labeled cooperation, equity, and altruism. This feature-based representation of the task structure was supported by results of a neuroimaging meta-analysis that each type of prosocial decisions recruited diverging neural systems. Results clarify some of the existing heterogeneity in how prosociality is conceptualized and generate insight for future research and task paradigm development.

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

“…Note that our discounting task did not employ actual payouts but hypothetical ones; this choice was made following evidence for the validity of this design 19 and evidence that hypothetical and real payouts yield similar social discounting patterns 46,81 . Other batteries of assessment should be administered among these altruistic populations to gain further insight into the characteristics related to extraordinary altruism, such as paradigms that map onto different types of prosocial decision-making according to divergent task and neural features 82 . These might include other economic games, those that examine moral decision-making 65,83 , or those that investigate moral inferences about others 84 .…”

Neural responses underlying extraordinary altruists’ generosity for socially distant others

“…We found no group differences for generous versus selfish decisions. We instead found similar activation patterns across the full sample reliably linked to prosocial versus selfish choices (20). In addition, altruists exhibited activity in right TPJ for generous versus selfish decisions that decreased with a greater temptation to be selfish.…”

“…Consistent with behavioral findings, we did not find any differences between meditation and nonmeditation controls, so these participants were combined into a single control group for subsequent analyses. In the full sample, generous choices in contrast to selfish choices recruited several regions previously linked to prosocial decision-making (20), including left medial PFC, right superior temporal gyrus, and left temporal pole. Selfish choices in contrast to generous choices recruited regions such as left inferior frontal gyrus, bilateral pre-supplementary motor area, and bilateral posterior cingulate gyrus (Figure 4; Supplementary Table S3).…”

“…In contrast to findings suggesting that generosity is a cognitively effortful process (6), other results suggest that prosociality may instead be driven by the intrinsic subjective valuation of others. In both humans (16)(17)(18)(19)(20) and nonhuman animals (21)(22)(23), a growing body of research implicates activation in medial PFC, specifically rostral anterior cingulate cortex (ACC), in encoding the subjective valuation of others' welfare (21,24), which is thought to be modulated by signals from amygdala (22,23). These neural responses encoding the subjective value of others' welfare have been shown to give rise to the prosocial decisions that promote their welfare (25).…”

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

“…Local fitting is divided into two parts: local fitting P C 1 of cross domain agglomeration data and local fitting P C 2 of relevant factors of specialized village agglomeration. The overall adjustment reflects the relationship between the interdomain agglomeration data and the relevant factors of the agglomeration specialized village [ 25 ], which is the final adjustment value to be achieved in building this model. where P c i 1,1,1 is the initial value; min[ P m ] and P min is the minimum value of P C and P M agglomeration data; and φ ( x ) is the agglomeration degree function.…”