ROOct 7, 2019

Force Field Generalization and the Internal Representation of Motor Learning

arXiv:1910.03087v115 citations
Originality Incremental advance
AI Analysis

This work addresses the problem of understanding motor learning representations for researchers in neuroscience and robotics, but it is incremental as it builds on prior studies by incorporating limb mechanics.

The study investigated how the internal representation of motor learning changes across training directions and is influenced by limb mechanics, finding that generalization in force field adaptation is local and asymmetric, with a model accounting for limb impedance and baseline variations explaining the data better than a standard model.

When learning a new motor behavior, e.g. reaching in a force field, the nervous system builds an internal representation. Examining how subsequent reaches in unpracticed directions generalize reveals this representation. Though it is the subject of frequent studies, it is not known how this representation changes across training directions, or how changes in reach direction and the corresponding changes in limb impedance, influence measurements of it. We ran a force field adaptation experiment using eight groups of subjects each trained on one of eight standard directions and then tested for generalization in the remaining seven directions. Generalization in all directions was local and asymmetric, providing limited and unequal transfer to the left and right side of the trained target. These asymmetries were not consistent in either magnitude or direction even after correcting for changes in limb impedance, at odds with previous explanations. Relying on a standard model for generalization the inferred representations inconsistently shifted to one side or the other of their respective training direction. A second model that accounted for limb impedance and variations in baseline trajectories explained more data and the inferred representations were centered on their respective training directions. Our results highlight the influence of limb mechanics and impedance on psychophysical measurements and their interpretations for motor learning.

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