摘要： Justice is one of fundamental principles in human evolution, and justice sensitivity, both from the proself perspective (e.g., as victim) and the prosocial perspective (e.g., as observer, beneficiary, and perpetrator), matters in mental wellness and social interaction. However, it remains unclear to what extent individual difference in justice sensitivity is influenced by genetic versus environmental factors. Using a sample with 244 twin pairs, the present research was an attempt to determine what extent genetic factor plays a role in the inter-individual difference of justice sensitivity as well as whether different facets of justice sensitivity, namely, proself and prosocial perspective, share common genetic basis. Results showed that (1) all the four facets of justice sensitivity were moderately heritable (21%–33%) and that the non-shared environmental factors accounted for the rest variations (67%–79%); (2) associations between the prosocial facets of justice sensitivity were driven by common genetics (rg: .50–.65) and non-shared environmental (re: .24–.65) influences, whereas no strong evidence supported a genetic correlation between proself and prosocial justice sensitivity. The current findings provide novel evidence that sensitivity to injustice, especially to others’ suffering, is fundamentally grounded upon genetic origin, thus shedding light on the nature and nurture aspects of justice behavior.
摘要：Abstract：Individuals have been observed to show higher propensity to make risk investments using non-labor income compared to labor income, although the underlying mechanisms behind this phenomenon remain unclear. In this study, we proposed that non-labor income leads to a higher prior expectation of risky investment and a reduced sensitivity towards losses. To quantitatively test this hypothesis, we employed computational modeling. A total 103 participants were recruited and completed the Balloon Analogue Risk Task (BART) with an equal monetary endowment, either as a token for completion of survey questionnaires (labor income) or as a prize from a lucky draw game (non-labor income). We found that individuals endowed with non-labor income made more risky investments in the BART compared to those with labor income. To formally compare the differences in the dynamic risk investment process between individuals with different source of income, we built four candidate computational models (Bayesian Sequential Risk-taking Model, Target Model, Scaled Target Learning Model and Scaled Target Learning with Decay Model (STL-D)). Through computational modeling, we found that within STL-D, the optimal model, the non-labor income group preset a higher targeted number of pumps at the beginning, showed a lower learning rate towards loss trials where the balloon exploded, and had lower behavioral consistency. Our study suggests that the increased tendency for risky investments with non-labor income can be attributed to an increase in prior expectations on risk-taking and a diminished sensitivity towards loss. These findings provide potential intervention targets to mitigate irrational investments associated with non-labor income.