Sequence, time, or state representation: how does the motor control system adapt to variable environments?

Abstract. Does the observation of well-timed movements imply the existence of some internal representation of time, such as a hypothetical neural clock? Here we report the results of experiments designed to investigate whether subjects form a correct adaptive representation of mechanical environments that change in a very predictable manner. In these experiments, subjects were asked to execute arm movements over a two-dimensional workspace while experiencing time-dependent disturbing forces. We provide a formal definition for time representation and conclude that our subjects didn't use time representation for motor adaptation under the tested conditions.Subjects performed arm-reaching movements in the following experiments: (1) six experiments in a sinusoidal time-varying force field; (2) six experiments in a simple sequence of alternating viscous force fields, in which the number of targets allowed for the approximation of the force by a complex state-dependent force field; and (3) six experiments in the same simple sequence of alternating viscous force fields, in which no state-dependent force field approximation was possible. We found that the subjects did not adapt to the time-varying force field and were unable to form an adequate representation of the simple sequence of force fields. In the latter case, whenever possible, they adapted to a single state-dependent field that produced forces similar to the two alternating fields. This state-dependent field produced the same forces as the applied sequence of fields only over the trajectories that subjects executed during the training phase. However, the state-dependent field was inadequate to produce the correct forces generated by the field sequence over a new set of trajectories.These results are not consistent with the hypothesis that subjects would develop a correct representation of time-dependent forces, at least under the tested circumstances. We speculate that the system responsible for adaptation of movements to external forces may be unable to employ temporal representation. While it is possible that such a representation may emerge in a more prolonged and/or intense training, our findings indicate a preference by the adaptive system to generalize based on representing dependence of external forces upon state rather than upon time.

[1]  J. Kalaska,et al.  Context-dependent anticipation of different task dynamics: rapid recall of appropriate motor skills using visual cues. , 2003, Journal of neurophysiology.

[2]  J. Houk,et al.  Model of cortical-basal ganglionic processing: encoding the serial order of sensory events. , 1998, Journal of neurophysiology.

[3]  Richard A. Block,et al.  Cognitive models of psychological time , 1992 .

[4]  Gideon F. Inbar,et al.  Human motor control: learning to control a time-varying, nonlinear, many-to-one system , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[5]  W. T. Thach,et al.  No clock signal in the discharge of neurons in the deep cerebellar nuclei. , 1997, Journal of neurophysiology.

[6]  A G Feldman,et al.  One-trial adaptation of movement to changes in load. , 1996, Journal of neurophysiology.

[7]  R A Scheidt,et al.  Persistence of motor adaptation during constrained, multi-joint, arm movements. , 2000, Journal of neurophysiology.

[8]  A. Friederici,et al.  Time Perception and Motor Timing: A Common Cortical and Subcortical Basis Revealed by fMRI , 2000, NeuroImage.

[9]  T. Flash,et al.  Models of Motor Adaptation and Impedance Control in Human Arm Movements , 1997 .

[10]  K G Pearson,et al.  Neural adaptation in the generation of rhythmic behavior. , 2000, Annual review of physiology.

[11]  G F Inbar,et al.  Parameter and signal adaptation in the stretch reflex loop. , 1976, Progress in brain research.

[12]  F. Mussa-Ivaldi,et al.  The motor system does not learn the dynamics of the arm by rote memorization of past experience. , 1997, Journal of neurophysiology.

[13]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[14]  Justin A. Harris,et al.  The Topography of Tactile Learning in Humans , 2001, The Journal of Neuroscience.

[15]  M M Merzenich,et al.  Temporal information transformed into a spatial code by a neural network with realistic properties , 1995, Science.

[16]  J. Lackner,et al.  Gravitoinertial force background level affects adaptation to coriolis force perturbations of reaching movements. , 1998, Journal of neurophysiology.

[17]  R. Ivry.  The representation of temporal information in perception and motor control , 1996, Current Opinion in Neurobiology.

[18]  Stefan Schaal,et al.  Interaction of rhythmic and discrete pattern generators in single-joint movements , 2000 .

[19]  E. Bizzi,et al.  Consolidation in human motor memory , 1996, Nature.

[20]  E Bizzi,et al.  Motor learning by field approximation. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Dagmar Sternad,et al.  Task-effector asymmetries in a rhythmic continuation task. , 2003, Journal of experimental psychology. Human perception and performance.

[22]  J. Ashe,et al.  Cerebellum Activation Associated with Performance Change but Not Motor Learning , 2002, Science.

[23]  A. Winfree.  The geometry of biological time , 1991 .

[24]  Gerald L. Gottlieb,et al.  The generation of the efferent command and the importance of joint compliance in fast elbow movements , 2004, Experimental Brain Research.

[25]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[26]  A. Reber.  Implicit learning and tacit knowledge , 1993 .

[27]  Ferdinando A. Mussa-Ivaldi,et al.  Robots can teach people how to move their arm , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[28]  R. Llinás,et al.  Dynamic organization of motor control within the olivocerebellar system , 1995, Nature.

[29]  Christopher D Mah,et al.  Manipulating objects with internal degrees of freedom: evidence for model-based control. , 2002, Journal of neurophysiology.

[30]  Peter Ford Dominey,et al.  Dissociable Processes for Learning the Surface Structure and Abstract Structure of Sensorimotor Sequences , 1998, Journal of Cognitive Neuroscience.

[31]  P. Morasso.  Spatial control of arm movements , 2004, Experimental Brain Research.

[32]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[33]  Reza Shadmehr,et al.  Learning of action through adaptive combination of motor primitives , 2000, Nature.

[34]  J R Flanagan,et al.  The Role of Internal Models in Motion Planning and Control: Evidence from Grip Force Adjustments during Movements of Hand-Held Loads , 1997, The Journal of Neuroscience.

[35]  R. Shadmehr,et al.  Motor disorder in Huntington's disease begins as a dysfunction in error feedback control , 2000, Nature.

[36]  Zoubin Ghahramani,et al.  Adaptation to switching force fields , 2001 .

[37]  Reza Shadmehr,et al.  Computational nature of human adaptive control during learning of reaching movements in force fields , 1999, Biological Cybernetics.

[38]  J R Flanagan,et al.  Composition and Decomposition of Internal Models in Motor Learning under Altered Kinematic and Dynamic Environments , 1999, The Journal of Neuroscience.

[39]  S Hocherman,et al.  Modality-Specific Effects on Discrimination of Short Empty Time Intervals , 1979, Perceptual and motor skills.

[40]  W. Meck.  Neuropharmacology of timing and time perception. , 1996, Brain research. Cognitive brain research.

[41]  G. Berns,et al.  Brain regions responsive to novelty in the absence of awareness. , 1997, Science.

[42]  G. Kraepelin,et al.  A. T. Winfree, The Geometry of Biological Time (Biomathematics, Vol.8). 530 S., 290 Abb. Berlin‐Heidelberg‐New‐York 1980. Springer‐Verlag. DM 59,50 , 1981 .

[43]  N. A. Bernshteĭn.  The co-ordination and regulation of movements , 1967 .

[44]  Gideon F. Inbar,et al.  Movement-related potentials during the performance of a motor task II: Cerebral areas activated during learning of the task , 2001, Biological Cybernetics.

[45]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[46]  E Bizzi,et al.  Motor learning through the combination of primitives. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[47]  David J. Ostry,et al.  Compensation for loads during arm movements using equilibrium-point control , 2000, Experimental Brain Research.

[48]  C. Palmer.  Music performance. , 1997, Annual review of psychology.

[49]  F A Mussa-Ivaldi,et al.  Central representation of time during motor learning. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[50]  Richard I. Ivry,et al.  Attention and structure in sequence learning. , 1990 .