The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.

中文翻译：

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暗控制：默认模式网络作为强化学习代理。

默认模式网络（DMN）被认为有助于人类的基线心理活动。与其他大脑网络相比，它的能量消耗更高，并且与意识意识的紧密结合都表明它具有未知的总体功能。许多研究流派都赞成在预见未来的过程中发挥进化适应性作用。在目前的工作中，我们提出了一个过程模型，试图解释DMN 如何实现环境的连续评估和预测来指导行为。我们认为，DMN 活动的主要目的可以通过马尔可夫决策过程来描述，该过程通过替代试验和错误的价值估计来优化行动策略。我们对 DMN 功能的正式观点自然地适应了之前基于 (a) 预测编码、(b) 语义关联和 (c) 哨兵角色的解释的特殊情况。此外，这种用于 DMN 中复杂行为的神经优化的过程模型为最近在动物和人类中的实验结果提供了简洁的解释。