Lin Bingxin, Guo Baoshun, Zhuang Lingyun, Zhang Dan, Wang Fei
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, 100084 China.
School of Marxism, Tsinghua University, Beijing, 100084 China.
Cogn Neurodyn. 2025 Dec;19(1):1. doi: 10.1007/s11571-024-10205-x. Epub 2024 Dec 31.
Flow experience, characterized by immersion in the activity at hand, provides a motivational boost and promotes positive behaviors. However, the oscillatory representations of flow experience are still poorly understood. In this study, the difficulty of the video game was adjusted to manipulate the individual's personalized flow or non-flow state, and EEG data was recorded throughout. Our results show that, compared to non-flow tasks, flow tasks exhibit higher theta power, moderate alpha power, and lower beta power, providing evidence for a focused yet effortless brain pattern during flow. Additionally, we employed Lasso regression to predict individual subjective flow scores based on neural data, achieving a correlation coefficient of 0.571 ( < 0.01) between the EEG-predicted scores and the actual self-reported scores. Our findings offer new insights into the oscillatory representation of flow and emphasize that flow, as a measure of individual experience quality, can be objectively and quantitatively predicted through neural oscillations.
心流体验的特点是沉浸于手头的活动，它能提供动力提升并促进积极行为。然而，心流体验的振荡表征仍未得到充分理解。在本研究中，通过调整电子游戏的难度来操纵个体的个性化心流或非心流状态，并全程记录脑电图（EEG）数据。我们的结果表明，与非心流任务相比，心流任务表现出更高的θ波功率、适中的α波功率和更低的β波功率，这为心流期间专注且轻松的大脑模式提供了证据。此外，我们使用套索回归基于神经数据预测个体主观心流分数，脑电图预测分数与实际自我报告分数之间的相关系数达到了0.571（<0.01）。我们的研究结果为心流的振荡表征提供了新的见解，并强调心流作为个体体验质量的一种度量，可以通过神经振荡进行客观和定量的预测。
