When we use a novel tool, the motor commands may not produce the expected outcome. In healthy individuals, with practice the brain learns to alter the motor commands. This change depends critically on the cerebellum as damage to this structure impairs adaptation. However, it is unclear precisely what the cerebellum contributes to the process of adaptation in human motor learning. Is the cerebellum crucial for learning to associate motor commands with novel sensory consequences, called forward model, or is the cerebellum important for learning to associate sensory goals with novel motor commands, called inverse model? Here, we compared performance of cerebellar patients and healthy controls in a reaching task with a gradual perturbation schedule. This schedule allowed both groups to adapt their motor commands. Following training, we measured two kinds of behavior: in one case people were presented with reach targets near the direction in which they had trained. The resulting generalization patterns of patients and controls were similar, suggesting comparable inverse models. In another case, they reached without a target and reported the location of their hand. In controls the pattern of change in reported hand location was consistent with simulation results of a forward model that had learned to associate motor commands with new sensory consequences. In patients, this change was significantly smaller. Therefore, in our sample of patients we observed that while adaptation of motor commands can take place despite cerebellar damage, cerebellar integrity appears critical for learning to predict visual sensory consequences of motor commands.

Small errors may affect the process of learning in a fundamentally different way than large errors. For example, adapting reaching movements in response to a small perturbation produces generalization patterns that are different from large perturbations. Are distinct neural mechanisms engaged in response to large versus small errors? Here, we examined the motor learning process in patients with severe degeneration of the cerebellum. Consistent with earlier reports, we found that the patients were profoundly impaired in adapting their motor commands during reaching movements in response to large, sudden perturbations. However, when the same magnitude perturbation was imposed gradually over many trials, the patients showed marked improvements, uncovering a latent ability to learn from errors. On sudden removal of the perturbation, the patients exhibited aftereffects that persisted much longer than did those in healthy controls. That is, despite cerebellar damage, the brain maintained the ability to learn from small errors and the motor memory that resulted from this learning was strongly resistant to change. Of note was the fact that on completion of learning, the motor output of the cerebellar patients remained distinct from healthy controls in terms of its temporal characteristics. Therefore cerebellar degeneration impaired the ability to learn from large-magnitude errors, but had a lesser impact on learning from small errors. The neural basis of motor learning in response to small and large errors appears to be distinct.

Accurate performance of reaching movements depends on adaptable neural circuitry that learns to predict forces and compensate for limb dynamics. In earlier experiments, we quantified generalization from training at one arm position to another position. The generalization patterns suggested that neural elements learning to predict forces coded a limb's state in an intrinsic, muscle-like coordinate system. Here, we test the sensitivity of these elements to the other arm by quantifying inter-arm generalization. We considered two possible coordinate systems: an intrinsic (joint) representation should generalize with mirror symmetry reflecting the joint's symmetry and an extrinsic representation should preserve the task's structure in extrinsic coordinates. Both coordinate systems of generalization were compared with a naïve control group. We tested transfer in right-handed subjects both from dominant to nondominant arm (D-->ND) and vice versa (ND-->D). This led to a 2 x 3 experimental design matrix: transfer direction (D-->ND/ND-->D) by coordinate system (extrinsic, intrinsic, control). Generalization occurred only from dominant to nondominant arm and only in extrinsic coordinates. To assess the dependence of generalization on callosal inter-hemispheric communication, we tested commissurotomy patient JW. JW showed generalization from dominant to nondominant arm in extrinsic coordinates. The results suggest that when the dominant right arm is used in learning dynamics, the information could be represented in the left hemisphere with neural elements tuned to both the right arm and the left arm. In contrast, learning with the nondominant arm seems to rely on the elements in the nondominant hemisphere tuned only to movements of that arm.

Can memories be unlearned, or is unlearning a form of acquiring a new memory that competes with the old, effectively masking it? We considered motor memories that were acquired when people learned to use a novel tool. We trained people to reach with tool A and quantified recall in error-clamp trials, i.e., trials in which the memory was reactivated but error-dependent learning was minimized. We measured both the magnitude of the memory and its resistance to change. With passage of time between acquisition and reactivation (up to 24 h), memory of A slowly declined, but with reactivation remained resistant to change. After learning of tool A, brief exposure to tool B brought performance back to baseline, i.e., apparent extinction. Yet, for up to a few minutes after AϩB training, output in error-clamp trials increased from baseline to match those who had trained only in A. This spontaneous recovery and convergence demonstrated that B did not produce any unlearning of A. Rather, it masked A with a new memory that was very fragile. We tracked the memory of B as a function of time and found that within minutes it was transformed from a fragile to a more stable state. Therefore, a sudden performance error in a well-learned motor task does not produce unlearning, but rather installs a competing but fragile memory that with passage of time acquires stability. Learning not only engages processes that adapt at multiple timescales, but once practice ends, the fast states are partially transformed into slower states.

The function of the cerebellum in motor control is a long-standing puzzle because cerebellar damage is associated with both timing and coordination deficits. Timing is the ability to produce consistent intervals between movements based on an internal representation of time. Coordination, in contrast, is a state-dependent control process in which motor commands to one effector depend on the predicted state of another effector. Here we considered a task consisting of two components, an arm movement and an isometric press with the thumb. We found that when the two components temporally overlapped, the brain controlled the thumb using an estimate of the state of the arm. In contrast, when the components did not temporally overlap, the brain controlled the thumb solely based on an internal estimate of time. Using functional magnetic resonance imaging, we contrasted these two conditions and found that lobule V of the cerebellum ipsilateral to the arm movement was consistently more activated during state-dependent control. When the brain learned time-dependent control, no region of the cerebellum showed consistently increased activity compared with state-dependent control. Rather, the consistent activity associated with time-dependent control was found in language areas of the left cerebral hemisphere along the Sylvian fissure. We suggest that timing and coordination are behaviorally distinct modes of motor control and that the anterior cerebellum is a crucial node in state-dependent motor control, computing a predictive state estimate of one effector (e.g., the arm) to coordinate actions of another effector (the thumb).

During adaptation, motor commands tend to repeat as performance plateaus. It has been hypothesized that this repetition produces plasticity in the motor cortex (M1). Here, we considered a force field reaching paradigm, varied the perturbation schedule to potentially alter the amount of repetition, and quantified the interaction between disruption of M1 using transcranial magnetic stimulation (TMS) and the schedule of perturbations. In the abrupt condition (introduction of the perturbation on a single trial followed by constant perturbation), motor output adapted rapidly and was then followed by significant repetition as performance plateaued. TMS of M1 had no effect on the rapid adaptation phase but reduced adaptation at the plateau. In the intermediate condition (introduction of the perturbation over 45 trials), disruption of M1 had no effect on the phase in which motor output changed but again impaired adaptation when performance had plateaued. Finally, when the perturbation was imposed gradually (over 240 trials), the motor commands continuously changed during adaptation and never repeated, and disruption of M1 had no effect on performance. Therefore, TMS of M1 appeared to reduce adaptation of motor commands during a specific phase of learning: when motor commands tended to repeat.

When we adapt our movements to a perturbation, and then adapt to another perturbation, is the initial memory destroyed, or is it protected? Despite decades of experiments, this question remains unresolved. The confusion, in our view, is due to the fact that in every instance the approach has been to assay contents of motor memory by re-testing with the same perturbations. When performance in re-testing is the same as naïve, this is usually interpreted as the memory being destroyed. However, it is also possible that the initial memory is simply masked by the competing memory. We trained humans in a reaching task in field B, and then in field A (or washout) over an equal number of trials. To assay contents of motor memory, we used a new tool: after completion of training in A, we withheld reinforcement (i.e., reward) for a brief block of trials and then clamped movement errors to zero over a long block of trials. We found that this led to spontaneous recovery of B. That is, withholding reinforcement for the current motor output resulted in the expression of the competing memory. Therefore, adaptation followed by washout or reverse-adaptation produced competing motor memories. The protection from unlearning was unrelated to sudden changes in performance errors that might signal a contextual change, as competing memories formed even when the perturbations were introduced gradually. Rather, reinforcement appears to be a critical signal that affords protection to motor memories, and lack of reinforcement encourages retrieval of a competing memory.

Gibo TL, Criscimagna-Hemminger SE, Okamura AM, Bastian AJ. Cerebellar motor learning: are environment dynamics more important than error size?