Analytical tools for the personalized assessment of natural behaviors are in great demand today, particularly among the community of performing artists. New wearables offer a variety of physiological signals that require proper integration in order to achieve this. Advances in this area of research would provide the artist and trainers with outcome measures of performance to help develop a standardized statistical language to facilitate communication across fields. In this work we present new visualization tools and analytics that enable the automatic identification and tracking of noise-to-signal transitions. The frequency of such transitions differentiate periods of spontaneous random noise from periods of well-structured noise in human motion. The latter are conducive of a predictive code denoting volition. The analyses are tailored to personalized tracking but also amenable to track the performance of an ensemble. We use our example to discuss new possibilities that these research tools may open for the community of performing artists.