Experimental Brain Research

, Volume 155, Issue 1, pp 1–8 | Cite as

Limitations in interlimb transfer of visuomotor rotations

Research Article

Abstract

It has been shown that learning visuomotor rotations with multiple target directions, compared with a single target direction, leads to greater generalization to untrained targets within the same limb. This implies that multiple direction learning results in a more complete internal model of the visuomotor transform. It has also been documented that the extent of transfer of movement information regarding visuomotor adaptations between the limbs is limited, relative to that between different configurations of the same limb. The present study thus investigated the origin of this restriction in interlimb transfer, by comparing the effects of eight-direction and one-direction training conditions with one arm on the subsequent performance with the other arm. It was hypothesized that if multiple direction learning leads to a more complete model of the novel visuomotor transform, interlimb transfer should be enhanced relative to that following single direction training. However, if no differences are observed between single and multiple direction training conditions, this would suggest that such learning is effector dependent. We also tested the hypothesis that interlimb transfer of visuomotor adaptation is not obligatory, by examining the effects of visual rotation direction (same or oppositely directed visuomotor rotations for the two arms). All subjects first adapted to a 30° rotation, either clockwise or counterclockwise, in the visual display during reaching movements. Following this, they adapted to a 30° rotation in either the same or opposing direction with the other arm. Results showed that initial training with the non-dominant arm facilitated subsequent performance with the dominant arm in terms of initial direction control, but only under the same rotation condition. Both single and eight direction training conditions led to substantial transfer in subsequent performance with the other arm, but multiple direction training was no more beneficial than single direction training. This finding suggests that the previously reported intralimb advantages of multiple direction learning are effector specific. Our findings are discussed in the context of hierarchical models of motor control to explain the intralimb advantages of multiple direction training.

Keywords

Handedness Visual rotation Manual asymmetry Hemispheric specialization Generalization 

References

  1. Criscimagna-Hemminger SE, Donchin O, Gazzaniga MS, Shadmehr R (2003) Learned dynamics of reaching movements generalize from dominant to nondominant arm. J Neurophysiol 89:168–176PubMedGoogle Scholar
  2. Dizio P, Lackner JR (1995) Motor adaptation to Coriolis force perturbations of reaching movements: endpoint but not trajectory adaptation transfers to the nonexposed arm. J Neurophysiol 74:1787–1792PubMedGoogle Scholar
  3. Elliott D, Roy EA (1981) Interlimb transfer after adaptation to visual displacement: patterns predicted from the functional closeness of limb neural control centres. Perception 10:383–389PubMedGoogle Scholar
  4. Ghahramani Z, Wolpert DM, Jordan MI (1996) Generalization to local remappings of the visuomotor coordinate transformation. J Neurosci 16:7085–7096PubMedGoogle Scholar
  5. Ghez C, Hening W, Favilla M (1989) Gradual specification of response amplitude in human tracking performance. Brain Behav Evol 33:69–74PubMedGoogle Scholar
  6. Ghez C, Hening W, Gordon J (1991) Organization of voluntary movement. Curr Opin Neurobiol 1:664–671PubMedGoogle Scholar
  7. Imamizu H, Shimojo S (1995) The locus of visual-motor learning at the task or manipulator level: implications from intermanual transfer. J Exp Psychol Hum Percept Perform 21:719–733PubMedGoogle Scholar
  8. Imamizu H, Uno Y, Kawato M (1998) Adaptive internal model of intrinsic kinematics involved in learning an aiming task. J Exp Psychol Hum Percept Perform 24:812–829CrossRefPubMedGoogle Scholar
  9. Jordan MI, Rumelhart DE (1992) Forward Models: supervised learning with a distal teacher. cognitive science 16:307–354CrossRefGoogle Scholar
  10. Kawato M (1999) Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9:718–727PubMedGoogle Scholar
  11. Kawato M, Isobe M, Maeda Y, Suzuki R (1988) Coordinates transformation and learning control for visually-guided voluntary movement with iteration: a Newton-like method in a function space. Biol Cybern 59:161–177PubMedGoogle Scholar
  12. Kawato M, Maeda Y, Uno Y, Suzuki R (1990) Trajectory formation of arm movement by cascade neural network model based on minimum torque-change criterion. Biol Cybern 62:275–288PubMedGoogle Scholar
  13. Krakauer JW, Ghilardi MF, Ghez C (1999) Independent learning of internal models for kinematic and dynamic control of reaching. Nat Neurosci 2:1026–1031PubMedGoogle Scholar
  14. Krakauer JW, Pine ZM, Ghilardi MF, Ghez C (2000) Learning of visuomotor transformations for vectorial planning of reaching trajectories. J Neurosci 20:8916–8924PubMedGoogle Scholar
  15. Laszlo JI, Baguley RA, Bairstow PJ (1970) Bilateral transfer in tapping skill in the absence of peripheral information. J Mot Behav 2:261–271Google Scholar
  16. Logan GD (1988) Toward an instance theory of automatization. Psychol Rev 95:492–527CrossRefGoogle Scholar
  17. Marzi CA, Bisiacchi P, Nicoletti R (1991) Is interhemispheric transfer of visuomotor information asymmetric? Evidence from a meta-analysis. Neuropsychologia 29:1163–1177PubMedGoogle Scholar
  18. Morton SM, Lang CE, Bastian AJ (2001) Inter- and intra-limb generalization of adaptation during catching. Exp Brain Res 141:438–445PubMedGoogle Scholar
  19. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh Inventory. Neuropsychologia 9:97–113PubMedGoogle Scholar
  20. Parlow SE, Kinsbourne M (1989) Asymmetrical transfer of training between hands: implications for interhemispheric communication in normal brain. Brain Cogn 11:98–113PubMedGoogle Scholar
  21. Rosenbaum DA, Chaiken SR (2001) Frames of reference in perceptual-motor learning: evidence from a blind manual aiming task. Psychol Res 65:119–127CrossRefPubMedGoogle Scholar
  22. Sainburg RL (2002) Evidence for a dynamic-dominance hypothesis of handedness. Exp Brain Res 142:241–258PubMedGoogle Scholar
  23. Sainburg RL, Wang J (2002) Interlimb transfer of visuomotor rotations: Independence of direction and final position information. Exp Brain Res 145:437–447PubMedGoogle Scholar
  24. Stoddard J, Vaid J (1996) Asymmetries in intermanual transfer of maze learning in right- and left-handed adults. Neuropsychologia 34:605–608PubMedGoogle Scholar
  25. Taylor HG, Heilman KM (1980) Left-hemisphere motor dominance in righthanders. Cortex 16:587–603PubMedGoogle Scholar
  26. Thut G, Cook ND, Regard M, Leenders KL, Halsband U, Landis T (1996) Intermanual transfer of proximal and distal motor engrams in humans. Expl Brain Res 108:321–327Google Scholar
  27. Tong C, Wolpert DM, Flanagan JR (2002) Kinematics and dynamics are not represented independently in motor working memory: evidence from an interference study. J Neurosci 22:1108–1113PubMedGoogle Scholar
  28. Vetter P, Goodbody SJ, Wolpert DM (1999) Evidence for an eye-centered spherical representation of the visuomotor map. J Neurophysiol 81:935–939PubMedGoogle Scholar
  29. Wang J, Sainburg RL (2003) Mechanisms underlying interlimb transfer of visuomotor rotations. Exp Brain Res 149:520–526PubMedGoogle Scholar
  30. Wigmore V, Tong C, Flanagan JR (2002) Visuomotor rotations of varying size and direction compete for a single internal model in motor working memory. J Exp Psychol Hum Percept Perform 28:447–457CrossRefPubMedGoogle Scholar
  31. Wolpert D, Ghahramani Z (2000) Computational principles of movement neuroscience. Nat Neurosci 3:1212–1217CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  1. 1.Department of KinesiologyThe Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations