公平加工的情境依赖性：来自ERP的证据

公平是人类社会生活的重要概念。大量研究采用最后通牒博弈, 发现人们具有不公平厌恶倾向, 即宁愿牺牲个人的经济利益, 也要拒绝不公平的分配提议。已有研究表明, 损失情境会增强不公平厌恶, 但其神经机制尚不清楚。本实验采用ERP技术, 运用最后通牒博弈范式, 考察两个情境因素：域(损失或获益)和博弈对象(人或计算机)对公平加工的影响。发现损失域下对不公平分配的拒绝率更高, 而博弈对象对决策行为无影响; 获益域下, 对家为人比对家为计算机诱发了更负的N1; 人机博弈时, 获益域比损失域诱发了更大的P2和LPP, 损失域比获益域、不公平提议比公平提议诱发了更大的N350, 而公平提议比不公平提议诱发了更大的LPP; 人际博弈时, 这些差异均不显著。这些结果表明, 对分配提议的大脑加工受博弈对象的调节, 人际博弈时, 对损益域、公平与不公平提议的加工类似, 而人机博弈时, 损失域和不公平提议涉及更多的抑制加工和冲突解决, 获益域和公平提议则更富动机性意义, 证实公平加工具有情境依赖性。

The Context-Dependency of Fairness Processing: Evidence from ERP Study

In human society, sharing losses is at least as common as sharing gains. Although the psychological and neural processes underlying the latter have been investigated in depth, those related to the former are not clear. Our recent study demonstrates an increased demand for fairness under adversity (e.g. loss sharing). Here we investigated how our brain encodes unfairness in the loss and gain domains using event-related potentials (ERP) technique. We adopted the Ultimatum Game (UG) to probe the processes related to fairness consideration in either gain or loss domain. In UG, two players, the proposer and the responder, bargain on how to divide a certain amount of money endowed by the experimenter. The proposer suggests a division policy, on which the responder evaluates and decides whether to accept. Upon acceptance, the money is divided as suggested; while rejection results in both players going empty-handed. Participants, as responders, were required to decide whether to accept an offer that was either fair (equal or nearly equal division) or unfair in both gain and loss domain. Offers were either made by the human partner or by the computer partner. Behavioral results replicated our previous findings that the rejection rate of unfair offers was higher in the loss than in the gain domain. ERP results revealed that the N1 amplitude was more pronounced for human partners compared with computer partners, however, this effect was only observed in the gain domain. When interacting with computer partners, unfair offers and offers in the loss domain were associated with larger N350 compared with fair offers and offers in the gain domain, and offers in the gain domain elicited larger P2 than offers in the loss domain, whereas fair offers and offers in the gain domain were associated with larger LPP than unfair offers and offers in the loss domain. In addition, these differences in ERP responses were diminished when the interacting partners were humans. These findings suggest that fairness processing is modulated by the property of the partner and gain-loss domain. In human-computer interaction, unfair offers and offers in the loss domain elicit more inhibition and conflict resolving process, while fair offers and offers in the gain domain are more motivationally significant to human. The present findings support the view that fairness processing is context-dependent, in which factors like gain-loss domain and the property of the partner play a role.