Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.

人类经常与代理人互动，随着时间的推移，代理人的意图可能会在竞争和合作之间波动。当互动的本质（合作或竞争）没有明确和真实地发出信号时，尚不清楚大脑如何适应他人不断变化的意图。在这里，我们使用基于模型的功能磁共振成像和一项任务，其中参与者认为他们正在与另一个玩家一起玩。事实上，他们使用了一种在没有信号的情况下在合作策略和竞争策略之间交替的算法。我们证明，在竞争性和合作性专家之间进行仲裁的神经计算机制在预测选择行为方面优于其他学习模型。在大脑层面，功能磁共振成像结果表明，腹侧纹状体和腹内侧前额叶皮层追踪这些专家之间的可靠性差异。当归因竞争意图时，我们发现这些区域和区分与竞争和合作相关的预测误差的网络之间的耦合增加。这些发现提供了一种神经计算解释，说明大脑在做出适应性社会决策时如何在合作和竞争意图之间进行动态仲裁。