title: The Sensorimotor System Can Sculpt Behaviorally Relevant Representations for Motor Learning.
abstract: The coordinate system in which humans learn novel motor skills is controversial. The representation of sensorimotor skills has been extensively studied by examining generalization after learning perturbations specifically designed to be ambiguous as to their coordinate system. Recent studies have found that learning is not represented in any simple coordinate system and can potentially be accounted for by a mixed representation. Here, instead of probing generalization, which has led to conflicting results, we examine whether novel dynamics can be learned when explicitly and unambiguously presented in particular coordinate systems. Subjects performed center-out reaches to targets in the presence of a force field, while varying the orientation of their hand (i.e., the wrist angle) across trials. Different groups of subjects experienced force fields that were explicitly presented either in Cartesian coordinates (field independent of hand orientation), in object coordinates (field rotated with hand orientation), or in anti-object coordinates (field rotated counter to hand orientation). Subjects learned to represent the dynamics when presented in either Cartesian or object coordinates, learning these as well as an ambiguous force field. However, learning was slower for the object-based dynamics and substantially impaired for the anti-object presentation. Our results show that the motor system is able to tune its representation to at least two natural coordinate systems but is impaired when the representation of the task does not correspond to a behaviorally relevant coordinate system. Our results show that the motor system can sculpt its representation through experience to match those of natural tasks.
authors: Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom; Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, 80992 Munich, Germany.
authors: David W
authors: 0000-0001-9530-0820
authors: DW
authors: Franklin
authors: Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom; Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, United Kingdom.
authors: Alexandra V
authors: 0000-0001-8628-6174
authors: AV
authors: Batchelor
authors: Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge , Cambridge CB2 1PZ, United Kingdom.
authors: Daniel M
authors: 0000-0003-2011-2790
authors: DM
authors: Wolpert
mesh_terms: D000222:Adaptation, Physiological; D000223:Adaptation, Psychological; D000328:Adult; D000704:Analysis of Variance; D001132:Arm / Q000502:physiology; D001696:Biomechanical Phenomena; D005260:Female; D006225:Hand / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology; D008297:Male; D009048:Motor Skills / Q000502:physiology; D010812:Physical Stimulation; D011897:Random Allocation
keywords: coordinate frame; dynamics; motor learning