TY - JOUR T1 - Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task A1 - Stevenson, Ian H. A1 - Fernandes, Hugo L. A1 - Vilares, Iris A1 - Wei, Kunlin A1 - Körding, Konrad P. Y1 - 2009/12/24 N2 - Author Summary There is a growing body of work demonstrating that humans are close to statistically optimal in both their perception of the world and their actions on it. That is, we seem to combine information from our sensors with the constraints and costs of moving to minimize our errors and effort. Most of the evidence for this type of behavior comes from tasks such as reaching in a small workspace or standing on a force plate passively viewing a stimulus. Although humans appear to be near-optimal for these tasks, it is not clear whether the theory holds for other tasks. Here we introduce a full-body, goal-directed task similar to surfing or snowboarding where subjects steer a cursor with their center of pressure. We find that subjects respond to sensory uncertainty near-optimally in this task, but their behavior is highly non-linear. This suggests that the computations performed by the nervous system may take into account a more complicated set of costs and constraints than previously supposed. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 5 IS - 12 UR - https://doi.org/10.1371/journal.pcbi.1000629 SP - e1000629 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000629 ER -