Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task: e1000629

• Published in: PLoS computational biology 2009-12, Vol.5 (12)
• Main Authors: Stevenson, Ian H, Fernandes, Hugo L, Vilares, Iris, Wei, Kunlin, Körding, Konrad P
• Format: Article
• Language: English
• Subjects: Behavior; Biomechanics; Costs; Experiments; Human subjects; Nervous system; Posture; Studies
• ISSN: 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000629

A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.