Sensory Agreement Guides Kinetic Energy Optimization of Arm Movements during Object Manipulation
Author(s) -
Ali Farshchiansadegh,
Alejandro Melendez-Calderon,
Rajiv Ranganathan,
Todd Murphey,
Ferdinando A. Mussa-Ivaldi
Publication year - 2016
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1004861
Subject(s) - kinetic energy , computer science , potential energy , object (grammar) , energy (signal processing) , action (physics) , task (project management) , haptic technology , simulation , trajectory , pendulum , planar , control theory (sociology) , classical mechanics , physics , artificial intelligence , control (management) , engineering , computer graphics (images) , systems engineering , quantum mechanics , astronomy
The laws of physics establish the energetic efficiency of our movements. In some cases, like locomotion, the mechanics of the body dominate in determining the energetically optimal course of action. In other tasks, such as manipulation, energetic costs depend critically upon the variable properties of objects in the environment. Can the brain identify and follow energy-optimal motions when these motions require moving along unfamiliar trajectories? What feedback information is required for such optimal behavior to occur? To answer these questions, we asked participants to move their dominant hand between different positions while holding a virtual mechanical system with complex dynamics (a planar double pendulum). In this task, trajectories of minimum kinetic energy were along curvilinear paths. Our findings demonstrate that participants were capable of finding the energy-optimal paths, but only when provided with veridical visual and haptic information pertaining to the object, lacking which the trajectories were executed along rectilinear paths.
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