During reaching movements, the brain's internal models map desired limb motion into predicted forces. When the forces in the task change, these models adapt. Adaptation is guided by generalization: errors in one movement influence prediction in other types of movement. If the mapping is accomplished with population coding, combining basis elements that encode different regions of movement space, then generalization can reveal the encoding of the basis elements. We present a theory that relates encoding to generalization using trial-by-trial changes in behavior during adaptation. We consider adaptation during reaching movements in various velocity-dependent force fields and quantify how errors generalize across direction. We find that the measurement of error is critical to the theory. A typical assumption in motor control is that error is the difference between a current trajectory and a desired trajectory (DJ) that does not change during adaptation. Under this assumption, in all force fields that we examined, including one in which force randomly changes from trial to trial, we found a bimodal generalization pattern, perhaps reflecting basis elements that encode direction bimodally. If the DJ was allowed to vary, bimodality was reduced or eliminated, but the generalization function accounted for nearly twice as much variance. We suggest, therefore, that basis elements representing the internal model of dynamics are sensitive to limb velocity with bimodal tuning; however, it is also possible that during adaptation the error metric itself adapts, which affects the implied shape of the basis elements.

Adaptation is sometimes viewed as a process in which the nervous system learns to predict and cancel effects of a novel environment, returning movements to near baseline (unperturbed) conditions. An alternate view is that cancellation is not the goal of adaptation. Rather, the goal is to maximize performance in that environment. If performance criteria are well defined, theory allows one to predict the reoptimized trajectory. For example, if velocity-dependent forces perturb the hand perpendicular to the direction of a reaching movement, the best reach plan is not a straight line but a curved path that appears to overcompensate for the forces. If this environment is stochastic (changing from trial to trial), the reoptimized plan should take into account this uncertainty, removing the overcompensation. If the stochastic environment is zero-mean, peak velocities should increase to allow for more time to approach the target. Finally, if one is reaching through a via-point, the optimum plan in a zero-mean deterministic environment is a smooth movement but in a zero-mean stochastic environment is a segmented movement. We observed all of these tendencies in how people adapt to novel environments. Therefore, motor control in a novel environment is not a process of perturbation cancellation. Rather, the process resembles reoptimization: through practice in the novel environment, we learn internal models that predict sensory consequences of motor commands. Through reward-based optimization, we use the internal model to search for a better movement plan to minimize implicit motor costs and maximize rewards.

Previous studies on sensorimotor adaptation revealed no awareness of the nature of the perturbation after adaptation to an abrupt 30° rotation of visual feedback or after adaptation to gradually introduced perturbations. Whether the degree of awareness depends on the magnitude of the perturbation, though, has as yet not been tested. Instead of using questionnaires, as was often done in previous work, the present study used a process dissociation procedure to measure awareness and unawareness. A naïve, implicit group and a group of subjects using explicit strategies adapted to 20°, 40° and 60° cursor rotations in different adaptation blocks that were each followed by determination of awareness and unawareness indices. The awareness index differed between groups and increased from 20° to 60° adaptation. In contrast, there was no group difference for the unawareness index, but it also depended on the size of the rotation. Early adaptation varied between groups and correlated with awareness: The more awareness a participant had developed the more the person adapted in the beginning of the adaptation block. In addition, there was a significant group difference for savings but it did not correlate with awareness. Our findings suggest that awareness depends on perturbation size and that aware and strategic processes are differentially involved during adaptation and savings. Moreover, the use of the process dissociation procedure opens the opportunity to determine awareness and unawareness indices in future sensorimotor adaptation research.

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.

Many voluntary movements involve coordination between the limbs. However, there have been very few attempts to study the neuronal mechanisms that mediate this coordination. Here we have studied the activity of cortical neurons while monkeys performed tasks that required coordination between the two arms. We found that most neurons in the primary motor cortex (MI) show activity specific to bimanual movements (bimanual-related activity), which is strikingly different from the activity of the same neurons during unimanual movements. Moreover, units in the supplementary motor area (SMA; the area of cortex most often associated with bimanual coordination) showed no more bimanual-related activity than units in MI. Our results challenge the classic view that MI controls the contralateral (opposite) side of the body and that SMA is responsible for the coordination of the arms. Rather, our data suggest that both cortical areas share the control of bilateral coordination.

Although it is widely agreed that the cerebellum is necessary for learning and consolidation of new motor tasks, it is not known whether adaptation to kinematic and dynamic errors is processed by the same cerebellar areas or whether different parts play a decisive role. We investigated arm movements in a visuomotor (VM) rotation and a force field (FF) perturbation task in 14 participants with cerebellar degeneration and 14 age- and gender-matched controls. Magnetic resonance images were used to calculate the volume of cerebellar areas (medial, intermediate, and lateral zones of the anterior and posterior lobes) and to identify cerebellar structure important for the two tasks. Corroborating previous studies, cerebellar participants showed deficits in adaptation to both tasks compared with controls (P < 0.001). However, it was not possible to draw conclusions from the performance in one task on the performance in the other task because an individual participant could show severe impairment in one task and perform relatively well in the other (rho = 0.1; P = 0.73). We found that atrophy of distinct cerebellar areas correlated with impairment in different tasks. Whereas atrophy of the intermediate and lateral zone of the anterior lobe correlated with impairment in the FF task (rho = 0.72, 0.70; P = 0.003, 0.005, respectively), atrophy of the intermediate zone of the posterior lobe correlated with adaptation deficits in the VM task (rho = 0.64; P = 0.015). Our results suggest that adaptation to the different tasks is processed independently and relies on different cerebellar structures.

Studies with patients and functional magnetic resonance imaging investigations have demonstrated that the cerebellum plays an essential role in adaptation to visuomotor rotation and force field perturbation. To identify cerebellar structures involved in the two tasks, we studied 19 patients with focal lesions after cerebellar infarction. Focal lesions were manually traced on magnetic resonance images and normalized using a new spatially unbiased template of the cerebellum. In addition, we reanalyzed data from 14 patients with cerebellar degeneration using voxel-based morphometry. We found that adjacent regions with only little overlap in the anterior arm area (lobules IV to VI) are important for adaptation in both tasks. Although adaptation to the force field task lay more anteriorly (lobules IV and V), lobule VI was more important for the visuomotor task. In addition, regions in the posterolateral cerebellum (Crus I and II) contributed to both tasks. No consistent involvement of the posterior arm region (lobule VIII) was found. Independence of the two kinds of adaptation is further supported by findings that performance in one task did not correlate to performance in the other task. Our results show that the anterior arm area of the cerebellum is functionally divided into a more posterior part of lobule VI, extending into lobule V, related to visuomotor adaption, and a more anterior part including lobules IV and V, related to force field adaption. The posterolateral cerebellum may process common aspects of both tasks.

Autism spectrum disorder is a developmental disorder characterized by deficits in social and communication skills and repetitive and stereotyped interests and behaviours. Although not part of the diagnostic criteria, individuals with autism experience a host of motor impairments, potentially due to abnormalities in how they learn motor control throughout development. Here, we used behavioural techniques to quantify motor learning in autism spectrum disorder, and structural brain imaging to investigate the neural basis of that learning in the cerebellum. Twenty children with autism spectrum disorder and 20 typically developing control subjects, aged 8-12, made reaching movements while holding the handle of a robotic manipulandum. In random trials the reach was perturbed, resulting in errors that were sensed through vision and proprioception. The brain learned from these errors and altered the motor commands on the subsequent reach. We measured learning from error as a function of the sensory modality of that error, and found that children with autism spectrum disorder outperformed typically developing children when learning from errors that were sensed through proprioception, but underperformed typically developing children when learning from errors that were sensed through vision. Previous work had shown that this learning depends on the integrity of a region in the anterior cerebellum. Here we found that the anterior cerebellum, extending into lobule VI, and parts of lobule VIII were smaller than normal in children with autism spectrum disorder, with a volume that was predicted by the pattern of learning from visual and proprioceptive errors. We suggest that the abnormal patterns of motor learning in children with autism spectrum disorder, showing an increased sensitivity to proprioceptive error and a decreased sensitivity to visual error, may be associated with abnormalities in the cerebellum.