Recently, Criscimagna-Hemminger et al. (2003) reported a pattern of generalization of force-field adaptation between arms that differs from the pattern that occurs across different configurations of the same arm. Although the intralimb pattern of generalization points to an intrinsic encoding of dynamics, the interlimb transfer described by these authors indicates that information about force is represented in a frame of reference external to the body. In the present study, subjects adapted to a viscous curl-field in two experimental conditions. In one condition, the field was introduced suddenly and produced clear deviations in hand paths; in the second condition, the field was introduced gradually so that at no point during the adaptation process could subjects observe or did they have to correct for a substantial kinematic error. In the first case, a pattern of interlimb transfer consistent with Criscimagna-Hemminger et al. (2003) was observed, whereas no transfer of learning between limbs occurred in the second condition. The findings suggest that there is limited transfer of fine compensatory-force adjustment between limbs. Transfer, when it does occur, may be primarily the result of a cognitive strategy that arises as a result of the sudden introduction of load and associated kinematic error.

Abs~uct:The authors develop a functional linear model in which the values at time t of a sample of curves y, ( t ) are explained in a feed-forward sense by the values of covariate curves 2 , (s) observed at times s 5 t. They give special attention to the case s f [t -6, t]. where the lag parameter 6 is estimated from the data. They use the finite element method to estimate the bivariate parameter regression function p(s, t), which is defined on the triangular domain s 5 t . They apply their model to the problem of predicting the acceleration of the lower lip during speech on the basis of electromyographical recordings from a muscle depressing the lip. They also provide simulation results to guide the calibration of the fitting process.Le rnodhle lineaire fonctionnel historique Rt?sumt? : Les auteurs dbcrivent un modble linbaire fonctionnel dans lequel les valeurs au temps t d'un Cchantillon de courbes y, ( t ) sont expliqubes par les valeurs observbes aux temps s 5 t de courbes covariables z,(s). Ils accordent une attention particulibre au cas oh s E [t -6, t ] , 6 repdsentant un parambtre de dtlai estimb ?i partir des donnbes. ns emploient la mbthode des tl6ments finis pour estimer la fonction parambtre p ( s , t) bivaribe dbfinie sur le domaine triangulaire s 5 t. Ils appliquentleur modble ? i la pdvision de courbes d'accblbration de la lbvre infbrieure d'un locuteur partir d'enregistrements blectromyographiques d'un muscle abaissant celle-ci. Ils pdsentent aussi des dsultats de simulation pouvant guider le processus de calibration intervenant dans l'ajustement du modble.

In a recent study, Tan et al. (2014a,b) showed that the increase in ␤-power typically observed after a movement above sensorimotor regions (␤-rebound) is attenuated when movement-execution errors are induced by visual perturbations. Moreover, akin to sensorimotor adaptation, the effect depended on the context in which the errors are experienced. Thus the ␤-rebound attenuation might relate to neural processes involved in trial-to-trial adaptive mechanisms. In two EEG experiments with human participants, along with the ␤-rebound, we examine ␤-activity during the preparation of reaches immediately following perturbed movements. In the first experiment, we show that both foreperiod and postmovement ␤-activities are parametrically modulated by the sizes of kinematic errors produced by unpredictable mechanical perturbations (force field) independent of their on-line corrections. In the second experiment, we contrast two types of reach errors: movement-execution errors that trigger trial-to-trial adaptive mechanisms and goal errors that do not elicit sensorimotor adaptation. Movement-execution errors were induced by mechanical or visual perturbations, whereas goal errors were caused by unexpected displacements of the target at movement initiation. Interestingly, foreperiod and postmovement ␤-activities exhibit contrasting patterns, pointing to important functional differences of their underlying neuronal activity. While both types of reach errors attenuate the postmovement ␤-rebound, only the kinematic errors that trigger trial-to-trial motor-command updates influenced ␤-activity during the foreperiod. These findings suggest that the error-related modulation of the ␤-rebound may reflect salience processing, independent of sensorimotor adaptation. In contrast, modulations in the foreperiod ␤-power might relate to the motorcommand adjustments activated after movement-execution errors are experienced.

Substantial neurophysiological evidence points to the posterior parietal cortex (PPC) as playing a key role in the coordinate transformation necessary for visually guided reaching. Our goal was to examine the role of PPC in the context of learning new dynamics of arm movements. We assessed this possibility by stimulating PPC with transcranial magnetic stimulation (TMS) while subjects learned to make reaching movements with their right hand in a velocity-dependent force field. We reasoned that, if PPC is necessary to adjust the trajectory of the arm as it interacts with a novel mechanical system, interfering with the functioning of PPC would impair adaptation. Single pulses of TMS were applied over the left PPC 40 msec after the onset of movement during adaptation. As a control, another group of subjects was stimulated over the visual cortex. During early stages of learning, the magnitude of the error (measured as the deviation of the hand paths) was similar across groups. By the end of the learning period, however, error magnitudes decreased to baseline levels for controls but remained significantly larger for the group stimulated over PPC. Our findings are consistent with a role of PPC in the adjustment of motor commands necessary for adapting to a novel mechanical environment.

It has been suggested that the learning of new dynamics occurs in intrinsic coordinates. However, it has also been suggested that elements that encode hand velocity, and hence act in an extrinsic frame of reference, play a role in the acquisition of dynamics. To reconcile claims regarding the coordinate system involved in the representation of dynamics, we have used a procedure involving the transfer of force-field learning between two workspace locations. Subjects made point-to-point movements while holding a two-link manipulandum. Subjects were first trained to make movements in a single direction at the left of the workspace. They were then tested for transfer of learning at the right of the workspace. Two groups of subjects were defined. For the subjects in group j, movements at the left and right workspace locations were matched in terms of joint displacements. For the subjects in group h, movements in the two locations had the same hand displacements. Workspace locations were chosen such that for group j, the paths (for training and testing) that were identical in joint space were orthogonal in hand space. The subjects in group j showed good transfer between workspace locations, whereas the subjects in group h showed poor transfer. These results are in agreement with the idea that new dynamics are encoded in intrinsic coordinates and that this learning has a limited range of generalization across joint velocities.

Because our environment and our body can change from time to time, the efficiency of human motor behavior relies on the updating of the neural processes transforming intentions into actions. Adaptation to the context critically depends on sensory feedback such as vision, touch or hearing. Although proprioception is not commonly listed as one of the main senses, its role is determinant for the coordination of daily gestures like goal-directed arm movements. In particular, previous work suggests that proprioceptive information is critical to update the internal representation of limb dynamic properties. Here, we examined the motor behavior of a deafferented patient, deprived of proprioception below the nose, to assess adaptation to new dynamic conditions in the absence of limb proprioception. The patient, and age-matched control participants, reached toward visual targets in a new force field created by a rotating platform. Full vision of the limb and workspace was available throughout the experiment. Although her impairment was obvious in baseline reaching performance, the proprioceptively deafferented patient clearly adapted to the new force conditions. In fact, her time course of adaptation was similar to that observed in controls. Moreover, when tested in the normal force field after adaptation to the new force field, the patient exhibited after-effects similar to those of controls. These findings show that motor adaptation to a modified force field is possible without proprioception and that vision can compensate for the permanent loss of proprioception to update the central representation of limb dynamics.

Darainy, Mohammad, Nicole Malfait, Paul L. Gribble, Farzad Towhidkhah, and David J. Ostry. Learning to control arm stiffness under static conditions. J Neurophysiol 92: 3344 -3350, 2004. First published July 28, 2004 doi:10.1152/jn.00596.2004. We used a robotic device to test the idea that impedance control involves a process of learning or adaptation that is acquired over time and permits the voluntary control of the pattern of stiffness at the hand. The tests were conducted in statics. Subjects were trained over the course of 3 successive days to resist the effects of one of three different kinds of mechanical loads: single axis loads acting in the lateral direction, single axis loads acting in the forward/backward direction, and isotropic loads that perturbed the limb in eight directions about a circle. We found that subjects in contact with single axis loads voluntarily modified their hand stiffness orientation such that changes to the direction of maximum stiffness mirrored the direction of applied load. In the case of isotropic loads, a uniform increase in endpoint stiffness was observed. Using a physiologically realistic model of two-joint arm movement, the experimentally determined pattern of impedance change could be replicated by assuming that coactivation of elbow and double joint muscles was independent of coactivation of muscles at the shoulder. Moreover, using this pattern of coactivation control we were able to replicate an asymmetric pattern of rotation of the stiffness ellipse that was observed empirically. These findings are consistent with the idea that arm stiffness is controlled through the use of at least two independent co-contraction commands.

In humans, electrophysiological correlates of error processing have been extensively investigated in relation to decision-making theories. In particular, error-related ERPs have been most often studied using response selection tasks. In these tasks, involving very simple motor responses (e.g., button press), errors concern inappropriate action-selection only. However, EEG activity in relation to inaccurate movement-execution in more complex motor tasks has been much less examined. In the present study, we recorded EEG while volunteers performed reaching movements in a force-field created by a robotic device. Hand-path deviations were induced by interspersing catch trials in which the force condition was unpredictably altered. Our goal was twofold. First, we wanted to determine whether a frontocentral ERP was elicited by sensory-prediction errors, whose amplitude reflected the size of kinematic errors. Then, we explored whether common neural processes could be involved in the generation of this ERP and the feedback-related negativity (FRN), often assumed to reflect reward-prediction errors. We identified a frontocentral negativity whose amplitude was modulated by the size of the hand-path deviations induced by the unpredictable mechanical perturbations. This kinematic error-related ERP presented great similarities in terms of time course, topography, and potential source-location with the FRN recorded in the same experiment. These findings suggest that the processing of sensory-prediction errors and the processing of reward-prediction errors could involve a shared neural network.