Generalization and transfer of contextual cues in motor learning
until further notice
Number of pages
SourceJournal of Neurophysiology, 114, 3, (2015), pp. 1565-1576
Article / Letter to editor
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SW OZ DCC CO
SW OZ DCC SMN
Journal of Neurophysiology
SubjectAction, intention, and motor control; DI-BCB_DCC_Theme 2: Perception, Action and Control; Radboudumc 3: Disorders of movement DCMN: Donders Center for Medical Neuroscience
We continuously adapt our movements in daily life, forming new internal models whenever necessary and updating existing ones. Recent work has suggested that this flexibility is enabled via sensorimotor cues, serving to access the correct internal model whenever necessary and keeping new models apart from previous ones. While research to date has mainly focused on identifying the nature of such cue representations, here we investigated whether and how these cue representations generalize, interfere, and transfer within and across effector systems. Subjects were trained to make two-stage reaching movements: a premovement that served as a cue, followed by a targeted movement that was perturbed by one of two opposite curl force fields. The direction of the premovement was uniquely coupled to the direction of the ensuing force field, enabling simultaneous learning of the two respective internal models. After training, generalization of the two premovement cues' representations was tested at untrained premovement directions, within both the trained and untrained hand. We show that the individual premovement representations generalize in a Gaussian-like pattern around the trained premovement direction. When the force fields are of unequal strengths, the cue-dependent generalization skews toward the strongest field. Furthermore, generalization patterns transfer to the nontrained hand, in an extrinsic reference frame. We conclude that contextual cues do not serve as discrete switches between multiple internal models. Instead, their generalization suggests a weighted contribution of the associated internal models based on the angular separation from the trained cues to the net motor output.
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