CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France.
Université Claude Bernard Lyon 1, Lyon, France.
Nat Commun. 2024 Apr 12;15(1):3189. doi: 10.1038/s41467-024-47491-2.
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.
人类经常与那些意图随时间在竞争和合作之间波动的主体进行交互。当交互的性质（合作或竞争）没有明确和真实地发出信号时，大脑如何适应他人意图的波动尚不清楚。在这里，我们使用基于模型的 fMRI 和一个任务，参与者认为他们正在与另一个玩家一起玩。事实上，他们与一个算法一起玩，该算法在没有信号的情况下交替使用合作和竞争策略。我们表明，在预测选择行为方面，竞争和合作专家之间具有仲裁功能的神经计算机制优于其他学习模型。在大脑水平上，fMRI 结果表明，腹侧纹状体和腹内侧前额叶皮层跟踪这些专家之间可靠性的差异。当归因于竞争意图时，我们发现这些区域与一个网络之间的耦合增加，该网络区分与竞争和合作相关的预测误差。这些发现提供了一个神经计算解释，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。
