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Adaptation and spatial generalization to a triaxial visuomotor perturbation in a virtual reality environment

  • Catherine Lefrançois
  • Julie MessierEmail author
Research Article
  • 38 Downloads

Abstract

We explored visuomotor adaptation and spatial generalization of three-dimensional reaching movements performed in a virtual reality environment. We used a multiphase learning paradigm. First, subjects performed reaching movements to six targets without visual feedback (VF) (pre-exposure phase). Next, participants aimed at one target with veridical VF (baseline phase). Immediately after, they were required to adapt their movements to a triaxial visuomotor perturbation (horizontal, vertical, and sagittal translations) between actual hand motion and VF of hand motion in the virtual environment (learning phase). Finally, subjects aimed at the same targets as in the baseline (aftereffect) and pre-exposure phases (generalization) without VF (post-exposure phase). The results revealed spatial axis-dependent visuomotor adaptation capacities. First, subjects showed smaller intertrial variability along the horizontal compared to the sagittal and vertical axes during the baseline and learning phases. Second, although subjects were unaware of the visual distortion, they adapted their movements to each component of the triaxial perturbation. However, they showed reduced learning rate and less persistent adaptation (aftereffect) along the vertical than the horizontal and sagittal axes. Similarly, subjects transferred the newly learned visuomotor association to untrained regions of the workspace, but their average level of generalization was smaller along the vertical than the horizontal and sagittal axes. Collectively, our results suggest that adapting three-dimensional movements to a visual distortion involves distinct processes according to the specific sensorimotor integration demands of moving along each spatial axis. This finding supports the idea that the brain employs a modular decomposition strategy to simplify complex multidimensional visuomotor tasks.

Keywords

Movement adaptation Spatial generalization Visuomotor perturbation Reaching movement Kinematic Virtual reality 

Notes

Acknowledgements

This work was supported by the Fondation du Grand défi Pierre Lavoie, Quebec, Canada. We wish to thank Marcel Beaulieu, the engineer who provided expert technical assistance, as well as David Mongeon and Stéphanie Bergeron who helped us throughout this experiment.

References

  1. Apker GA, Karimi CP, Buneo CA (2011) Contributions of vision and proprioception to arm movement planning in the vertical plane. Neurosci Lett 503(3):186–190Google Scholar
  2. Bédard P, Song JH (2013) Attention modulates generalization of visuomotor adaptation. J Vision 13(12):12Google Scholar
  3. Berkinblit MB, Fookson OI, Smetanin B, Adamovich SV, Poizner H (1995) The interaction of visual and proprioceptive inputs in pointing to actual and remembered targets. Exp Brain Res 107(2):326–330Google Scholar
  4. Desmurget M, Jordan M, Prablanc C, Jeannerod M (1997) Constrained and unconstrained movements involve different control strategies. J Neurophysiol 77(3):1644–1650Google Scholar
  5. Ghahramani Z, Wolpert DM (1997) Modular decomposition in visuomotor learning. Nature 386(6623):392–395Google Scholar
  6. Ghahramani Z, Wolpert DM, Jordan MI (1996) Generalization to local remappings of the visuomotor coordinate transformation. J Neurosci 16(21):7085–7096Google Scholar
  7. Gordon J, Ghilardi MF, Cooper SE, Ghez C (1994) Accuracy of planar reaching movements. II. Systematic extent errors resulting from inertial anisotropy. Exp Brain Res 99(1):112–130Google Scholar
  8. Graybiel AM, Aosaki T, Flaherty AW, Kimura M (1994) The basal ganglia and adaptive motor control. Science 265(5180):1826–1831Google Scholar
  9. Heuer H, Sülzenbrück S (2012) A progression of approximations to internal models of complex visuo-motor transformations. Hum Mov Sci 31:1056–1070Google Scholar
  10. Joiner WM, Brayanov JB, Smith MA (2013) The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics. J Neurophysiol 110(4):984–998Google Scholar
  11. Jörges B, López-Moliner J (2017) Gravity as a strong prior: implications for perception and action. Front Hum Neurosci 11:203Google Scholar
  12. Kagerer FA, Contreras-Vidal JL, Stelmach GE (1997) Adaptation to gradual as compared with sudden visuo-motor distortions. Exp Brain Res 115(3):557–556Google Scholar
  13. Kluzik J, Diedrichsen J, Shadmehr R, Bastian AJ (2008) Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm? J Neurophysiol 100(3):1455–1464Google Scholar
  14. Krakauer JW, Ghilardi MF, Ghez C (1999) Independent learning of internal models for kinematic and dynamic control of reaching. Nat Neurosci 2(11):1026–1031Google Scholar
  15. Krakauer JW, Pine ZM, Ghilardi MF, Ghez C (2000) Learning of visuomotor transformations for vectorial planning of reaching trajectories. J Neurosci 20(23):8916–8924Google Scholar
  16. Krakauer JW, Ghez C, Ghilardi MF (2005) Adaptation to visuomotor transformations: consolidation, interference, and forgetting. J Neurosci 25(2):473–478Google Scholar
  17. Lacquaniti F, Guigon E, Bianchi L, Ferraina S, Caminiti R (1995) Representing spatial information for limb movement: role of area 5 in the monkey. Cereb Cortex 5(5):391–409Google Scholar
  18. Le Seac’h AB, McIntyre J (2007) Multimodal reference frame for the planning of vertical arms movements. Neurosci Lett 423(3):211–215Google Scholar
  19. Mars F, Archambault PS, Feldman AG (2003) Vestibular contribution to combined arm and trunk motion. Exp Brain Res 150:515–519Google Scholar
  20. Mattar AAG, Ostry DJ (2007) Modifiability of generalization in dynamics learning. J Neurophysiol 98(6):3321–3329Google Scholar
  21. Messier J, Kalaska JF (1997) Differential effect of task conditions on errors of direction and extent of reaching movements. Exp Brain Res 115(3):469–478Google Scholar
  22. Messier J, Kalaska JF (1999) Comparison of variability of initial kinematics and endpoints of reaching movements. Exp Brain Res 125:139–152Google Scholar
  23. Messier J, Adamovich S, Jack D, Hening W, Sage J, Poizner H (2007) Visuomotor learning in immersive 3D virtual reality in Parkinson’s disease and in aging. Exp Brain Res 179(3):457–474Google Scholar
  24. Michel C, Pisella L, Prablanc C, Rode G, Rossetti Y (2007) Enhancing visuomotor adaptation by reducing error signals: single-step (aware) versus multiple-step (unaware) exposure to wedge prisms. J Cogn Neurosci 19(2):341–350Google Scholar
  25. Mongeon D, Blanchet P, Messier J (2013) Impact of Parkinson’s disease and dopaminergic medication on adaptation to explicit and implicit visuomotor perturbations. Brain Cogn 81(2):271–282.  https://doi.org/10.1016/j.bandc.2012.12.001 Google Scholar
  26. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1):97–113Google Scholar
  27. Pearson TS, Krakauer JW, Mazzoni P (2010) Learning not to generalize: modular adaptation of visuomotor gain. J Neurophysiol 103(6):2938–2952Google Scholar
  28. Proske U (2005) What is the role of muscle receptors in proprioception? Muscle Nerve 31:780–787Google Scholar
  29. Ruttle JE, Cressman EK, Marius’t Hart B, Henriques DY (2016) Time course of reach adaptation and proprioceptive recalibration during visuomotor learning. PLoS One. 11(10):e0163695Google Scholar
  30. Saijo N, Gomi H (2010) Multiple motor learning strategies in visuomotor rotation. PLoS One 5(2):e9399Google Scholar
  31. Sainburg RL, Lateiner JE, Latash ML, Bagesteiro LB (2003) Effects of altering initial position on movement direction and extent. J Neurophysiol 89:401–415Google Scholar
  32. Shabbott BA, Sainburg RL (2010) Learning a visuomotor rotation: simultaneous visual and proprioceptive information is crucial for visuomotor remapping. Exp Brain Res 203(1):75–87Google Scholar
  33. Sober SJ, Sabes PN (2003) Multisensory integration during motor planning. J Neurosci 23(18):6982–6992Google Scholar
  34. Soechting JF, Flanders M (1989) Sensorimotor representations for pointing to targets in three-dimensional space. J Neurophysiol 62(2):582–594Google Scholar
  35. Taylor JA, Ivry RB (2013) Context-dependent generalization. Front Hum Neurosci.  https://doi.org/10.3389/fnhum.2013.00171 Google Scholar
  36. Toma S, Sciutti A, Papaxanthis C, Pozzo T (2015) Visuomotor adaptation to a visual rotation is gravity dependent. J Neurophysiol 113(6):1885–1895Google Scholar
  37. van den Dobbelsteen JJ, Brenner E, Smeets JB (2003) Adaptation of movement endpoints to perturbations of visual feedback. Exp Brain Res 148(4):471–481.  https://doi.org/10.1007/s00221-002-1321-4 Google Scholar
  38. Vetter P, Goodbody SJ, Wolpert DM (1999) Evidence for an eye-centered spherical representation of the visuomotor map. J Neurophysiol 81(2):935–939Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.École de kinésiologie et des sciences de l’activité physique, Faculté de médecineUniversité de MontréalMontrealCanada
  2. 2.Institut universitaire de gériatrie de MontréalUniversité de MontréalMontréalCanada

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