Occam's Razor in sensorimotor learning

A large number of recent studies suggest that the sensorimotor system employs probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor - an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task, where participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models - a simple model with a large length scale, leading to smooth curves and a complex model with a short length scale, leading to more wiggly curves. In training trials participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials participants were then exposed to ambiguous stimul...