Adaptive representation of dynamics during learning of a motor task

By Reza Shadmehr, Ferdinando A. Mussa-Ivaldi. 1994

We investigated how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented. In particular, we considered the task of making reaching movements in the presence of externally imposed forces from a mechanical environment. This environment was a force field produced by a robot manipulandum, and the subjects made reaching movements while holding the end-effector of this manipulandum. Since the force field significantly changed the dynamics of the task, subjects' initial movements in the force field were grossly distorted compared to their movements in free space. However, with practice, hand trajectories in the force field converged to a path very similar to that observed in free space. This indicated that for reaching movements, there was a kinematic plan independent of dynamical conditions. The recovery of performance within the changed mechanical environment is motor adaptation. In order to investigate the mechanism underlying this adaptation, we considered the response to the sudden removal of the field after a training phase. The resulting trajectories, named aftereffects, were approximately mirror images of those that were observed when the subjects were initially exposed to the field. This suggested that the motor controller was gradually composing a model of the force field, a model that the nervous system used to predict and compensate for the forces imposed by the environment. In order to explore the structure of the model, we investigated whether adaptation to a force field, as presented in a small region, led to aftereffects in other regions of the workspace. We found that indeed there were aftereffects in workspace regions where no exposure to the field had taken place; that is, there was transfer beyond the boundary of the training data. This observation rules out the hypothesis that the subject's model of the force field was constructed as a narrow association between visited states and experienced forces; that is, adaptation was not via composition of a look-up table. In contrast, subjects modeled the force field by a combination of computational elements whose output was broadly tuned across the motor state space. These elements formed a model that extrapolated to outside the training region in a coordinate system similar to that of the joints and muscles rather than end-point forces. This geometric property suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.

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Citations in the corpus

Learned dynamics of reaching movements generalize from dominant to nondominant arm. (2003, methodology): “The generalization patterns seen in earlier experiments suggest that this is a good approximation of what subjects actually learn (Shadmehr and Moussavi 2000; Shadmehr and Mussa-Ivaldi 1994).”
Motor learning through the combination of primitives. (2000, methodology): “In particular, Shadmehr & Mussa-Ivaldi (1994) used an instrumented manipulandum for applying a velocity-dependent ¢eld to the hand of the subjects.”
Evidence for a dynamic-dominance hypothesis of handedness (2001, background): “(Shadmehr and Mussa-Ivaldi 1994; Sainburg et al. 1995; Gandolfo et al. 1996; Shadmehr and Brashers-Krug 1997; Sainburg et al. 1999; Scheidt et al. 2000; Shadmehr and Moussavi 2000) Aftereffects have also been demonstrated following adaptation to distortion visual feedback displays (Baily 1972;…”
Persistence of motor adaptation during constrained, multi-joint, arm movements. (2000, methodology): “The robotic apparatus employed was similar to that described in detail by Mussa-Ivaldi and colleagues (Conditt et al. 1997; Shadmehr and Mussa-Ivaldi 1994).”
Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2012, background): “Among other topics, research on human motor control focuses on motor adaptation within a changing environment (e.g., Shadmehr & Mussa-Ivaldi, 1994), age-related learning (e.g., Takahashi et al., 2003), generalization and transfer of skills from one movement to another (e.g., Conditt, Gandolfo, &…”
Dealing with delays does not transfer across sensorimotor tasks. (2014, unknown): “…Hay & Pick, 1966; Kitazawa, Kimura, & Uka, 1997; Rock, Goldberg, & Mack, 1966; Sarlegna et al., 2003; Smeets et al., 2006) or by mechanically introducing new tools or movement dynamics (e.g., Conditt & Mussa-Ivaldi, 1999; Cothros, Wong, & Gribble, 2006; Shadmehr & Mussa-Ivaldi, 1994).”
Does the motor control system use multiple models and context switching to cope with a variable environment? (2002, unknown): “…the planned motion: (2) In previous studies it was shown that the internal representation can be a function of position velocity and acceleration (Shadmehr and Mussa-Ivaldi 1994; Flash and Gurevich 1997; Lackner and Dizio 1998; Sainburg et al. 1999), however it appears that it cannot be an…”
Contributions of the basal ganglia and functionally related brain structures to motor learning. (2009, unknown): “, [62,96,97,99,101], see [108] and [98] for reviews).”
Use-Dependent and Error-Based Learning of Motor Behaviors (2010, background): “…3: adaptation solution is shaped by both use-dependent and error-based learning In the third experiment, we demonstrate that parallel action of use-dependent and error-based learning determines the final so- lution during adaptation to a viscous force field (Shadmehr and Mussa-Ivaldi, 1994).”
Learning Movement Primitives (2005, background): “rable to human learning in related tasks [28].”
Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning (2006, methodology): “We studied a variant of the standard force-field adaptation paradigm [24].”
A Framework to Describe, Analyze and Generate Interactive Motor Behaviors (2012, unknown): “, [6–8]) and with each other (e.”