Abstract
Within the field of motor control, there is no consensus on which kinematic and kinetic aspects of movements are planned or controlled. Perturbing goal-directed movements is a frequently used tool to answer this question. To be able to draw conclusions about motor control from kinematic responses to perturbations, a model of the periphery (i.e., the skeleton, muscle–tendon complexes, and spinal reflex circuitry) is required. The purpose of the present study was to determine to what extent such conclusions depend on the level of simplification with which the dynamical properties of the periphery are modeled. For this purpose, we simulated fast goal-directed single-joint movement with four existing types of models. We tested how three types of perturbations affected movement trajectory if motor commands remained unchanged. We found that the four types of models of the periphery showed different robustness to the perturbations, leading to different predictions on how accurate motor commands need to be, i.e., how accurate the knowledge of external conditions needs to be. This means that when interpreting kinematic responses obtained in perturbation experiments the level of error correction attributed to adaptation of motor commands depends on the type of model used to describe the periphery.
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References
Bhushan N, Shadmehr R (1999) Computational nature of human adaptive control during learning of reaching movements in force fields. Biol Cybern 81: 39–60
Bizzi E, Abend W (1983) Posture control and trajectory formation in single- and multi-joint arm movements. Adv Neurol 39: 31–45
Bizzi E, Accornero N, Chapple W, Hogan N (1982) Arm trajectory formation in monkeys. Exp Brain Res 46: 139–143
De Lussanet MHE, Smeets JBJ, Brenner E (2002) Relative damping improves linear mass–spring models of goal-directed movements. Hum Mov Sci 21: 85–100
de Vlugt E, Schouten AC, van der Helm FCT (2006) Quantification of intrinsic and reflexive properties during multijoint arm posture. J Neurosci Methods 155: 328–349
Debicki DB, Gribble PL (2004) Inter-joint coupling strategy during adaptation to novel viscous loads in human arm movement. J Neurophysiol 92: 754–765
Feldman AG (1986) Once more on the equilibrium-point hypothesis lambda-model) for motor control. J Motor Behav 18: 17–54
Gottlieb GL (2000) A test of torque-control and equilibrium-point models of motor control. Hum Mov Sci 19: 925–931
Gribble PL, Ostry DJ (2000) Compensation for loads during arm movements using equilibrium-point control. Exp Brain Res 135: 474–482
Grieve DW, Pheasant S, Cavanagh PR (1978) Prediction of gastrocnemius length from knee and ankle joint posture. In: Asmussen E, Jorgensen K (eds.) Biomechanics, vol vi-a. University Park Press, Baltimore, pp 405–412
Hinder MR, Milner TE (2003) The case for an internal dynamics model versus equilibrium point control in human movement. J Physiol Lond 549: 953–963
Hogan N (1984) Adaptive-control of mechanical impedance by coactivation of antagonist muscles. IEEE Trans Autom Control 29: 681–690
Izawa J, Rane T, Donchin O, Shadmehr R (2008) Motor adaptation as a process of reoptimization. J Neurosci 28: 2883–2891
Kawato M (1999) Internal models for motor control and trajectory planning. Curr Opin Neurol 9: 718–727
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: 275–288
Kistemaker DA, Rozendaal LA (2011) In vivo dynamics of the musculoskeletal system cannot be adequately described using a stiffness–damping–inertia model. PloS One 6: 2011
Kistemaker DA, van Soest AJ, Bobbert MF (2006) Is equilibrium point control feasible for fast goal-directed single-joint movements. J Neurophysiol 95: 2898–2912
Kistemaker DA, van Soest , Bobbert MF (2007) A model of open-loop control of equilibrium position and stiffness of the human elbow joint. Biol Cybern 96: 341–350
Kistemaker DA, Wong JD, Gribble PL (2010) The central nervous system does not minimize energy cost in arm movements. J Neurophysiol 104: 2985–2994
Kurtzer I, DiZio PA, Lackner JR (2005) Adaptation to a novel multi-force environment. Exp Brain Res 164: 120–132
Lacquaniti F, Carrozzo M, Borghese NA (1993) Time-varying mechanical-behavior of multijointed arm in man. J Neurophysiol 69: 1443–1464
Mussa-Ivaldi FA, Hogan N, Bizzi E (1985) Neural, mechanical, and geometric factors subserving arm posture in humans. J Neurosci 5: 2732–2743
Nijhof EJ, Kouwenhoven E (2000) Simulation of multijoint arm movements. In: Winters JM, Crago PE (eds.) Biomechanics and neural control of posture and movement, vol xxii. Springer, New York, pp 363–372
Pinter IJ, Bobbert MF, van Soest AJ, Smeets BJ (2010) Isometric torque–angle relationships of the elbow flexors and extensors in the transverse plane. J Electromyogr Kinesiol 20: 923–931
Sainburg RL, Ghez C, Kalakanis D (1999) Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms. J Neurophysiol 81: 1045–1056
Scheidt RA, Ghez C (2007) Separate adaptive mechanisms for controlling trajectory and final position in reaching. J Neurophysiol 98: 3600–3613
Scheidt RA, Conditt MA, Secco EL, Mussa-Ivaldi FA et al (2005) Interaction of visual and proprioceptive feedback during adaptation of human reaching movements. J Neurophysiol 93: 3200–3213
Shadmehr R, Mussaivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14: 3208–3224
Shapiro MB, Gottlieb GL, Moore CG, Corcos DM (2002) Electromyographic responses to an unexpected load in fast voluntary movements:Descending regulation of segmental reflexes. J Neurophysiol 88: 1059–1063
Shapiro MB, Niu CXM, Poon C, David FJ, Corcos DM (2009) Proprioceptive feedback during point-to-point arm movements is tuned to the expected dynamics of the task. Exp Brain Res 195: 575–591
Smeets JBJ, Erkelens CJ, Denier van der Gon JJ (1990) Adjustments of fast goal-directed movements in response to an unexpected inertial load. Exp Brain Res 81: 303–312
Song D, Lan N, Loeb GE, Gordon J (2008) Model-based sensorimotor integration for multi-joint control:Development of a virtual arm model. Ann Biomed Eng 36: 1033–1048
Thoroughman KA, Shadmehr R (1999) Electromyographic correlates of learning an internal model of reaching movements. J Neurosci 19: 8573–8588
Uno Y, Kawato M, Suzuki R (1989) Formation and control of optimal trajectory in human multijoint arm movement—minimum torque-change model. Biol Cybern 61: 89–101
van der Burg JCE, Casius LJR, Kingma I, van Dieen JH, van Soest AJ (2005) Factors underlying the perturbation resistance of the trunk in the first part of a lifting movement. Biol Cybern 93: 54–62
van Soest AJ, Bobbert MF (1993) The contribution of muscle properties in the control of explosive movements. Biol Cybern 69: 195–204
van Soest AJ, Haenen WP, Rozendaal LA (2003) Stability of bipedal stance: the contribution of cocontraction and spindle feedback. Biol Cybern 88: 293–301
Wolpert DM, Ghahramani Z (2000) Computational principles of movement neuroscience. Nat Neurosci 3: 1212–1217
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Pinter, I.J., van Soest, A.J., Bobbert, M.F. et al. Conclusions on motor control depend on the type of model used to represent the periphery. Biol Cybern 106, 441–451 (2012). https://doi.org/10.1007/s00422-012-0505-7
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DOI: https://doi.org/10.1007/s00422-012-0505-7