Department of Psychology, Education, and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Developmental and Educational Psychology Unit, Leiden University, Leiden, The Netherlands.
Eur J Neurosci. 2021 Feb;53(4):947-963. doi: 10.1111/ejn.15014. Epub 2020 Nov 9.
Flow is a state of full task absorption, accompanied with a strong drive and low levels of self-referential thinking. Flow is likely when there is a match between a person's skills and the task challenge. Despite its relevance for human performance and the vast body of research on flow, there is currently still relatively little insight in its underlying neurocognitive mechanisms. In this paper, we discuss a set of large brain networks that may be involved in establishing the core dimensions of flow. We propose that dopaminergic and noradrenergic systems mediate the intrinsic motivation and activate mood states that are typical for flow. The interaction between three large-scale attentional networks, namely the Default Mode Network, Central Executive Network and the Salience Network is proposed to play a role in the strong task engagement, low self-referential thinking, feedback and feelings of control in flow. The proposed relationships between flow and the brain networks may support the generation of new hypotheses and can guide future research in this field.
心流是一种完全投入任务的状态，伴随着强烈的驱动力和低自我反思思维。当一个人的技能与任务挑战相匹配时，就有可能产生心流。尽管心流对人类表现具有重要意义，并且已经有大量关于心流的研究，但目前对于其潜在的神经认知机制仍然知之甚少。在本文中，我们讨论了一组可能参与建立心流核心维度的大型大脑网络。我们提出，多巴胺能和去甲肾上腺素能系统介导内在动机，并激活心流中典型的情绪状态。我们提出，三个大型注意力网络，即默认模式网络、中央执行网络和突显网络之间的相互作用，在心流中强烈的任务投入、低自我反思思维、反馈和控制感中发挥作用。心流与大脑网络之间的这种关系可以为新的假设提供支持，并指导该领域的未来研究。
