Experimental Brain Research

, Volume 212, Issue 2, pp 213–224 | Cite as

Visual target separation determines the extent of generalisation between opposing visuomotor rotations

  • Daniel G. Woolley
  • Aymar de Rugy
  • Richard G. Carson
  • Stephan Riek
Research Article

Abstract

Here we investigated the influence of angular separation between visual and motor targets on concurrent adaptation to two opposing visuomotor rotations. We inferred the extent of generalisation between opposing visuomotor rotations at individual target locations based on whether interference (negative transfer) was present. Our main finding was that dual adaptation occurred to opposing visuomotor rotations when each was associated with different visual targets but shared a common motor target. Dual adaptation could have been achieved either within a single sensorimotor map (i.e. with different mappings associated with different ranges of visual input), or by forming two different internal models (the selection of which would be based on contextual information provided by target location). In the present case, the pattern of generalisation was dependent on the relative position of the visual targets associated with each rotation. Visual targets nearest the workspace of the opposing visuomotor rotation exhibited the most interference (i.e. generalisation). When the minimum angular separation between visual targets was increased, the extent of interference was reduced. These results suggest that the separation in the range of sensory inputs is the critical requirement to support dual adaptation within a single sensorimotor mapping.

Keywords

Motor learning Motor adaptation Sensorimotor transformation Motor control Interference Generalisation 

References

  1. Baraduc P, Wolpert DM (2002) Adaptation to a visuomotor shift depends on the starting posture. J Neurophysiol 88:973–981PubMedGoogle Scholar
  2. Cohen J (1969) Statistical power analysis for the behavioural sciences. Lawrence Erlbaum Associates, HillsdaleGoogle Scholar
  3. de Rugy A (2010) Generalization of visuomotor adaptation to different muscles is less efficient: experiment and model. Hum Mov Sci 29:684–700PubMedCrossRefGoogle Scholar
  4. de Rugy A, Hinder MR, Woolley DG, Carson RG (2009) The synergistic organisation of muscle recruitment constrains visuomotor adaptation. J Neurophysiol 101:2263–2269PubMedCrossRefGoogle Scholar
  5. Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R (2005) Neural correlates of reach errors. J Neurosci 25:9919–9931PubMedCrossRefGoogle Scholar
  6. Donchin O, Francis JT, Shadmehr R (2003) Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control. J Neurosci 23:9032–9045PubMedGoogle Scholar
  7. Ghahramani Z, Wolpert DM (1997) Modular decomposition in visuomotor learning. Nature 386:392–395PubMedCrossRefGoogle Scholar
  8. Howell DC (2002) Statistical methods for psychology, 5th edn. Duxberry, Pacific GroveGoogle Scholar
  9. Hwang EJ, Donchin O, Smith MA, Shadmehr R (2003) A gain-field encoding of limb position and velocity in the internal model of arm dynamics. PLOS Biol 1:E25PubMedCrossRefGoogle Scholar
  10. Hwang EJ, Smith MA, Shadmehr R (2006) Dissociable effects of the implicit and explicit memory systems on learning control of reaching. Exp Brain Res 173:425–437PubMedCrossRefGoogle Scholar
  11. Imamizu H, Kawato M (2008) Neural correlates of predictive and postdictive switching mechanisms for internal models. J Neurosci 28:10751–10765PubMedCrossRefGoogle Scholar
  12. Imamizu H, Uno Y, Kawato M (1995) Internal representations of the motor apparatus: implications from generalization in visuomotor learning. J Exp Psychol Human 21:1174–1198CrossRefGoogle Scholar
  13. Imamizu H, Sugimoto N, Osu R, Tsutsui K, Sugiyama K, Wada Y, Kawato M (2007) Explicit contextual information selectively contributes to predictive switching of internal models. Exp Brain Res 181:395–408PubMedCrossRefGoogle 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. Krakauer JW, Mazzoni P, Ghazizadeh A, Ravindran R, Shadmehr R (2006) Generalization of motor learning depends on the history of prior action. PLOS Biol 4:e316PubMedCrossRefGoogle Scholar
  16. Lonini L, Dipietro L, Zollo L, Guglielmelli E, Krebs HI (2009) An internal model for acquisition and retention of motor learning during arm reaching. Neural Comput 21:2009–2027Google Scholar
  17. Osu R, Hirai S, Yoshioka T, Kawato M (2004) Random presentation enables subjects to adapt to two opposing forces on the hand. Nat Neurosci 7:111–112PubMedCrossRefGoogle Scholar
  18. Pearson TS, Krakauer JW, Mazzoni P (2010) Learning not to generalize: modular adaptation of visuomotor gain. J Neurophysiol 103:2938–2952PubMedCrossRefGoogle Scholar
  19. Pine ZM, Krakauer JW, Gordon J, Ghez C (1996) Learning of scaling factors and reference axes for reaching movements. Neuroreport 7:2357–2361PubMedCrossRefGoogle Scholar
  20. Poggio T, Bizzi E (2004) Generalization in vision and motor control. Nature 431:768–774PubMedCrossRefGoogle Scholar
  21. Pouget A, Sejnowski TJ (1997) A new view of hemineglect based on the response properties of parietal neurones. Philos Trans R Soc Lond B Biol Sci 352:1449–1459PubMedCrossRefGoogle Scholar
  22. Salinas E, Abbott LF (1995) Transfer of coded information from sensory to motor networks. J Neurosci 15:6461–6474PubMedGoogle Scholar
  23. Shemmell J, Forner M, Tresilian JR, Riek S, Barry B, Carson RG (2005) Neuromuscular adaptation during skill acquisition on a two degree-of-freedom target-acquisition task: Isometric torque production. J Neurophysiol 94:3046–3057PubMedCrossRefGoogle Scholar
  24. Teasdale N, Bard C, Fleury M, Young DE, Proteau L (1993) Determining movement onsets from temporal series. J Motor Behav 25:97–106CrossRefGoogle Scholar
  25. Thoroughman KA, Shadmehr R (1999) Electromyographic correlates of learning an internal model of reaching movements. J Neurosci 19:8573–8588PubMedGoogle Scholar
  26. Wang J, Sainburg RL (2005) Adaptation to visuomotor rotations remaps movement vectors, not final positions. J Neurosci 25:4024–4030PubMedCrossRefGoogle Scholar
  27. Wolpert DM, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11:1317–1329PubMedCrossRefGoogle Scholar
  28. Woolley DG, Tresilian JR, Carson RG, Riek S (2007) Dual adaptation to two opposing visuomotor rotations when each is associated with different regions of workspace. Exp Brain Res 179:155–165PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Daniel G. Woolley
    • 1
    • 2
    • 3
  • Aymar de Rugy
    • 2
  • Richard G. Carson
    • 2
    • 3
  • Stephan Riek
    • 2
  1. 1.Department of Biomedical KinesiologyResearch Centre for Movement Control and NeuroplasticityHeverleeBelgium
  2. 2.Perception and Motor Systems LaboratoryThe University of QueenslandBrisbaneAustralia
  3. 3.School of PsychologyQueen’s University BelfastBelfastNorthern Ireland, UK

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