Zhou Xiaolin's research group published a paper in PNAS to reveal the computational neural mechanism of multiple prosocial motivational interactions affecting distributive justice

The research group of Professor Zhou Xiaolin, School of Psychological and Cognitive Sciences, Peking University/East China Normal University, and Professor Zhou Xiaolin, IDG McGovern Institute of Brain Science, Peking University, and the Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, conducted a study "Neurocomputational evidence that conflicting prosocial motives guide distributive justice" It was published
in the journal Proceedings of the National Academy of Sciences (PNAS) on November 29, 2022.
This study reveals the computational neural mechanisms
by which different prosocial motivations (including unfair aversion, hurt aversion, and hierarchical reversal aversion) interact and influence individuals' redistributive decisions in the process of wealth redistribution.
This study not only helps us to have a deeper understanding of the cognitive and biological basis of multiple prosocial motivational interactions affecting resource/wealth redistribution, but also helps to expand the one-dimensional motivational economic theory of third-party social preferences, and provides ideas and theoretical basis
for the formulation and improvement of resource and wealth redistribution systems such as tax policies.

The pursuit of fairness and justice has always been an important driving force for mankind to achieve peaceful coexistence and efficient cooperation, and it is also the cornerstone of
achieving social justice.
The principle of equitable distribution affects not only the vital interests of each individual in society (e.
g.
, wage income), but also the ideology and social welfare of social groups more broadly (e.
g.
, tax and medical resource allocation policies).

However, people often face complex situations
when it comes to the actual distribution of resources and wealth.
For example, when formulating tax policies, policy makers need to increase the proportion of taxes levied on high-income groups to reduce the gap between the rich and the poor, and at the same time protect the interests of each individual as much as possible and maintain a stable social order
.
That is to say, when solving the problem of resource and wealth redistribution, people need to consider not only the principle of fairness (such as unfair aversion), but also other social motives
that may cause individuals to no longer pursue fair distribution.
However, most previous studies have focused only on the impact of equity principles on the distribution or redistribution of resources/wealth, ignoring the important role
of other prosocial motivations in this decision-making process.

In this study, based on the relevant research of others, Zhou Xiaolin's group analyzed and separated three different prosocial motivations - unfair aversion, hurt aversion and grade reversal aversion - through computational modeling, based on the relevant research of others, with the help of a new wealth redistribution paradigm, and combined with functional magnetic resonance imaging (fMRI) technology, to investigate the neural mechanism
of different motivations processing and influencing the decision-making process when people make wealth redistribution decisions.

In the wealth redistribution paradigm, subjects are required to redistribute remuneration to two unfamiliar anonymous subjects in each round of tasks (Figure 1).

Participants were told that the two anonymous participants had previously participated in another study and had done exactly the same work and made exactly the same empowering contributions
.
The computer randomly proposes a relatively unfair initial allocation scheme (Figure 1: Initial offer).

Participants in this study, as uninterested third parties, had the right to choose one of two alternatives (Figure 1: Offer 1 and Offer 2) to replace the initial proposal
proposed by the computer.
In the no-hierarchical inversion condition (Figure 1 left), both alternatives were fairer than the computer-proposed, and both alternatives maintained the relative rank
at which two anonymous subjects were paid in the initial protocol.
In the class reversal condition (Figure 1), the computer-proposed initial scheme and the relatively unfair alternative (Offer 1) are exactly the same as the
corresponding scheme in the no grade reversal condition.
The only difference is that in the rank reversal condition, the relatively fair alternative (Offer 2) inverts the relative rank
in which two anonymous subjects were paid in the initial protocol.
If the subject's redistributive decision is influenced solely by the principle of fairness (unfair aversion), then the proportion of subjects choosing a fairer alternative (Offer 2) should be exactly the same
in both conditions.
However, the results observed were that the proportion of subjects choosing fairer alternatives in the class reversal condition was significantly lower than in the no grade reversal condition (Figure 1, right).

This result suggests that individual wealth redistribution decisions are influenced not only by the principle of fairness, but also by other motives
.

Figure 1.
Wealth redistribution paradigm: no grade reversal condition (left), grade reversal condition (middle).

Behavioral outcomes (right): Subjects chose a significantly lower proportion of the more equitable alternatives in the hierarchy reversal condition compared to the no grade reversal condition

Computational modeling analysis further helped us to explain the motivations
behind the subjects' decisions.
The results showed that subjects' redistribution decisions were influenced not only by unfair aversion (Figure 2, parameter α), but also by nociceptive aversion (Figure 2, parameter β) and hierarchical reversal aversion (Figure 2, parameter δ
).
Hurt aversion means that an individual avoids harming the interests of one party while helping the other to gain more benefits; This tendency leads individuals to avoid choosing a fairer alternative in a hierarchical reversal condition because they are unwilling to inflict additional damage on the dominant party in the initial scheme
.
Grade reversal aversion refers to the tendency of individuals to maintain existing hierarchies (avoid reversing existing hierarchies) when redistributing; This tendency can also lead individuals to avoid choosing fairer alternatives in the hierarchy reversal condition because they are unwilling to reverse the existing hierarchy in the initial scheme
.
The results of computational modeling show that three prosocial motivations, namely unfair aversion, hurt aversion, and hierarchical reversal aversion, collectively influence individual wealth redistribution decisions
.

Figure 2.
Calculate the modeling results
.
(A) Parameter recovery analysis showed that the proposed model could successfully separate three different prosocial motivations
: unfair aversion (α), hurt aversion (β), and hierarchical reversal aversion (δ).
(B) The distribution
of parameters representing the three motivations in the test population.
(C) The behavioral data generated by the simulation based on the computational model and the parameter values of each subject is highly correlated with the actual observed behavioral data

Given that behavioral and computational modeling results have shown that noccuprious aversion and hierarchical reversal aversion conflict with unfair aversion, causing subjects to no longer pursue fair distribution, the researchers hope to use brain imaging data to further investigate how individuals' processing and use of fair information changes
when different prosocial motivations conflict with each other.
The brain imaging results showed that the activity intensity of the striatum was highly correlated with fair information in the inversion condition.
In the inversion condition, the activity intensity of the striatum is no longer correlated with fair information (Figure 3B).

This result suggests that individuals may be greatly less sensitive to fair information when different prosocial motivations conflict with each other
.
The results of functional ligation further indicate that the strength of the connection between the striatum and the dorsomedial prefrontal lobe (DMPFC) is moderated by fair information in hierarchical inversion conditions (Figure 3C); The enhancement of this moderating effect was significantly correlated with a decrease in the sensitivity of the striatum to fairness information (Figure 3D), a decrease in the proportion of individuals choosing fairness options (Figure 3E left), and an increase in individual nociceptive aversion (Figure 3E right).

These results collectively show that the striatum plays an important role in fair information processing and fair decision-making, and that the functional connection between the striatum and the dorsomedial prefrontal lobe can reflect the trade-off between the two motivations of unfair aversion and hurt aversion
.

Figure 3.
Brain imaging analysis results
.
(A) As the striatum
of the seed brain region.
(B) in the no-inversion condition, striatal activity intensity correlates with fair information; In the inversion condition, the striatal activity intensity was less correlated with fair information
.
(C) The strength of functional connection between the dorsomedial prefrontal lobe and the striatum is moderated
by fair information in the inversion condition.
(D) The greater the DMPFC-Striatum ligation strength is affected by fair information, the less sensitive striatal activity is to fair information
.
(E) The greater the DMPFC-Striatum connection strength is affected by fair information, the lower the proportion of individuals choosing fair options (left), and the stronger the injury aversion exhibited by individuals (right)

In addition, brain imaging results also found that different prosocial motivations may interact with different frontal brain regions through the striatum, influencing individual redistribution decisions
.
Specifically, individuals with a stronger unfair aversion showed a stronger functional connection between the striatum and the inferior frontal gyrus (IFG) when choosing unfair options; Individuals with a stronger hierarchical inversion aversion exhibited a stronger functional connection
between the striatum and the superior frontal gyrus (SFG) when choosing unfair options.
Computational modeling and brain imaging results together show that the striatum and its interactions with different brain regions integrate three different prosocial motivations to influence individual redistributive decisions
.

Figure 4.
Feature connection analysis results
.
(A-B) Individuals with a stronger unfair aversion had a stronger functional connection between the striatum and the inferior frontal gyrus (IFG) when choosing unfair options (top); Individuals with a stronger hierarchical reversal aversion had a stronger functional connection between the striatum and the superior frontal gyrus (SFG) when choosing unfair options (bottom)

Li Yue, a doctoral student in Zhou Xiaolin's research group, and Hu Jie, a graduate doctoral student (now a postdoctoral fellow at the Department of Economics at the University of Zurich), are the co-first authors of the paper, Hu Jie and Zhou Xiaolin are the co-corresponding authors, and Professor Christian Ruff of the Zurich Center for Neuroeconomics participated in part of the work
.
The research was supported
by the National Natural Science Foundation of China (31630034, 71942001).
Hujie and Christian Ruff received a name from the European Union's Horizon 2020 research and innovation program (ERC); grant agreement No 725355, BRAINCODES
).