Different Faces of Medial Beta-Band Activity Reflect Distinct Visuomotor Feedback Signals.

Beta-band (13-35 Hz) modulations following reward, task outcome feedback, and error have been described in cognitive and/or motor adaptation tasks. Observations from different studies are, however, difficult to conciliate. Among the studies that used cognitive response selection tasks, several reported an increase in beta-band activity following reward, whereas others observed increased beta power after negative feedback. Moreover, in motor adaptation tasks, an attenuation of the postmovement beta rebound follows a movement execution error induced by visual or mechanical perturbations. Given that kinematic error typically leads to negative task-outcome feedback (e.g., target missed), one may wonder how contradictory modulations, beta power decrease with movement error versus beta power increase with negative feedback, may coexist. We designed a motor adaptation task in which female and male participants experience varied feedbacks-binary success/failure feedback, kinematic error, and sensory-prediction error-and demonstrate that beta-band modulations in opposite directions coexist at different spatial locations, time windows, and frequency ranges. First, high beta power in the medial frontal cortex showed opposite modulations well separated in time when compared in success and failure trials; that is, power was higher in success trials just after the binary success feedback, whereas it was lower in the postmovement period compared with failure trials. Second, although medial frontal high-beta activity was sensitive to task outcome, low-beta power in the medial parietal cortex was strongly attenuated following movement execution error but was not affected by either the outcome of the task or sensory-prediction error. These findings suggest that medial beta activity in different spatio-temporal-spectral configurations play a multifaceted role in encoding qualitatively distinct feedback signals.SIGNIFICANCE STATEMENT Beta-band activity reflects neural processes well beyond sensorimotor functions, including cognition and motivation. By disentangling alternative spatio-temporal-spectral patterns of possible beta-oscillatory activity, we reconcile a seemingly discrepant literature. First, high-beta power in the medial frontal cortex showed opposite modulations separated in time in success and failure trials; power was higher in success trials just after success feedback and lower in the postmovement period compared with failure trials. Second, although medial frontal high-beta activity was sensitive to task outcome, low-beta power in the medial parietal cortex was strongly attenuated following movement execution error but was not affected by the task outcome or the sensory-prediction error. We propose that medial beta activity reflects distinct feedback signals depending on its anatomic location, time window, and frequency range.

Spatially Distinct Beta-Band Activities Reflect Implicit Sensorimotor Adaptation and Explicit Re-aiming Strategy.

Previous research suggests that so-called implicit and explicit processes of motor adaptation are implemented by distinct neural structures. Here we tested whether implicit sensorimotor adaptation and strategic re-aiming used to reduce movement error are reflected by spatially distinct EEG oscillatory components. We analyzed beta-band oscillations (∼13-30 Hz), which have long been linked to sensorimotor functions, at the time when these adaptive processes intervene for movement planning. We hypothesized that beta-band activity within sensorimotor regions relates to implicit adaptive processes, whereas beta-band activity within medial motor areas reflects deliberate re-aiming. In female and male human volunteers, we recorded EEG in a motor adaptation task in which a visual rotation was introduced in short series of trials separated by unperturbed trials. Participants were instructed in advance about the nature of the visual perturbation and trained to counter it by strategically re-aiming at a neighboring target. Consistent with our hypothesis, we found that preparatory beta-band activities within the two regions exhibited different patterns of modulation. Beta power in lateral central regions was attenuated when a change in the visual condition rendered internal-model predictions uncertain. In contrast, beta power in medial frontal regions was selectively decreased when participants strategically re-aimed their reaches. We propose that the reduction in lateral central beta power reflects an increased weighting of peripheral sensory information implicitly triggered when an adaptive change in the sensorimotor mapping is required, whereas the reduction in medial frontal beta-band activity relates to the inhibition of automatic motor responses in favor of cognitively controlled movements.SIGNIFICANCE STATEMENT Behavioral and modeling studies have proposed that so-called implicit and explicit components of motor adaptation recruit different neural circuits. Here, we investigated whether these different processes are reflected by spatially distinct beta-band activities. Analyzing EEG signals at the time they influence movement planning, during the foreperiod, we found that beta power within lateral central regions was decreased when a change in visual conditions required implicit sensorimotor remapping, which may reflect enhanced sensory processing when internal-model predictions are rendered uncertain. In contrast, beta-band power within medial frontal areas was selectively attenuated when participants deliberately re-aimed their movements to improve task performance, which may be associated with the inhibition of automatic motor responses in favor of cognitively controlled movements.

Error-related modulations of the sensorimotor post-movement and foreperiod beta-band activities arise from distinct neural substrates and do not reflect efferent signal processing.

While beta activity has been extensively studied in relation to voluntary movement, its role in sensorimotor adaptation remains largely uncertain. Recently, it has been shown that the post-movement beta rebound as well as beta activity during movement-preparation are modulated by movement errors. However, there are critical functional differences between pre- and post-movement beta activities. Here, we addressed two related open questions. Do the pre- and post-movement error-related modulations arise from distinct neural substrates? Do these modulations relate to efferent signals shaping muscle-activation patterns or do they reflect integration of sensory information, intervening upstream of the motor output? For this purpose, first we exploited independent component analysis (ICA) which revealed a double dissociation suggesting that distinct neural substrates are recruited in error-related beta-power modulations observed before and after movement. Second, we compared error-related beta oscillation responses observed in two bimanual reaching tasks involving similar movements but different interlimb coordination, and in which the same mechanical perturbations induced different behavioral adaptive responses. While the task difference was not reflected in the post-movement beta rebound, the pre-movement beta activity was differently modulated according to the interlimb coordination. Critically, we show an uncoupling between the behavioral and the electrophysiological responses during the movement preparation phase, which demonstrates that the error-related modulation of the foreperiod beta activity does not reflect changes in the motor output from primary motor cortex. It seems instead to relate to higher level processing of sensory afferents, essential for sensorimotor adaptation.

Distinct Modulations in Sensorimotor Postmovement and Foreperiod ß-Band Activities Related to Error Salience Processing and Sensorimotor Adaptation.

In a recent study, Tan et al. (2014a,b) showed that the increase in ß-power typically observed after a movement above sensorimotor regions (ß-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 ß-rebound attenuation might relate to neural processes involved in trial-to-trial adaptive mechanisms. In two EEG experiments with human participants, along with the ß-rebound, we examine ß-activity during the preparation of reaches immediately following perturbed movements. In the first experiment, we show that both foreperiod and postmovement ß-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 ß-activities exhibit contrasting patterns, pointing to important functional differences of their underlying neuronal activity. While both types of reach errors attenuate the postmovement ß-rebound, only the kinematic errors that trigger trial-to-trial motor-command updates influenced ß-activity during the foreperiod. These findings suggest that the error-related modulation of the ß-rebound may reflect salience processing, independent of sensorimotor adaptation. In contrast, modulations in the foreperiod ß-power might relate to the motor-command adjustments activated after movement-execution errors are experienced. SIGNIFICANCE STATEMENT: The functional significance of sensorimotor ß-band (15-25 Hz) oscillations remains uncertain. Recently ß-power was found to be reduced following erroneous movements. We extend and refine this novel finding in two crucial ways. First, by contrasting the EEG correlates of movement errors driving or not driving adaptation we dissociate error-salience processing from error-based adaptation. Second, in addition to ß-activity in error trials, we examine ß-power during the preparation of the subsequent movements. We find clearly distinct patterns of error-related modulations for ß-activities preceding and succeeding movements, highlighting critical functional differences. Postmovement ß-power may reflect error-salience processing independent of sensorimotor adaptation. In contrast, modulations in the foreperiod ß-band power may directly relate to the motor-command adjustments activated after movement-execution errors are experienced.

Does the processing of sensory and reward-prediction errors involve common neural resources? Evidence from a frontocentral negative potential modulated by movement execution errors.

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.

Different neural networks are involved in audiovisual speech perception depending on the context.

How are we able to easily and accurately recognize speech sounds despite the lack of acoustic invariance? One proposed solution is the existence of a neural representation of speech syllable perception that transcends its sensory properties. In the present fMRI study, we used two different audiovisual speech contexts both intended to identify brain areas whose levels of activation would be conditioned by the speech percept independent from its sensory source information. We exploited McGurk audiovisual fusion to obtain short oddball sequences of syllables that were either (a) acoustically different but perceived as similar or (b) acoustically identical but perceived as different. We reasoned that, if there is a single network of brain areas representing abstract speech perception, this network would show a reduction of activity when presented with syllables that are acoustically different but perceived as similar and an increase in activity when presented with syllables that are acoustically similar but perceived as distinct. Consistent with the long-standing idea that speech production areas may be involved in speech perception, we found that frontal areas were part of the neural network that showed reduced activity for sequences of perceptually similar syllables. Another network was revealed, however, when focusing on areas that exhibited increased activity for perceptually different but acoustically identical syllables. This alternative network included auditory areas but no left frontal activations. In addition, our findings point to the importance of subcortical structures much less often considered when addressing issues pertaining to perceptual representations.

fMRI activation during observation of others' reach errors.

When exposed to novel dynamical conditions (e.g., externally imposed forces), neurologically intact subjects easily adjust motor commands on the basis of their own reaching errors. Subjects can also benefit from visual observation of others' kinematic errors. Here, using fMRI, we scanned subjects watching movies depicting another person learning to reach in a novel dynamic environment created by a robotic device. Passive observation of reaching movements (whether or not they were perturbed by the robot) was associated with increased activation in fronto-parietal regions that are normally recruited in active reaching. We found significant clusters in parieto-occipital cortex, intraparietal sulcus, as well as in dorsal premotor cortex. Moreover, it appeared that part of the network that has been shown to be engaged in processing self-generated reach error is also involved in observing reach errors committed by others. Specifically, activity in left intraparietal sulcus and left dorsal premotor cortex, as well as in right cerebellar cortex, was modulated by the amplitude of observed kinematic errors.

Is there an Intrinsic Relationship between LFP Beta Oscillation Amplitude and Firing Rate of Individual Neurons in Macaque Motor Cortex?

The properties of motor cortical local field potential (LFP) beta oscillations have been extensively studied. Their relationship to the local neuronal spiking activity was also addressed. Yet, whether there is an intrinsic relationship between the amplitude of beta oscillations and the firing rate of individual neurons remains controversial. Some studies suggest a mapping of spike rate onto beta amplitude, while others find no systematic relationship. To help resolve this controversy, we quantified in macaque motor cortex the correlation between beta amplitude and neuronal spike count during visuomotor behavior. First, in an analysis termed "task-related correlation", single-trial data obtained across all trial epochs were included. These correlations were significant in up to 32% of cases and often strong. However, a trial-shuffling control analysis recombining beta amplitudes and spike counts from different trials revealed these task-related correlations to reflect systematic, yet independent, modulations of the 2 signals with the task. Second, in an analysis termed "trial-by-trial correlation", only data from fixed trial epochs were included, and correlations were calculated across trials. Trial-by-trial correlations were weak and rarely significant. We conclude that there is no intrinsic relationship between the firing rate of individual neurons and LFP beta oscillation amplitude in macaque motor cortex.

Is interlimb transfer of force-field adaptation a cognitive response to the sudden introduction of load?

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.

Transfer of motor learning across arm configurations.

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.

Stimulation of the posterior parietal cortex interferes with arm trajectory adjustments during the learning of new dynamics.

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.

Corrigendum to "Why does picture naming take longer than word naming? The contribution of articulatory processes".

In a previous article, (Riès, Legou, Burle, Alario, & Malfait, 2012), we reported that articulatory processes contribute to the well-established finding that response latencies are longer for picture naming than for word reading. We based this conclusion on the observation that picture naming, as compared with word reading, lengthened not only the interval between stimulus onset and the initiation of lip muscle activation (premotor time), but also the interval between lip muscle activation and vocal response onset (motor time). However, on the basis of our subsequent work in this area, we believe that our original definition of premotor time (and, consequently, of motor time) was suboptimal. On a sizable number of trials, this led to the detection of lip muscle activation (as inferred from surface EMG) that was apparently unrelated to the articulation of the vocal response. Therefore, we believe it is preferable to operationalize premotor time as the interval between stimulus onset and the muscle activation that occurred closest in time to vocal response onset. After reestimating premotor times according to this new definition, we no longer found an effect of our task contrast on the motor time interval. The present article explains the caveats regarding our previous analysis.

A comparison of two procedures for verbal response time fractionation.

To describe the mental architecture between stimulus and response, cognitive models often divide the stimulus-response (SR) interval into stages or modules. Predictions derived from such models are typically tested by focusing on the moment of response emission, through the analysis of response time (RT) distributions. To go beyond the single response event, we recently proposed a method to fractionate verbal RTs into two physiologically defined intervals that are assumed to reflect different processing stages. The analysis of the durations of these intervals can be used to study the interaction between cognitive and motor processing during speech production. Our method is inspired by studies on decision making that used manual responses, in which RTs were fractionated into a premotor time (PMT), assumed to reflect cognitive processing, and a motor time (MT), assumed to reflect motor processing. In these studies, surface EMG activity was recorded from participants' response fingers. EMG onsets, reflecting the initiation of a motor response, were used as the point of fractionation. We adapted this method to speech-production research by measuring verbal responses in combination with EMG activity from facial muscles involved in articulation. However, in contrast to button-press tasks, the complex task of producing speech often resulted in multiple EMG bursts within the SR interval. This observation forced us to decide how to operationalize the point of fractionation: as the first EMG burst after stimulus onset (the stimulus-locked approach), or as the EMG burst that is coupled to the vocal response (the response-locked approach). The point of fractionation has direct consequences on how much of the overall task effect is captured by either interval. Therefore, the purpose of the current paper was to compare both onset-detection procedures in order to make an informed decision about which of the two is preferable. We concluded in favor or the response-locked approach.

Why does picture naming take longer than word reading? The contribution of articulatory processes.

Since the 19th century, it has been known that response latencies are longer for naming pictures than for reading words aloud. While several interpretations have been proposed, a common general assumption is that this difference stems from cognitive word-selection processes and not from articulatory processes. Here we show that, contrary to this widely accepted view, articulatory processes are also affected by the task performed. To demonstrate this, we used a procedure that to our knowledge had never been used in research on language processing: response-latency fractionating. Along with vocal onsets, we recorded the electromyographic (EMG) activity of facial muscles while participants named pictures or read words aloud. On the basis of these measures, we were able to fractionate the verbal response latencies into two types of time intervals: premotor times (from stimulus presentation to EMG onset), mostly reflecting cognitive processes, and motor times (from EMG onset to vocal onset), related to motor execution processes. We showed that premotor and motor times are both longer in picture naming than in reading, although than in reading, although articulation is already initiated in the latter measure. Future studies based on this new approach should bring valuable clues for a better understanding of the relation between the cognitive and motor processes involved in speech production.

The role of motor learning in spatial adaptation near a tool.

Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented.

Force-field adaptation without proprioception: can vision be used to model limb dynamics?

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.

Limb stiffness is modulated with spatial accuracy requirements during movement in the absence of destabilizing forces.

The motor system can use a number of mechanisms to increase movement accuracy and compensate for perturbing external forces, interaction torques, and neuromuscular noise. Empirical studies have shown that stiffness modulation is one adaptive mechanism used to control arm movements in the presence of destabilizing external force loads. Other work has shown that arm muscle activity is increased at movement end for reaching movements to small visual targets and that changes in stiffness at movement end are oriented to match changes in visual accuracy requirements such as target shape. In this study, we assess whether limb stiffness is modulated to match spatial accuracy requirements during movement, conveyed using visual stimuli, in the absence of external force loads. Limb stiffness was estimated in the middle of reaching movements to visual targets located at the end of a narrow (8 mm) or wide (8 cm) visual track. When greater movement accuracy was required, we observed modest but reliable increases in limb stiffness in a direction perpendicular to the track. These findings support the notion that the motor system uses stiffness control to augment movement accuracy during movement and does so in the absence of external unstable force loads, in response to changing accuracy requirements conveyed using visual cues.

The influence of visual perturbations on the neural control of limb stiffness.

To adapt to novel unstable environments, the motor system modulates limb stiffness to produce selective increases in arm stability. The motor system receives information about the environment via somatosensory and proprioceptive signals related to the perturbing forces and visual signals indicating deviations from an expected hand trajectory. Here we investigated whether subjects modulate limb stiffness during adaptation to a purely visual perturbation. In a first experiment, measurements of limb stiffness were taken during adaptation to an elastic force field (EF). Observed changes in stiffness were consistent with previous reports: subjects increased limb stiffness and did so only in the direction of the environmental instability. In a second experiment, stiffness changes were measured during adaptation to a visual perturbing environment that magnified hand-path deviations in the lateral direction. In contrast to the first experiment, subjects trained in this visual task showed no accompanying change in stiffness, despite reliable improvements in movement accuracy. These findings suggest that this sort of visual information alone may not be sufficient to engage neural systems for stiffness control, which may depend on sensory signals more directly related to perturbing forces, such as those arising from proprioception and somatosensation.

Shape distortion produced by isolated mismatch between vision and proprioception.

To investigate the nature of the visuomotor transformation, previous studies have used pointing tasks and examined how adaptation to a spatially localized mismatch between vision and proprioception generalizes across the workspace. Whereas some studies found extensive spatial generalization of single-point remapping, consistent with the hypothesis of a global realignment of visual and proprioceptive spaces, other studies reported limited transfer associated with variations in initial limb posture. Here, we investigated the effects of spatially localized remapping in the context of a visuomanual tracking task. Subjects tracked a visual target tracing a simple two-dimensional geometrical form without visual feedback except at a single point, where the visual display of the hand was shifted relative to its actual position. After adaptation, hand paths exhibited distortions relative to the visual templates that were inconsistent with the idea of a global realignment of visual and proprioceptive spaces. Results of a visuoproprioceptive matching task showed that these distortions were not limited to active movements but also affected perception of passive limb movements.

Does dystonia always include co-contraction? A study of unconstrained reaching in children with primary and secondary dystonia.

Dystonia is a movement disorder in which involuntary or intermittent muscle contractions cause twisting and repetitive movements, abnormal postures, or both. Excessive co-contraction and abnormalities in the time course of reciprocal inhibition between antagonist groups of muscles are considered to be cardinal features of some types of dystonia and reduced speed of movement is often attributed to involuntary activation of antagonist muscles about a joint. In the present study we describe muscle activity during unconstrained multi-joint reaching movements. Children diagnosed with arm dystonia due to cerebral palsy (CP) or primary dystonia (n=7, 4-16 years, 4 with CP, 3 primary) and similar age healthy subjects pointed alternately to two targets as fast as possible. The children with dystonia showed decreased speed, greater variability, and pauses at targets compared with controls. Decreased speed was mostly due to difficulty in reversing reaching direction, and increased variability was associated with large fluctuations in the duration of the pauses at targets, rather than with variations in the flexion/extension velocity profiles. Surface electromyographic (EMG) activities were examined to assess if the abnormalities observed in the children with dystonia could be explained in terms of increased levels of co-contraction. Unexpectedly, we found that the children with dystonia showed lower levels of co-contraction than the controls during movement, and the pauses at tar-gets were associated with reduced levels of activation rather than with excessive activity in antagonist groups of muscles. Therefore reduced speed of movement during unconstrained reaching may not be due to involuntary activation of the antagonist muscle, and co-contraction of opposing muscles about a joint is not an obligatory feature of multi-joint movement in children with dystonia.