We investigated how human subjects adapt to forces perturbing the motion of their arms. We found that this kind of learning is based on the capacity of the central nervous system (CNS) METHODSWe tested 15 right-handed individuals, ranging in age from 18 to 35 years and with no known history of neuromotor disorders. Subjects were seated on a chair and instructed to grasp the handle of a robot manipulandum with their right hand (see Fig. 1A).They were asked to execute arm movements to targets displayed on a computer screen. They used the manipulandum to guide a cursor on the screen to the targets. Full visual feedback (target and cursor) was given during the experiment.In the first part of each experiment, subjects practiced movements until they achieved a stable performance (baseline) and the desired timing. Subsequently, the torque motors of the robot generated a programmed pattern of force perturbations. The programmed forces were proportional to the subject's hand velocity. An example of a perturbation pattern is shown in Fig. 1B, where the force exerted by the manipulandum is plotted as a function of the hand's velocity. Initially, the subjects' trajectories were highly distorted by this perturbation (Fig. 1D), but as training progressed, they recovered the baseline pattern (Fig. 1E). On random trials, no perturbation was applied, and compensatory trajectories, referred to as aftereffects, were observed (Fig. 1F).A quantitative study of the distortions in the trajectories was performed by analyzing the velocity profiles. We defined our measure of similarity, or correlation coefficient, as the inner product between the velocity profile of the baseline trajectory and the trajectory itself. The baseline trajectories were chosen among the unperturbed trajectories as those having the highest mean correlation coefficient with the other unperturbed trajectories. The average score for the trajectories in Fig. 1C was 0.97. Each trajectory during the experiment was then compared with the typical trajectory (1). RESULTSTo estimate the generalization of motor learning, we trained subjects over a region of the workspace and tested for evidence of adaptation by observing the aftereffects inside and outside this region.We asked subjects to execute movements to targets placed as shown in Fig. 1A. Fig. 2A shows the baseline trajectories

The purpose of this study was to investigate the learning mechanisms underlying motor adaptation of arm movements to externally applied perturbing forces. We considered two alternative hypotheses. According to one, adaptation occurs through the learning of a mapping between the states (positions and velocities) visited by the arm and the forces experienced at those states. The alternative hypothesis is that adaptation occurs through the memorization of the temporal sequence of forces experienced along specific trajectories. The first mechanism corresponds to developing a model of the dynamics of the environment, whereas the second is a form of "rote learning." Both types of learning would lead to the recovery of the unperturbed performance. We have tested these hypotheses by examining how adaptation is transferred across different types of movements. Our results indicate that 1) adaptation to an externally applied force field occurs with different classes of movements including but not limited to reaching movements and 2) adaptation generalizes across different movements that visit the same regions of the external field. These findings are not compatible with the hypothesis of rote learning. Instead, they are consistent with the hypothesis that adaptation to changes in movement dynamics is achieved by a module that learns to reproduce the structure of the environmental field as an association between visited states and experienced forces, independent of the kinematics of the movements made during adaptation.

In this paper, we describe the neural changes observed in the primary motor cortex of two monkeys while they learned a new motor skill. The monkeys had to adapt their reaching movements to external forces that interfered with the execution of their arm movements. We found a sizable population of cells that changed their tuning properties during exposure to the force field. These cells took on the properties of neurons that are involved in the control of movement. Furthermore, the cells maintained the acquired activity as the monkey readapted to the no-force condition. Recent imaging studies in humans have reported the effects of motor learning in the primary motor cortex. Our results are consistent with the findings of these studies and provide evidence for single-cell plasticity in the primary motor cortex of primates.I n a number of recent studies investigators have shown that when networks of neurons are repeatedly exposed to sensorymotor associations, learning of motor tasks occurs. Primates learn a new task as the result of repeated exposures to sensory signals coming from a variety of visual and proprioceptive sources. The sensory inputs are funneled to the motor areas of the central nervous system each time a movement is produced. The current view is that learning results from a change in the internal structure of the cortical and subcortical networks brought about by sensory and recurrent signals.Presumably, the iterative sensory-motor process leads to the establishment of an internal model of the controlled dynamics through a gradual change of the synaptic strength (1) of the neurons of the cortical and subcortical motor areas. The internal model is embedded in the newly formed connectivity of a group of neurons, and the activity of these trained neurons generates the neural impulses necessary for the execution of the learned motor task. According to this view, motor learning and the control of dynamics are two facets of the same process. In this paper, we describe the cellular changes in the circuitry of the primary cortical motor area of the monkey during the acquisition of a motor skill.In the experiments described here, a key feature of the task to which the monkeys were exposed involved a change in the mechanical environment with which their hand interacted. Because of this change, the neural representation of the arm would have to develop a new model to deal with the new dynamics of the environment. In this paper, we present psychophysical evidence for the formation of this new internal model and we describe the neural changes observed in the primary motor cortex as the new model was formed. MethodsDescription of the Motor Task. Two monkeys (Macaca nemestrina) were trained to grasp the manipulandum of a 2-df, lightweight, low-friction robot with a force-torque transducer mounted on the handle. Two torque motors were mounted on the base of the robot and produced force fields upon the hand of the monkey as the animal performed reaching movements (see ref. 2 for details).During an experimental sessio...