Flow is characterized by a high level of intrinsic reward that results from a balance between task difficulty and individual ability. The Synchronization Theory of Flow offers an explanation for the neural basis of this process. It predicts an energetically-optimized, brainnetwork organization between cognitive control and reward regions when task difficulty and individual ability are balanced. While initial results provide support for structural predictions, the many-to-many connectivity and energetic optimality hypotheses remain untested. Our study addresses this gap. Subjects played a video game while undergoing functional magnetic resonance imaging. We experimentally manipulated task difficulty and individual ability. Using graph theoretical analyses, we show that the balanced-difficulty condition (compared to lowor high-difficulty) was associated with the highest average network degree in the fronto-parietal control network (implicated in cognitive control) and had the lowest global efficiency value, indicating low metabolic cost, and thereby testing Synchronization Theory’s core predictions.

中文翻译：

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网络神经科学揭示了媒体使用过程中流动的不同神经标志物

心流的特点是在任务难度和个人能力之间取得平衡，从而获得高水平的内在奖励。流动的同步理论为这个过程的神经基础提供了解释。当任务难度和个人能力平衡时，它预测了认知控制和奖励区域之间的能量优化的大脑网络组织。虽然初步结果为结构预测提供了支持，但多对多连通性和能量最优假设仍未得到检验。我们的研究解决了这一差距。受试者在接受功能性磁共振成像的同时玩电子游戏。我们通过实验操纵了任务难度和个人能力。使用图论分析，