State dependence of adaptation of force output following movement observation
Author(s) -
Paul A. Wanda,
Gang Li,
Kurt A. Thoroughman
Publication year - 2013
Publication title -
journal of neurophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 245
eISSN - 1522-1598
pISSN - 0022-3077
DOI - 10.1152/jn.00353.2012
Subject(s) - kinematics , adaptation (eye) , motor learning , movement (music) , dynamics (music) , motor control , haptic technology , task (project management) , representation (politics) , control theory (sociology) , computer science , psychology , artificial intelligence , cognitive psychology , physics , control (management) , neuroscience , engineering , acoustics , classical mechanics , pedagogy , systems engineering , politics , law , political science
Humans readily learn to move through direct physical practice and by watching the movements of others. Some researchers have proposed that action observation can inform subsequent changes in control through the acquisition of a neural representation of the novel dynamics, but to date learning following observation has been described by kinematic metrics. Here we designed an experiment to consider the specificity of adaptation to novel dynamic perturbations at the level of force generation. We measured changes in temporal patterns of force output following either the performance or observation of movements perturbed by either position- or velocity-dependent dynamic environments to 1) establish whether previously described observational motor learning effects were attributable to changes in predictive limb control and 2) determine whether such adaptation reflected a learned dependence on limb states appropriate to the haptic environment. We found that subjects who observed perturbed movements produced significant compensatory changes in their lateral force output, despite never directly experiencing force perturbations firsthand while performing the motor task. The time series of observers' adapted force outputs suggested that the state dependence of observed dynamics shapes adaptation. We conclude that the brain can transform observation of kinematics into state-dependent adaptation of reach dynamics.
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