1932

Abstract

The study of motor planning and learning in humans has undergone a dramatic transformation in the 20 years since this journal's last review of this topic. The behavioral analysis of movement, the foundational approach for psychology, has been complemented by ideas from control theory, computer science, statistics, and, most notably, neuroscience. The result of this interdisciplinary approach has been a focus on the computational level of analysis, leading to the development of mechanistic models at the psychological level to explain how humans plan, execute, and consolidate skilled reaching movements. This review emphasizes new perspectives on action selection and motor planning, research that stands in contrast to the previously dominant representation-based perspective of motor programming, as well as an emerging literature highlighting the convergent operation of multiple processes in sensorimotor learning.

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2021-01-04
2024-04-24
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Literature Cited

  1. Addou T, Krouchev N, Kalaska JF 2011. Colored context cues can facilitate the ability to learn and to switch between multiple dynamical force fields. J. Neurophysiol. 106:1163–83
    [Google Scholar]
  2. Avraham G, Morehead JR, Kim HE, Ivry RB 2020. Re-exposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes. bioRxiv 2020.07.16.205609. https://doi.org/10.1101/2020.07.16.205609
    [Crossref]
  3. Bastian A, Riehle A, Erlhagen W, Schöner G 1998. Prior information preshapes the population representation of movement direction in motor cortex. NeuroReport 9:2315
    [Google Scholar]
  4. Bédard P, Sanes JN. 2014. Brain representations for acquiring and recalling visual–motor adaptations. NeuroImage 101:225–35
    [Google Scholar]
  5. Ben-Itzhak S, Karniel A. 2007. Minimum acceleration criterion with constraints implies bang-bang control as an underlying principle for optimal trajectories of arm reaching movements. Neural Comput 20:3779–812
    [Google Scholar]
  6. Bernard JA, Seidler RD. 2013. Cerebellar contributions to visuomotor adaptation and motor sequence learning: an ALE meta-analysis. Front. Hum. Neurosci. 7:27
    [Google Scholar]
  7. Bernardi NF, Van Vugt FT, Valle-Mena RR, Vahdat S, Ostry DJ 2018. Error-related persistence of motor activity in resting-state networks. J. Cogn. Neurosci. 30:121883–901
    [Google Scholar]
  8. Berniker M, Kording KP. 2011. Estimating the relevance of world disturbances to explain savings, interference and long-term motor adaptation effects. PLOS Comput. Biol. 7:10e1002210
    [Google Scholar]
  9. Bond KM, Taylor JA. 2015. Flexible explicit but rigid implicit learning in a visuomotor adaptation task. J. Neurophysiol. 113:103836–49
    [Google Scholar]
  10. Braitenberg V. 1986. Vehicles: Experiments in Synthetic Psychology Cambridge, MA: MIT Press
  11. Brashers-Krug T, Shadmehr R, Bizzi E 1996. Consolidation in human motor memory. Nature 382:6588252–55
    [Google Scholar]
  12. Brayanov JB, Press DZ, Smith MA 2012. Motor memory is encoded as a gain-field combination of intrinsic and extrinsic action representations. J. Neurosci. 32:4314951–65
    [Google Scholar]
  13. Brennan AE, Smith MA. 2015. The decay of motor memories is independent of context change detection. PLOS Comput. Biol. 11:6e1004278
    [Google Scholar]
  14. Brudner SN, Kethidi N, Graeupner D, Ivry RB, Taylor JA 2016. Delayed feedback during sensorimotor learning selectively disrupts adaptation but not strategy use. J. Neurophysiol. 115:31499–511
    [Google Scholar]
  15. Butcher PA, Ivry RB, Kuo S-H, Rydz D, Krakauer JW, Taylor JA 2017. The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks. J. Neurophysiol. 118:31622–36
    [Google Scholar]
  16. Carroll TJ, McNamee D, Ingram JN, Wolpert DM 2019. Rapid visuomotor responses reflect value-based decisions. J. Neurosci. 39:203906–20
    [Google Scholar]
  17. Cashaback JGA, McGregor HR, Mohatarem A, Gribble PL 2017. Dissociating error-based and reinforcement-based loss functions during sensorimotor learning. PLOS Comput. Biol. 13:7e1005623
    [Google Scholar]
  18. Chapman CS, Gallivan JP, Wood DK, Milne JL, Culham JC, Goodale MA 2010. Reaching for the unknown: multiple target encoding and real-time decision-making in a rapid reach task. Cognition 116:2168–76
    [Google Scholar]
  19. Charalambous CC, Alcantara CC, French MA, Li X, Matt KS et al. 2018. A single exercise bout and locomotor learning after stroke: physiological, behavioural, and computational outcomes. J. Physiol. 596:101999–2016
    [Google Scholar]
  20. Churchland MM, Afshar A, Shenoy KV 2006. A central source of movement variability. Neuron 52:61085–96
    [Google Scholar]
  21. Churchland MM, Cunningham JP, Kaufman MT, Ryu SI, Shenoy KV 2010. Cortical preparatory activity: representation of movement or first cog in a dynamical machine. Neuron 68:3387–400
    [Google Scholar]
  22. Cisek P. 2006. Integrated neural processes for defining potential actions and deciding between them: a computational model. J. Neurosci. 26:389761–70
    [Google Scholar]
  23. Cisek P. 2007. Cortical mechanisms of action selection: the affordance competition hypothesis. Philos. Trans. R. Soc. B 362:14851585–99
    [Google Scholar]
  24. Cisek P, Kalaska JF. 2005. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45:5801–14
    [Google Scholar]
  25. Classen J, Liepert J, Wise SP, Hallett M, Cohen LG 1998. Rapid plasticity of human cortical movement representation induced by practice. J. Neurophysiol. 79:21117–23
    [Google Scholar]
  26. Codol O, Holland PJ, Galea JM 2018. The relationship between reinforcement and explicit control during visuomotor adaptation. Sci. Rep. 8:9121
    [Google Scholar]
  27. Criscimagna-Hemminger SE, Shadmehr R. 2008. Consolidation patterns of human motor memory. J. Neurosci. 28:399610–18
    [Google Scholar]
  28. Dale R, Kehoe C, Spivey MJ 2007. Graded motor responses in the time course of categorizing atypical exemplars. Mem. Cogn. 35:115–28
    [Google Scholar]
  29. Day BL, Lyon IN. 2000. Voluntary modification of automatic arm movements evoked by motion of a visual target. Exp. Brain Res. 130:2159–68
    [Google Scholar]
  30. Day KA, Roemmich RT, Taylor JA, Bastian AJ 2016. Visuomotor learning generalizes around the intended movement. eNeuro 3:2ENEURO.0005–16.2016
    [Google Scholar]
  31. Dekleva BM, Kording KP, Miller LE 2018. Single reach plans in dorsal premotor cortex during a two-target task. Nat. Commun. 9:3556
    [Google Scholar]
  32. Derosiere G, Klein P-A, Nozaradan S, Zénon A, Mouraux A, Duque J 2018. Visuomotor correlates of conflict expectation in the context of motor decisions. J. Neurosci. 38:449486–504
    [Google Scholar]
  33. Desmurget M, Pélisson D, Urquizar C, Prablanc C, Alexander GE, Grafton ST 1998. Functional anatomy of saccadic adaptation in humans. Nat. Neurosci. 1:6524–28
    [Google Scholar]
  34. Dhawale AK, Smith MA, Ölveczky BP 2017. The role of variability in motor learning. Annu. Rev. Neurosci. 40:479–98
    [Google Scholar]
  35. Diedrichsen J. 2007. Optimal task-dependent changes of bimanual feedback control and adaptation. Curr. Biol. 17:191675–79
    [Google Scholar]
  36. Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R 2005. Neural correlates of reach errors. J. Neurosci. 25:439919–31
    [Google Scholar]
  37. Diedrichsen J, Hazeltine E, Kennerley S, Ivry RB 2001. Moving to directly cued locations abolishes spatial interference during bimanual actions. Psychol. Sci. 12:6493–98
    [Google Scholar]
  38. Diedrichsen J, Shadmehr R, Ivry RB 2010a. The coordination of movement: optimal feedback control and beyond. Trends Cogn. Sci. 14:131–39
    [Google Scholar]
  39. Diedrichsen J, White O, Newman D, Lally N 2010b. Use-dependent and error-based learning of motor behaviors. J. Neurosci. 30:155159–66
    [Google Scholar]
  40. Dingwell JB, Mah CD, Mussa-Ivaldi FA 2004. Experimentally confirmed mathematical model for human control of a non-rigid object. J. Neurophysiol. 91:31158–70
    [Google Scholar]
  41. 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. 23:279032–45
    [Google Scholar]
  42. Elsayed GF, Lara AH, Kaufman MT, Churchland MM, Cunningham JP 2016. Reorganization between preparatory and movement population responses in motor cortex. Nat. Commun. 7:13239
    [Google Scholar]
  43. Faisal AA, Selen LPJ, Wolpert DM 2008. Noise in the nervous system. Nat. Rev. Neurosci. 9:4292–303
    [Google Scholar]
  44. Feldman AG. 1986. Once more on the equilibrium-point hypothesis (λ model) for motor control. J. Motor Behav. 18:117–54
    [Google Scholar]
  45. Fernandez-Ruiz J, Wong W, Armstrong IT, Flanagan JR 2011. Relation between reaction time and reach errors during visuomotor adaptation. Behav. Brain Res. 219:18–14
    [Google Scholar]
  46. Fine MS, Thoroughman KA. 2006. Motor adaptation to single force pulses: sensitive to direction but insensitive to within-movement pulse placement and magnitude. J. Neurophysiol. 96:2710–20
    [Google Scholar]
  47. Fine MS, Thoroughman KA. 2007. Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics. J. Neurophysiol. 98:31392–404
    [Google Scholar]
  48. Fitts PM. 1954. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47:6381–91
    [Google Scholar]
  49. Flash T, Hogan N. 1985. The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5:71688–703
    [Google Scholar]
  50. Franklin DW, Reichenbach A, Franklin S, Diedrichsen J 2016. Temporal evolution of spatial computations for visuomotor control. J. Neurosci. 36:82329–41
    [Google Scholar]
  51. Franklin DW, Wolpert DM. 2008. Specificity of reflex adaptation for task-relevant variability. J. Neurosci. 28:5214165–75
    [Google Scholar]
  52. Fu QG, Suarez JI, Ebner TJ 1993. Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys. J. Neurophysiol. 70:52097–116
    [Google Scholar]
  53. Galea JM, Mallia E, Rothwell J, Diedrichsen J 2015. The dissociable effects of punishment and reward on motor learning. Nat. Neurosci. 18:4597–602
    [Google Scholar]
  54. Gallivan JP, Chapman CS, Wolpert DM, Flanagan JR 2018. Decision-making in sensorimotor control. Nat. Rev. Neurosci. 19:9519–34
    [Google Scholar]
  55. Gallivan JP, Stewart BM, Baugh LA, Wolpert DM, Flanagan JR 2017. Rapid automatic motor encoding of competing reach options. Cell Rep 18:71619–26
    [Google Scholar]
  56. Gandolfo F, Mussa-Ivaldi FA, Bizzi E 1996. Motor learning by field approximation. PNAS 93:93843–46
    [Google Scholar]
  57. Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT 1982. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J. Neurosci. 2:111527–37
    [Google Scholar]
  58. Gonzalez Castro LN, Hadjiosif AM, Hemphill MA, Smith MA 2014. Environmental consistency determines the rate of motor adaptation. Curr. Biol. 24:101050–61
    [Google Scholar]
  59. Gordon J, Ghilardi MF, Cooper SE, Ghez C 1994a. Accuracy of planar reaching movements. II. Systematic extent errors resulting from inertial anisotropy. Exp. Brain Res. 99:1112–30
    [Google Scholar]
  60. Gordon J, Ghilardi MF, Ghez C 1994b. Accuracy of planar reaching movements. I. Independence of direction and extent variability. Exp. Brain Res. 99:197–111
    [Google Scholar]
  61. Graydon FX, Friston KJ, Thomas CG, Brooks VB, Menon RS 2005. Learning-related fMRI activation associated with a rotational visuo-motor transformation. Cogn. Brain Res. 22:3373–83
    [Google Scholar]
  62. Griffin DM, Hoffman DS, Strick PL 2015. Corticomotoneuronal cells are “functionally tuned.”. Science 350:6261667–70
    [Google Scholar]
  63. Gu C, Pruszynski JA, Gribble PL, Corneil BD 2018. A rapid visuomotor response on the human upper limb is selectively influenced by implicit motor learning. J. Neurophysiol. 121:185–95
    [Google Scholar]
  64. Gupta R, Ashe J. 2007. Lack of adaptation to random conflicting force fields of variable magnitude. J. Neurophysiol. 97:1738–45
    [Google Scholar]
  65. Hadjiosif A, Smith M. 2013. Savings is restricted to the temporally labile component of motor adaptation. Proceedings of the 2013 Conference on Translational and Computational Motor Control409–19 New York: ACM
    [Google Scholar]
  66. Haith AM, Huberdeau DM, Krakauer JW 2015a. Hedging your bets: intermediate movements as optimal behavior in the context of an incomplete decision. PLOS Comput. Biol. 11:3e1004171
    [Google Scholar]
  67. Haith AM, Huberdeau DM, Krakauer JW 2015b. The influence of movement preparation time on the expression of visuomotor learning and savings. J. Neurosci. 35:135109–17
    [Google Scholar]
  68. Haith AM, Krakauer JW. 2018. The multiple effects of practice: skill, habit and reduced cognitive load. Curr. Opin. Behav. Sci. 20:196–201
    [Google Scholar]
  69. Harris CM, Wolpert DM. 1998. Signal-dependent noise determines motor planning. Nature 394:6695780–84
    [Google Scholar]
  70. Harris CS. 1974. Beware of the straight-ahead shift—a nonperceptual change in experiments on adaptation to displaced vision. Perception 3:4461–76
    [Google Scholar]
  71. Hayashi T, Kato Y, Nozaki D 2019. Divisively normalized integration of multisensory error information develops motor memories specific to vision and proprioception. bioRxiv 561332. https://doi.org/10.1101/561332
    [Crossref]
  72. Holland PJ, Codol O, Galea JM 2018. The contribution of explicit processes to reinforcement-based motor learning. J. Neurophysiol. 119:62241–55
    [Google Scholar]
  73. Howard IS, Ford C, Cangelosi A, Franklin DW 2017. Active lead-in variability affects motor memory formation and slows motor learning. Sci. Rep. 7:7806
    [Google Scholar]
  74. Howard IS, Ingram JN, Franklin DW, Wolpert DM 2012. Gone in 0.6 seconds: The encoding of motor memories depends on recent sensorimotor states. J. Neurosci. 32:3712756–68
    [Google Scholar]
  75. Howard IS, Wolpert DM, Franklin DW 2015. The value of the follow-through derives from motor learning depending on future actions. Curr. Biol. 25:3397–401
    [Google Scholar]
  76. Huang VS, Haith A, Mazzoni P, Krakauer JW 2011. Rethinking motor learning and savings in adaptation paradigms: Model-free memory for successful actions combines with internal models. Neuron 70:4787–801
    [Google Scholar]
  77. Huberdeau DM, Haith AM, Krakauer JW 2015. Formation of a long-term memory for visuomotor adaptation following only a few trials of practice. J. Neurophysiol. 114:2969–77
    [Google Scholar]
  78. Huberdeau DM, Krakauer JW, Haith AM 2019. Practice induces a qualitative change in the memory representation for visuomotor learning. J. Neurophysiol. 122:31050–59
    [Google Scholar]
  79. Hudson TE, Landy MS. 2012. Motor learning reveals the existence of multiple codes for movement planning. J. Neurophysiol. 108:102708–16
    [Google Scholar]
  80. Hudson TE, Landy MS. 2016. Sinusoidal error perturbation reveals multiple coordinate systems for sensory motor adaptation. Vis. Res. 119:82–98
    [Google Scholar]
  81. Hudson TE, Maloney LT, Landy MS 2007. Movement planning with probabilistic target information. J. Neurophysiol. 98:53034–46
    [Google Scholar]
  82. Imamizu H, Kuroda T, Miyauchi S, Yoshioka T, Kawato M 2003. Modular organization of internal models of tools in the human cerebellum. PNAS 100:95461–66
    [Google Scholar]
  83. Imamizu H, Miyauchi S, Tamada T, Sasaki Y, Takino R et al. 2000. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403:192–95
    [Google Scholar]
  84. Ivry RB, Diedrichsen J, Spencer R, Hazeltine E, Semjen A 2004. A cognitive neuroscience perspective on bimanual coordination and interference. Neuro-Behavioral Determinants of Interlimb Coordination: A Multidisciplinary Approach SP Swinnen, J Duysens 259–95 Boston: Springer
    [Google Scholar]
  85. Izawa J, Criscimagna-Hemminger SE, Shadmehr R 2012. Cerebellar contributions to reach adaptation and learning sensory consequences of action. J. Neurosci. 32:124230–39
    [Google Scholar]
  86. Izawa J, Shadmehr R. 2011. Learning from sensory and reward prediction errors during motor adaptation. PLOS Comput. Biol. 7:3e1002012
    [Google Scholar]
  87. Jones KE, Hamilton AF, Wolpert DM 2002. Sources of signal-dependent noise during isometric force production. J. Neurophysiol. 88:31533–44
    [Google Scholar]
  88. Jordan MI, Rumelhart DE. 1992. Forward models: supervised learning with a distal teacher. Cogn. Sci. 16:3307–54
    [Google Scholar]
  89. Kakei S, Hoffman DS, Strick PL 1999. Muscle and movement representations in the primary motor cortex. Science 285:54362136–39
    [Google Scholar]
  90. Kakei S, Hoffman DS, Strick PL 2001. Direction of action is represented in the ventral premotor cortex. Nat. Neurosci. 4:101020–25
    [Google Scholar]
  91. Kelso JA, Holt KG. 1980. Exploring a vibratory systems analysis of human movement production. J. Neurophysiol. 43:51183–96
    [Google Scholar]
  92. Kim HE, Morehead JR, Parvin DE, Moazzezi R, Ivry RB 2018. Invariant errors reveal limitations in motor correction rather than constraints on error sensitivity. Commun. Biol. 1:19
    [Google Scholar]
  93. Kim HE, Parvin DE, Ivry RB 2019. The influence of task outcome on implicit motor learning. eLife 8:e39882
    [Google Scholar]
  94. Kitago T, Ryan SL, Mazzoni P, Krakauer JW, Haith AM 2013. Unlearning versus savings in visuomotor adaptation: comparing effects of washout, passage of time, and removal of errors on motor memory. Front. Hum. Neurosci. 7:307
    [Google Scholar]
  95. Klaes C, Westendorff S, Chakrabarti S, Gail A 2011. Choosing goals, not rules: deciding among rule-based action plans. Neuron 70:3536–48
    [Google Scholar]
  96. Klein P-A, Olivier E, Duque J 2012. Influence of reward on corticospinal excitability during movement preparation. J. Neurosci. 32:5018124–36
    [Google Scholar]
  97. 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. 100:31455–64
    [Google Scholar]
  98. Kording KP. 2014. Bayesian statistics: relevant for the brain. Curr. Opin. Neurobiol. 25:130–33
    [Google Scholar]
  99. Krakauer JW. 2019. The intelligent reflex. Philos. Psychol. 32:5823–31
    [Google Scholar]
  100. Krakauer JW, Ghazanfar AA, Gomez-Marin A, MacIver MA, Poeppel D 2017. Neuroscience needs behavior: correcting a reductionist bias. Neuron 93:3480–90
    [Google Scholar]
  101. Krakauer JW, Ghilardi M-F, Ghez C 1999. Independent learning of internal models for kinematic and dynamic control of reaching. Nat. Neurosci. 2:111026–31
    [Google Scholar]
  102. Krakauer JW, Ghilardi M-F, Mentis M, Barnes A, Veytsman M et al. 2004. Differential cortical and subcortical activations in learning rotations and gains for reaching: a PET study. J. Neurophysiol. 91:2924–33
    [Google Scholar]
  103. Krakauer JW, Hadjiosif AM, Xu J, Wong AL, Haith AM 2019. Motor learning. Compr. Physiol. 9:2613–63
    [Google Scholar]
  104. Krakauer JW, Mazzoni P. 2011. Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol. 21:4636–44
    [Google Scholar]
  105. Krakauer JW, Pine ZM, Ghilardi M-F, Ghez C 2000. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 20:238916–24
    [Google Scholar]
  106. Lara AH, Cunningham JP, Churchland MM 2018. Different population dynamics in the supplementary motor area and motor cortex during reaching. Nat. Commun. 9:2754
    [Google Scholar]
  107. Leib R, Karniel A. 2012. Minimum acceleration with constraints of center of mass: a unified model for arm movements and object manipulation. J. Neurophysiol. 108:61646–55
    [Google Scholar]
  108. Leow L-A, Marinovic W, de Rugy A, Carroll TJ 2018. Task errors contribute to implicit aftereffects in sensorimotor adaptation. Eur. J. Neurosci. 48:113397–409
    [Google Scholar]
  109. Levin MF, Weiss PL, Keshner EA 2015. Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles. Phys. Ther. 95:3415–25
    [Google Scholar]
  110. Liem EIML, Frens MA, Smits M, van der Geest JN 2013. Cerebellar activation related to saccadic inaccuracies. Cerebellum 12:2224–35
    [Google Scholar]
  111. Liu D, Todorov E. 2007. Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. J. Neurosci. 27:359354–68
    [Google Scholar]
  112. Logan CG, Grafton ST. 1995. Functional anatomy of human eyeblink conditioning determined with regional cerebral glucose metabolism and positron-emission tomography. PNAS 92:167500–4
    [Google Scholar]
  113. Marinovic W, Poh E, de Rugy A, Carroll TJ 2017. Action history influences subsequent movement via two distinct processes. eLife Sci 6:e26713
    [Google Scholar]
  114. Marko MK, Haith AM, Harran MD, Shadmehr R 2012. Sensitivity to prediction error in reach adaptation. J. Neurophysiol. 108:61752–63
    [Google Scholar]
  115. Marsden CD, Merton PA, Morton HB 1981. Human postural responses. Brain 104:3513–34
    [Google Scholar]
  116. Martin TA, Keating JG, Goodkin HP, Bastian AJ, Thach WT 1996. Throwing while looking through prisms. II. Specificity and storage of multiple gaze–throw calibrations. Brain 119:41199–211
    [Google Scholar]
  117. Mathis A, Mamidanna P, Cury KM, Abe T, Murthy VN et al. 2018. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21:91281–89
    [Google Scholar]
  118. Mawase F, Lopez D, Celnik PA, Haith AM 2018. Movement repetition facilitates response preparation. Cell Rep 24:4801–8
    [Google Scholar]
  119. Mawase F, Uehara S, Bastian AJ, Celnik P 2017. Motor learning enhances use-dependent plasticity. J. Neurosci. 37:102673–85
    [Google Scholar]
  120. Mazzoni P, Krakauer JW. 2006. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 26:143642–45
    [Google Scholar]
  121. McDougle SD, Bond KM, Taylor JA 2015. Explicit and implicit processes constitute the fast and slow processes of sensorimotor learning. J. Neurosci. 35:269568–79
    [Google Scholar]
  122. McDougle SD, Bond KM, Taylor JA 2017. Implications of plan-based generalization in sensorimotor adaptation. J. Neurophysiol. 118:1383–93
    [Google Scholar]
  123. McDougle SD, Taylor JA. 2019. Dissociable cognitive strategies for sensorimotor learning. Nat. Commun. 10:40
    [Google Scholar]
  124. McKinstry C, Dale R, Spivey MJ 2008. Action dynamics reveal parallel competition in decision making. Psychol. Sci. 19:122–24
    [Google Scholar]
  125. Mechsner F, Kerzel D, Knoblich G, Prinz W 2001. Perceptual basis of bimanual coordination. Nature 414:685969–73
    [Google Scholar]
  126. Miall RC, Christensen LOD, Cain O, Stanley J 2007. Disruption of state estimation in the human lateral cerebellum. PLOS Biol 5:11e316
    [Google Scholar]
  127. Miall RC, Jenkinson N, Kulkarni K 2004. Adaptation to rotated visual feedback: a re-examination of motor interference. Exp. Brain Res. 154:2201–10
    [Google Scholar]
  128. Miall RC, Weir DJ, Wolpert DM, Stein JF 1993. Is the cerebellum a Smith predictor. J. Motor Behav. 25:3203–16
    [Google Scholar]
  129. Miyamoto YR, Wang S, Smith MA 2020. Implicit adaptation compensates for erratic explicit strategy in human motor learning. Nat. Neurosci. 23:443–55
    [Google Scholar]
  130. Morasso P. 1981. Spatial control of arm movements. Exp. Brain Res. 42:2223–27
    [Google Scholar]
  131. Morehead JR, Qasim SE, Crossley MJ, Ivry RB 2015. Savings upon re-aiming in visuomotor adaptation. J. Neurosci. 35:4214386–96
    [Google Scholar]
  132. Morehead JR, Taylor JA, Parvin DE, Ivry RB 2017. Characteristics of implicit sensorimotor adaptation revealed by task-irrelevant clamped feedback. J. Cogn. Neurosci. 29:61061–74
    [Google Scholar]
  133. Morton SM, Bastian AJ. 2006. Cerebellar contributions to locomotor adaptations during splitbelt treadmill walking. J. Neurosci. 26:369107–16
    [Google Scholar]
  134. Nashed JY, Crevecoeur F, Scott SH 2014. Rapid online selection between multiple motor plans. J. Neurosci. 34:51769–80
    [Google Scholar]
  135. Nezafat R, Shadmehr R, Holcomb HH 2001. Long-term adaptation to dynamics of reaching movements: a PET study. Exp. Brain Res. 140:166–76
    [Google Scholar]
  136. Nielsen JB. 2004. Sensorimotor integration at spinal level as a basis for muscle coordination during voluntary movement in humans. J. Appl. Physiol. 96:51961–67
    [Google Scholar]
  137. Nikooyan AA, Ahmed AA. 2015. Reward feedback accelerates motor learning. J. Neurophysiol. 113:2633–46
    [Google Scholar]
  138. Oliveira FTP, Diedrichsen J, Verstynen T, Duque J, Ivry RB 2010. Transcranial magnetic stimulation of posterior parietal cortex affects decisions of hand choice. PNAS 107:4117751–56
    [Google Scholar]
  139. Oostwoud Wijdenes L, Ivry RB, Bays PM 2016. Competition between movement plans increases motor variability: evidence of a shared resource for movement planning. J. Neurophysiol. 116:31295–303
    [Google Scholar]
  140. Padoa-Schioppa C. 2011. Neurobiology of economic choice: a good-based model. Annu. Rev. Neurosci. 34:333–59
    [Google Scholar]
  141. Panouillères M, Alahyane N, Urquizar C, Salemme R, Nighoghossian N et al. 2013. Effects of structural and functional cerebellar lesions on sensorimotor adaptation of saccades. Exp. Brain Res. 231:11–11
    [Google Scholar]
  142. Parvin DE, McDougle SD, Taylor JA, Ivry RB 2018. Credit assignment in a motor decision making task is influenced by agency and not sensory prediction errors. J. Neurosci. 38:194521–30
    [Google Scholar]
  143. Pearson TS, Krakauer JW, Mazzoni P 2010. Learning not to generalize: modular adaptation of visuomotor gain. J. Neurophysiol. 103:62938–52
    [Google Scholar]
  144. Poh E, Carroll TJ, Taylor JA 2016. Effect of coordinate frame compatibility on the transfer of implicit and explicit learning across limbs. J. Neurophysiol. 116:31239–49
    [Google Scholar]
  145. Poh E, Taylor JA. 2019. Generalization via superposition: combined effects of mixed reference frame representations for explicit and implicit learning in a visuomotor adaptation task. J. Neurophysiol. 121:51953–66
    [Google Scholar]
  146. Polit A, Bizzi E. 1979. Characteristics of motor programs underlying arm movements in monkeys. J. Neurophysiol. 42:1183–94
    [Google Scholar]
  147. Pruszynski JA, Kurtzer I, Lillicrap TP, Scott SH 2009. Temporal evolution of “automatic gain-scaling.”. J. Neurophysiol. 102:2992–1003
    [Google Scholar]
  148. Pruszynski JA, Scott SH. 2012. Optimal feedback control and the long-latency stretch response. Exp. Brain Res. 218:3341–59
    [Google Scholar]
  149. Redding GM, Wallace B. 1978. Sources of “overadditivity” in prism adaptation. Percept. Psychophys. 24:158–62
    [Google Scholar]
  150. Reichenbach A, Franklin DW, Zatka-Haas P, Diedrichsen J 2014. A dedicated binding mechanism for the visual control of movement. Curr. Biol. 24:7780–85
    [Google Scholar]
  151. Reichenthal M, Avraham G, Karniel A, Shmuelof L 2016. Target size matters: target errors contribute to the generalization of implicit visuomotor learning. J. Neurophysiol. 116:2411–24
    [Google Scholar]
  152. Roemmich RT, Long AW, Bastian AJ 2016. Seeing the errors you feel enhances locomotor performance but not learning. Curr. Biol. 26:202707–16
    [Google Scholar]
  153. Rosenbaum DA, Dawson AM, Challis JH 2006. Haptic tracking permits bimanual independence. J. Exp. Psychol. Hum. Percept. Perform. 32:51266–75
    [Google Scholar]
  154. Sainburg RL, Lateiner JE, Latash ML, Bagesteiro LB 2003. Effects of altering initial position on movement direction and extent. J. Neurophysiol. 89:1401–15
    [Google Scholar]
  155. Sainburg RL, Wang J. 2002. Interlimb transfer of visuomotor rotations: independence of direction and final position information. Exp. Brain Res. 145:4437–47
    [Google Scholar]
  156. Scheidt RA, Reinkensmeyer DJ, Conditt MA, Rymer WZ, Mussa-Ivaldi FA 2000. Persistence of motor adaptation during constrained, multi-joint, arm movements. J. Neurophysiol. 84:2853–62
    [Google Scholar]
  157. Schlerf JE, Galea JM, Bastian AJ, Celnik PA 2012. Dynamic modulation of cerebellar excitability for abrupt, but not gradual, visuomotor adaptation. J. Neurosci. 32:3411610–17
    [Google Scholar]
  158. Schmidt RA, Zelaznik H, Hawkins B, Frank JS, Quinn JTJr 1979. Motor-output variability: a theory for the accuracy of rapid motor acts. Psychol. Rev. 86:5415–51
    [Google Scholar]
  159. Schween R, McDougle SD, Hegele M, Taylor JA 2020. Assessing explicit strategies in force field adaptation. J. Neurophysiol. 123:41552–65
    [Google Scholar]
  160. Scott SH. 2004. Optimal feedback control and the neural basis of volitional motor control. Nat. Rev. Neurosci. 5:7532–46
    [Google Scholar]
  161. Scott SH, Cluff T, Lowrey CR, Takei T 2015. Feedback control during voluntary motor actions. Curr. Opin. Neurobiol. 33:85–94
    [Google Scholar]
  162. Selen LPJ, Shadlen MN, Wolpert DM 2012. Deliberation in the motor system: Reflex gains track evolving evidence leading to a decision. J. Neurosci. 32:72276–86
    [Google Scholar]
  163. Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP 2002. Instant neural control of a movement signal. Nature 416:6877141–42
    [Google Scholar]
  164. Shadmehr R, Holcomb HH. 1997. Neural correlates of motor memory consolidation. Science 277:5327821–25
    [Google Scholar]
  165. Shadmehr R, Huang HJ, Ahmed AA 2016. A representation of effort in decision-making and motor control. Curr. Biol. 26:141929–34
    [Google Scholar]
  166. Shadmehr R, Krakauer JW. 2008. A computational neuroanatomy for motor control. Exp. Brain Res. 185:3359–81
    [Google Scholar]
  167. Shadmehr R, Moussavi ZMK. 2000. Spatial generalization from learning dynamics of reaching movements. J. Neurosci. 20:207807–15
    [Google Scholar]
  168. Shadmehr R, Mussa-Ivaldi FA. 1994. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14:53208–24
    [Google Scholar]
  169. Shadmehr R, Smith MA, Krakauer JW 2010. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33:89–108
    [Google Scholar]
  170. Shadmehr R, Wise SP. 2005. The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning Cambridge, MA: MIT Press
  171. Sheahan HR, Franklin DW, Wolpert DM 2016. Motor planning, not execution, separates motor memories. Neuron 92:4773–79
    [Google Scholar]
  172. Shenoy KV, Sahani M, Churchland MM 2013. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36:337–59
    [Google Scholar]
  173. Shepard RN, Metzler J. 1971. Mental rotation of three-dimensional objects. Science 171:3972701–3
    [Google Scholar]
  174. Shmuelof L, Huang VS, Haith AM, Delnicki RJ, Mazzoni P, Krakauer JW 2012a. Overcoming motor “forgetting” through reinforcement of learned actions. J. Neurosci. 32:4214617–21
    [Google Scholar]
  175. Shmuelof L, Krakauer JW, Mazzoni P 2012b. How is a motor skill learned? Change and invariance at the levels of task success and trajectory control. J. Neurophysiol. 108:2578–94
    [Google Scholar]
  176. Sing GC, Smith MA. 2010. Reduction in learning rates associated with anterograde interference results from interactions between different timescales in motor adaptation. PLOS Comput. Biol. 6:8e1000893
    [Google Scholar]
  177. Smith MA, Ghazizadeh A, Shadmehr R 2006. Interacting adaptive processes with different timescales underlie short-term motor learning. PLOS Biol 4:6e179
    [Google Scholar]
  178. Smith MA, Shadmehr R. 2005. Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration. J. Neurophysiol. 93:52809–21
    [Google Scholar]
  179. Sober SJ, Sabes PN. 2003. Multisensory integration during motor planning. J. Neurosci. 23:186982–92
    [Google Scholar]
  180. Soechting JF, Flanders M. 1989. Errors in pointing are due to approximations in sensorimotor transformations. J. Neurophysiol. 62:2595–608
    [Google Scholar]
  181. Song J-H, Nakayama K. 2009. Hidden cognitive states revealed in choice reaching tasks. Trends Cogn. Sci. 13:8360–66
    [Google Scholar]
  182. Spivey MJ, Grosjean M, Knoblich G 2005. Continuous attraction toward phonological competitors. PNAS 102:2910393–98
    [Google Scholar]
  183. Stark-Inbar A, Raza M, Taylor JA, Ivry RB 2016. Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning. J. Neurophysiol. 117:1412–28
    [Google Scholar]
  184. Stewart BM, Baugh LA, Gallivan JP, Flanagan JR 2013. Simultaneous encoding of the direction and orientation of potential targets during reach planning: evidence of multiple competing reach plans. J. Neurophysiol. 110:4807–16
    [Google Scholar]
  185. Stewart BM, Gallivan JP, Baugh LA, Flanagan JR 2014. Motor, not visual, encoding of potential reach targets. Curr. Biol. 24:19R953–54
    [Google Scholar]
  186. Stratton GM. 1896. Some preliminary experiments on vision without inversion of the retinal image. Psychol. Rev. 3:6611–17
    [Google Scholar]
  187. Taylor JA, Ivry RB. 2011. Flexible cognitive strategies during motor learning. PLOS Comput. Biol. 7:3e1001096
    [Google Scholar]
  188. Taylor JA, Krakauer JW, Ivry RB 2014. Explicit and implicit contributions to learning in a sensorimotor adaptation task. J. Neurosci. 34:83023–32
    [Google Scholar]
  189. Taylor JA, Wojaczynski GJ, Ivry RB 2011. Trial-by-trial analysis of intermanual transfer during visuomotor adaptation. J. Neurophysiol. 106:63157–72
    [Google Scholar]
  190. Telgen S, Parvin DE, Diedrichsen J 2014. Mirror reversal and visual rotation are learned and consolidated via separate mechanisms: recalibrating or learning de novo. J. Neurosci. 34:4113768–79
    [Google Scholar]
  191. Thaler L, Todd JT. 2009. The control parameters used by the CNS to guide the hand depend on the visuo-motor task: evidence from visually guided pointing. Neuroscience 159:2578–98
    [Google Scholar]
  192. Therrien AS, Wolpert DM, Bastian AJ 2018. Increasing motor noise impairs reinforcement learning in healthy individuals. eNeuro 5:3ENEURO.0050–18.2018
    [Google Scholar]
  193. Thoroughman KA, Shadmehr R. 1999. Electromyographic correlates of learning an internal model of reaching movements. J. Neurosci. 19:198573–88
    [Google Scholar]
  194. Thoroughman KA, Shadmehr R. 2000. Learning of action through adaptive combination of motor primitives. Nature 407:6805742–47
    [Google Scholar]
  195. Todorov E, Jordan MI. 2002. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5:111226–35
    [Google Scholar]
  196. Tseng YW, Diedrichsen J, Krakauer JW, Shadmehr R, Bastian AJ 2007. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. J. Neurophysiol. 98:154–62
    [Google Scholar]
  197. van Beers RJ, Haggard P, Wolpert DM 2004. The role of execution noise in movement variability. J. Neurophysiol. 91:21050–63
    [Google Scholar]
  198. van Broekhoven PCA, Schraa-Tam CKL, van der Lugt A, Smits M, Frens MA, van der Geest JN 2009. Cerebellar contributions to the processing of saccadic errors. Cerebellum 8:403–15
    [Google Scholar]
  199. van den Berg R, Anandalingam K, Zylberberg A, Kiani R, Shadlen MN, Wolpert DM 2016. A common mechanism underlies changes of mind about decisions and confidence. eLife 5:e12192
    [Google Scholar]
  200. van den Dobbelsteen JJ, Brenner E, van Smeets JBJ 2001. Endpoints of arm movements to visual targets. Exp. Brain Res. 138:3279–87
    [Google Scholar]
  201. van der Kooij K, Brenner E, van Beers RJ, Smeets JBJ 2015. Visuomotor adaptation: how forgetting keeps us conservative. PLOS ONE 10:2e0117901
    [Google Scholar]
  202. Vandevoorde K, Orban de Xivry J-J 2019. Internal model recalibration does not deteriorate with age while motor adaptation does. Neurobiol. Aging 80:138–53
    [Google Scholar]
  203. Vaswani PA, Shmuelof L, Haith AM, Delnicki RJ, Huang VS et al. 2015. Persistent residual errors in motor adaptation tasks: reversion to baseline and exploratory escape. J. Neurosci. 35:176969–77
    [Google Scholar]
  204. Verbruggen F, Aron AR, Band GP, Beste C, Bissett PG et al. 2019. A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife 8:e46323
    [Google Scholar]
  205. Verstynen T, Sabes PN. 2011. How each movement changes the next: an experimental and theoretical study of fast adaptive priors in reaching. J. Neurosci. 31:2710050–59
    [Google Scholar]
  206. Vindras P, Desmurget M, Prablanc C, Viviani P 1998. Pointing errors reflect biases in the perception of the initial hand position. J. Neurophysiol. 79:63290–94
    [Google Scholar]
  207. Vindras P, Desmurget M, Viviani P 2005. Error parsing in visuomotor pointing reveals independent processing of amplitude and direction. J. Neurophysiol. 94:21212–24
    [Google Scholar]
  208. von Helmholtz H. 1867. Handbuch der physiologischen Optik 9 Leipzig, Ger: Voss
  209. Wei K, Körding K. 2009. Relevance of error: What drives motor adaptation. J. Neurophysiol. 101:2655–64
    [Google Scholar]
  210. Welch RB. 1974. Research on adaptation to rearranged vision: 1966–1974. Perception 3:4367–92
    [Google Scholar]
  211. Werner S, Schorn CF, Bock O, Theysohn N, Timmann D 2014. Neural correlates of adaptation to gradual and to sudden visuomotor distortions in humans. Exp. Brain Res. 232:41145–56
    [Google Scholar]
  212. Werner S, Strüder HK, Donchin O 2019. Intermanual transfer of visuomotor adaptation is related to awareness. PLOS ONE 14:9e0220748
    [Google Scholar]
  213. Werner S, van Aken BC, Hulst T, Frens MA, van der Geest JN et al. 2015. Awareness of sensorimotor adaptation to visual rotations of different size. PLOS ONE 10:4e0123321
    [Google Scholar]
  214. Wispinski NJ, Gallivan JP, Chapman CS 2018. Models, movements, and minds: bridging the gap between decision making and action. Ann. N. Y. Acad. Sci. 1464:130–51
    [Google Scholar]
  215. Wolpert DM, Ghahramani Z, Jordan MI 1995. An internal model for sensorimotor integration. Science 269:52321880–82
    [Google Scholar]
  216. Wolpert DM, Miall RC, Kawato M 1998. Internal models in the cerebellum. Trends Cogn. Sci. 2:9338–47
    [Google Scholar]
  217. Wong AL, Haith AM. 2017. Motor planning flexibly optimizes performance under uncertainty about task goals. Nat. Commun. 8:14624
    [Google Scholar]
  218. Wong AL, Marvel CL, Taylor JA, Krakauer JW 2019. Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits. Brain 142:3662–73
    [Google Scholar]
  219. Wu HG, Miyamoto YR, Castro LNG, Ölveczky BP, Smith MA 2014. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat. Neurosci. 17:2312–21
    [Google Scholar]
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