The brain integrates streams of sensory input and builds accurate predictions, while arriving at stable percepts under disparate time scales. This stochastic process bears different dynamics for different people, yet statistical learning (SL) currently averages out, as noise, individual fluctuations in data streams registered from the brain as the person learns. We here adopt the motor systems perspective to reframe SL. Specifically, we rethink this problem using the demands that the person’s brain faces to predict, and control variations in biorhythmic activity akin to those present in bodily motions. This new approach harnesses gross data as the important signals, to reassess how individuals learn predictive information in stable and unstable environments. We find two types of learners: narrow-variance learners, who retain explicit knowledge of the regularity embedded in the stimuli -the goal. They seem to use an error-correction strategy steadily present in both stable and unstable cases. In contrast, broad-variance learners emerge only in the unstable environment. They undergo an initial period of memoryless learning characterized by a gamma process that starts out exponentially distributed but converges to Gaussian. We coin this mode exploratory, preceding the more general error-correction mode characterized by skewed-to-symmetric distributions and higher signal content from the start. Our work demonstrates that statistical learning is a highly dynamic and stochastic process, unfolding at different time scales, and evolving distinct learning strategies on demand.