Tracking Dynamic Flow: Decoding Flow Fluctuations Through Performance in a Fine Motor Control Task

Flow, an optimal mental state merging action and awareness, significantly impacts our emotion, performance, and well-being. However, capturing its swift transitions 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 select eight performance metrics from fingertip force sequence and reveal their significant differences under distinct self-reported flow states. Further, we built a machine learning-based decoder that aims to predict the continuous flow intensity during the user experiment through the 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 can capture the 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.