Two topics that you might not put together, tennis and cutting edge cognitive research, are combined in this New York Times story. The story reports on the research of two University College, London scientists who have found that the cognitive patterns of athletes follow Bayesian lines. As they say in their first paragraph:

When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities—the prior—with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.

What's the point? That we learn motor techniques in a much more subtile way than previously thought. This is the sort of research that can lead to some real breakthroughs in teaching and coaching techniques.

(Thanks to Clive for the pointer!)

AuthorMark McClusky