The Statistical Determinants of the Speed of Motor Learning

It has recently been suggested that movement variability directly increases the speed of motor learning. Here we use computational modeling of motor adaptation to show that variability can have a broad range of effects on learning, both negative and positive. Experimentally, we also find contributing and decelerating effects. Lastly, through a meta-analysis of published papers, we verify that across a wide range of experiments, movement variability has no statistical relation with learning rate. While motor learning is a complex process that can be modeled, further research is needed to understand the relative importance of the involved factors.Author Summary: Variability is a fundamental component of our motor behaviors. It is caused by numerous factors, including sensory, planning, neuromuscular noise, as well as random external perturbations. Investigation of its underpinnings has been a driving force for numerous theoretical advances in motor control. Recently, it has been suggested that initial motor variability can promote the speed of motor learning. We first demonstrate with a series of simulations of a common learning model that different factors leading to increased variability can affect learning rate in completely different directions, instead of merely the positive trend as claimed. Second, we present experimental evidence that sensory uncertainty, which affects motor variability, instead of variability per se, determines learning speed during trial-by-trial random perturbations. Third, we present results from a meta-analysis of published studies that show the same lack of positive correlation. We conclude that motor learning is not generally facilitated by initial motor variability. Instead, their relationship should be investigated by considering the factors that affect variability in a task-specific manner.