  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.
