Neural oscillations predict flow experience.
Bingxin Lin, Baoshun Guo, Lingyun Zhuang, Dan Zhang, Fei Wang
Cognitive Neurodynamics December 1, 2025 DOI: 10.1007/s11571-024-10205-x (opens in new tab) via PubMed
Summary
AI-generated from the abstractDuring flow, a state of deep immersion in an activity, the brain shows higher theta power, moderate alpha power, and lower beta power compared to non-flow states, suggesting a focused yet effortless neural pattern. Machine learning (Lasso regression) predicted individuals' subjective flow scores from EEG data with a correlation of 0.571, indicating that flow can be objectively quantified from neural oscillations.
Study at a glance
| Characteristics | Observational study Peer reviewed |
|---|---|
| Keywords | Flow experience Machine learning Oscillatory representation Quantitative prediction Video game |
| Key finding | Flow tasks, compared to non-flow tasks, exhibit higher theta power, moderate alpha power, and lower beta power, and subjective flow scores can be predicted from EEG data with a correlation of 0.571. |
Abstract
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 (p < 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.