Predicting learning rates given motor and exploratory movement variability.

In A) and B) we predict the rate of learning (λ; y-axis) after varying motor (σm, deg; x-axis) and exploratory (σe, deg; different shaded lines) contributions to movement variability, when the our learning model experiences the steep (blue) and shallow (orange) landscapes, respectively (n = 10, 000 per data point). In both A) and B) we find that increasing either σm or σe leads to a faster rate of learning (i.e., lower magnitudes of λ).