Bayesian integration and non-linear feedback control in a full-body motor task.
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.
Stevenson
Ian H
IH
Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, USA. i-stevenson@northwestern.edu
Fernandes
Hugo L
HL
Vilares
Iris
I
Wei
Kunlin
K
Körding
Konrad P
KP
D000328:Adult; D001499:Bayes Theorem; D003198:Computer Simulation; D025461:Feedback, Physiological / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D017711:Nonlinear Dynamics; D004856:Postural Balance / Q000502:physiology*; D011187:Posture / Q000502:physiology*; D013647:Task Performance and Analysis