Flow, an optimal mental state merging action and awareness, significantly impacts our emotion, performance, and well-being. However, capturing its swift fluctuations on a fine timescale is challenging due to the sparsity of the existing flow detecting tools. Here we present a fine fingertip force control (F3C) task to induce flow, wherein the task challenge is set at a compatible level with personal skill, and to quantitatively track the flow state variations from synchronous motor control performance. We extract eight performance metrics from fingertip force sequence and reveal their significant differences under distinct flow states. Further, we built a learning-based flow decoder that aims to predict the continuous flow intensity during the user experiment through the selected performance metrics, taking the self-reported flow as the label. Cross-validation shows that the predicted flow intensity reaches significant correlation with the self-reported flow intensity (r=0.81). Based on the decoding results, we observe rapid oscillations in flow fluctuations during the intervals between sparse self-reporting probes. This study showcases the feasibility of tracking intrinsic flow variations with high temporal resolution using task performance measures and may serve as foundation for future work aiming to take advantage of flow' s dynamics to enhance performance and positive emotions.

翻译：心流是一种融合行动与意识的最优心理状态，深刻影响我们的情绪、表现及幸福感。然而，由于现有心流检测工具的稀疏性，捕捉其细微时间尺度上的快速波动颇具挑战。本文提出一项精细指尖力控制（F3C）任务，通过将任务挑战设定在与个人技能相适应的水平来诱发心流，并基于同步运动控制表现定量追踪心流状态变化。我们从指尖力序列中提取八项表现指标，揭示其在不同心流状态下的显著差异。进一步，我们构建了一个基于学习的心流解码器，旨在通过选定的表现指标预测用户实验过程中的连续心流强度，并以自我报告的心流作为标签。交叉验证表明，预测的心流强度与自我报告的心流强度（r=0.81）呈显著相关。基于解码结果，我们观察到在稀疏自我报告探测间隔期间，心流波动存在快速振荡。本研究展示了利用任务表现指标以高时间分辨率跟踪内在心流变化的可行性，并为未来利用心流动力学增强表现与积极情绪的研究奠定基础。