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1 Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
2 Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, USA
* To whom correspondence should be addressed. E-mail: opher{at}bme.jhu.edu.
Accurate performance of reaching movements depends on adaptable neural circuitry that learns to predict forces and compensate for limb dynamics. In earlier experiments, we quantified generalization from training at one arm position to another position. The generalization patterns suggested that neural elements learning to predict forces coded a limb's state in an intrinsic, muscle-like coordinate system. Here, we ask test the sensitivity of these elements to the other arm by quantifying inter-arm generalization. We considered two possible coordinate systems: an intrinsic (joint) representation should generalize with mirror symmetry reflecting the joint's symmetry; an extrinsic representation should preserve the task's structure in extrinsic coordinates. Both coordinate systems of generalization were compared to a naive control group. We tested transfer in right-handed subjects both from dominant to non-dominant arm (D
ND) and vice versa (NDD). This led to a 2x3 experimental design matrix: transfer direction (DND / NDD) by coordinate system (extrinsic, intrinsic, control). Generalization occurred only from dominant to non-dominant arm and only in extrinsic coordinates. To assess the dependence of generalization on callosal inter-hemispheric communication, we tested commissurotomy patient JW. JW showed generalization from dominant to non-dominant arm in extrinsic coordinates. The results suggest that when the dominant right arm is used in learning dynamics, the information could be represented in the left hemisphere with neural elements tuned to both the right arm and the left arm. In contrast, learning with the non-dominant arm seems to rely on the elements in the non-dominant hemisphere tuned only to movements of that arm.
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