A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability.

ABSTRACT: In complex real-life motor skills such as unconstrained throwing, performance depends on how accurate is on average the outcome of noisy, high-dimensional, and redundant actions. What characteristics of the action distribution relate to performance and how different individuals select specific action distributions are key questions in motor control. Previous computational approaches have highlighted that variability along the directions of first order derivatives of the action-to-outcome mapping affects performance the most, that different mean actions may be associated to regions of the actions space with different sensitivity to noise, and that action covariation in addition to noise magnitude matters. However, a method to relate individual high-dimensional action distribution and perfor

SUBMITTER: Tommasino P 

PROVIDER: S-EPMC8244910 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature