Spatial generalization from learning dynamics of reaching movements

R. Shadmehr, Z. M.K. Moussavi

Research output: Contribution to journalArticlepeer-review

Abstract

When subjects practice reaching movements in a force field, they learn a new sensorimotor map that associates desired trajectories to motor commands. The map is formed in the brain with elements that allow for generalization beyond the region of training. We quantified spatial generalization properties of these elements by training in one extreme of the reachable space and testing near another. Training resulted in rotations in the preferred direction (PD) of activation of some arm muscles. We designed force fields that maintained a constant rotation in muscle PDs as the shoulder joint rotated in the horizontal plane. In such fields, training in a small region resulted in generalization to near and far work spaces (80 cm). In one such field, the forces on the hand reversed directions for a given hand velocity with respect to the location of original training. Despite this, there was generalization. However, if the field was such that the change in the muscle PDs reversed as the work spaces changed, then performance was worse than performance of naive subjects. We suggest that the sensorimotor map of arm dynamics is represented in the brain by elements that globally encode the position of the arm but locally encode its velocity. The elements have preferred directions of movement but are modulated globally by the position of the shoulder joint. We suggest that tuning properties of cells in the motor system influence behavior and that this influence is reflected in the way that we learn dynamics of reaching movements.

Original languageEnglish (US)
Pages (from-to)7807-7815
Number of pages9
JournalJournal of Neuroscience
Volume20
Issue number20
DOIs
StatePublished - Oct 15 2000

Keywords

  • Computational modeling
  • Electromyography
  • Human
  • Internal model
  • Motor control
  • Motor cortex
  • Motor learning

ASJC Scopus subject areas

  • Neuroscience(all)

Fingerprint Dive into the research topics of 'Spatial generalization from learning dynamics of reaching movements'. Together they form a unique fingerprint.

Cite this