Abstract:

How does the brain coordinate our actions and interactions with the environment? How do we throw or bounce a ball; how do we drink a cup of coffee without spilling it? Revealing the fundamental principles that underlie motor control and skill learning in the healthy nervous system is a necessary basis to understand neurological dysfunction and to develop intervention. Recent research of my lab has examined three interactive model skills: the rhythmic task of bouncing a ball, the discrete task of throwing a projectile to hit a target, and the continuous task of carrying a cup filled with coffee. Key concepts that drive our empirical and theoretical inquiry are variability and stability. Characteristic for our approach is to start with a mechanical model of the task and render it in a virtual environment. As such, the human interacts with a known task environment. Based on stability analyses of a dynamical model of the task, we study how the neuro-mechanical system develops robust solutions to meet the task demands. Using the three skills as model examples, we show that developing skill means 1) exploiting solutions with dynamical stability, 2) finding the most error-tolerant strategy and channeling sensorimotor noise into task-irrelevant dimensions, and 3) optimizing safety margins and taking advantage of small perturbations. Based on these insights into healthy function, new intervention techniques can be developed that facilitate learning and relearning of motor tasks.

Variability, Noise, and Sensitivity to Error in Learning a Motor Task