Bayesian Occam's Razor for structure selection in human motor learning

Learning structure is a key-element for achieving flexible and adaptive control in real-world environments. However, what looks easy and natural in human motor control, remains one of the main challenges in today’s robotics. Here we investigate in a quantitative manner how humans select between several learned structures when faced with novel adaptation problems. One very successful framework for modeling learning of statistical structures are hierarchical Bayesian models, because of their capability to capture statistical relationships on different levels of abstraction. Another important advantage is the automatic trade-off between prediction error and model complexity that is embodied by Bayesian inference. This so called Bayesian Occam’s Razor results from the marginalization over the model parameters when computing a model’s evidence and has the effect of penalizing unnecessarily complex models— see Figure 1.