The human somatosensory cortex contributes to the encoding of newly learned movements.

Recent studies have indicated somatosensory cortex involvement in motor learning and retention. However, the nature of its contribution is unknown. One possibility is that the somatosensory cortex is transiently engaged during movement. Alternatively, there may be durable learning-related changes which would indicate sensory participation in the encoding of learned movements. These possibilities are dissociated by disrupting the somatosensory cortex following learning, thus targeting learning-related changes which may have occurred. If changes to the somatosensory cortex contribute to retention, which, in effect, means aspects of newly learned movements are encoded there, disruption of this area once learning is complete should lead to an impairment. Participants were trained to make movements while receiving rotated visual feedback. The primary motor cortex (M1) and the primary somatosensory cortex (S1) were targeted for continuous theta-burst stimulation, while stimulation over the occipital cortex served as a control. Retention was assessed using active movement reproduction, or recognition testing, which involved passive movements produced by a robot. Disruption of the somatosensory cortex resulted in impaired motor memory in both tests. Suppression of the motor cortex had no impact on retention as indicated by comparable retention levels in control and motor cortex conditions. The effects were learning specific. When stimulation was applied to S1 following training with unrotated feedback, movement direction, the main dependent variable, was unaltered. Thus, the somatosensory cortex is part of a circuit that contributes to retention, consistent with the idea that aspects of newly learned movements, possibly learning-updated sensory states (new sensory targets) which serve to guide movement, may be encoded there.

Disruption of somatosensory cortex impairs motor learning and retention.

This study tests for a function of the somatosensory cortex, that, in addition to its role in processing somatic afferent information, somatosensory cortex contributes both to motor learning and the stabilization of motor memory. Continuous theta-burst magnetic stimulation (cTBS) was applied, before force-field training to disrupt activity in either the primary somatosensory cortex, primary motor cortex, or a control zone over the occipital lobe. Tests for retention and relearning were conducted after a 24 h delay. Analysis of movement kinematic measures and force-channel trials found that cTBS to somatosensory cortex disrupted both learning and subsequent retention, whereas cTBS to motor cortex had little effect on learning but possibly impaired retention. Basic movement variables are unaffected by cTBS suggesting that the stimulation does not interfere with movement but instead disrupts changes in the cortex that are necessary for learning. In all experimental conditions, relearning in an abruptly introduced force field, which followed retention testing, showed extensive savings, which is consistent with previous work suggesting that more cognitive aspects of learning and retention are not dependent on either of the cortical zones under test. Taken together, the findings are consistent with the idea that motor learning is dependent on learning-related activity in the somatosensory cortex.NEW & NOTEWORTHY This study uses noninvasive transcranial magnetic stimulation to test the contribution of somatosensory and motor cortex to human motor learning and retention. Continuous theta-burst stimulation is applied before learning; participants return 24 h later to assess retention. Disruption of the somatosensory cortex is found to impair both learning and retention, whereas disruption of the motor cortex has no effect on learning. The findings are consistent with the idea that motor learning is dependent upon learning-related plasticity in somatosensory cortex.

Towards a somatosensory theory of speech perception.

Speech perception is known to be a multimodal process, relying not only on auditory input but also on the visual system and possibly on the motor system as well. To date there has been little work on the potential involvement of the somatosensory system in speech perception. In the present review, we identify the somatosensory system as another contributor to speech perception. First, we argue that evidence in favor of a motor contribution to speech perception can just as easily be interpreted as showing somatosensory involvement. Second, physiological and neuroanatomical evidence for auditory-somatosensory interactions across the auditory hierarchy indicates the availability of a neural infrastructure that supports somatosensory involvement in auditory processing in general. Third, there is accumulating evidence for somatosensory involvement in the context of speech specifically. In particular, tactile stimulation modifies speech perception, and speech auditory input elicits activity in somatosensory cortical areas. Moreover, speech sounds can be decoded from activity in somatosensory cortex; lesions to this region affect perception, and vowels can be identified based on somatic input alone. We suggest that the somatosensory involvement in speech perception derives from the somatosensory-auditory pairing that occurs during speech production and learning. By bringing together findings from a set of studies that have not been previously linked, the present article identifies the somatosensory system as a presently unrecognized contributor to speech perception.

Persistence of adaptation following visuomotor training.

Retention tests conducted after sensorimotor adaptation frequently exhibit a rapid return to baseline performance once the altered sensory feedback is removed. This so-called washout of learning stands in contrast with other demonstrations of retention, such as savings on re-learning and anterograde interference effects of initial learning on new learning. In the present study, we tested the hypothesis that washout occurs when there is a detectable discrepancy in retention tests between visual information on the target position and somatosensory information on the position of the limb. Participants were tested following adaptation to gradually rotated visual feedback (15° or 30°). Two different types of targets were used for retention testing, a point target in which a perceptual mismatch is possible, and an arc-target that eliminated the mismatch. It was found that, except when point targets were used, retention test movements were stable throughout aftereffect trials, indicating little loss of information. Substantial washout was only observed in tests with a single point target, following adaptation to a large amplitude 30° rotation. In control studies designed to minimize the use of explicit strategies during learning, we observed similar patterns of decay when participants moved to point targets that suggests that the effects observed here relate primarily to implicit learning. The results suggest that washout in aftereffect trials following visuomotor adaptation is due to a detectable mismatch between vision and somatosensation. When the mismatch is removed experimentally, there is little evidence of loss of information.NEW & NOTEWORTHY Aftereffects following sensorimotor adaptation are important because they bear on the understanding of the mechanisms that subserve forgetting. We present evidence that information loss previously reported during retention testing occurs only when there is a detectable discrepancy between vision and somatosensation and, if this mismatch is removed, the persistence of adaptation is observed. This suggests that washout during aftereffect trials is a consequence of the experimental design rather than a property of the memory system itself.

Ventrolateral Prefrontal Cortex Contributes to Human Motor Learning.

This study assesses the involvement in human motor learning, of the ventrolateral prefrontal cortex (BA 9/46v), a somatic region in the middle frontal gyrus. The potential involvement of this cortical area in motor learning is suggested by studies in nonhuman primates which have found anatomic connections between this area and sensorimotor regions in frontal and parietal cortex, and also with basal ganglia output zones. It is likewise suggested by electrophysiological studies which have shown that activity in this region is implicated in somatic sensory memory and is also influenced by reward. We directly tested the hypothesis that area 9/46v is involved in reinforcement-based motor learning in humans. Participants performed reaching movements to a hidden target and received positive feedback when successful. Before the learning task, we applied continuous theta burst stimulation (cTBS) to disrupt activity in 9/46v in the left or right hemisphere. A control group received sham cTBS. The data showed that cTBS to left 9/46v almost entirely eliminated motor learning, whereas learning was not different from sham stimulation when cTBS was applied to the same zone in the right hemisphere. Additional analyses showed that the basic reward-history-dependent pattern of movements was preserved but more variable following left hemisphere stimulation, which suggests an overall deficit in somatic memory for target location or target directed movement rather than reward processing per se. The results indicate that area 9/46v is part of the human motor learning circuit.

Clustering analysis of movement kinematics in reinforcement learning.

Reinforcement learning has been used as an experimental model of motor skill acquisition, where at times movements are successful and thus reinforced. One fundamental problem is to understand how humans select exploration over exploitation during learning. The decision could be influenced by factors such as task demands and reward availability. In this study, we applied a clustering algorithm to examine how a change in the accuracy requirements of a task affected the choice of exploration over exploitation. Participants made reaching movements to an unseen target using a planar robot arm and received reward after each successful movement. For one group of participants, the width of the hidden target decreased after every other training block. For a second group, it remained constant. The clustering algorithm was applied to the kinematic data to characterize motor learning on a trial-to-trial basis as a sequence of movements, each belonging to one of the identified clusters. By the end of learning, movement trajectories across all participants converged primarily to a single cluster with the greatest number of successful trials. Within this analysis framework, we defined exploration and exploitation as types of behavior in which two successive trajectories belong to different or similar clusters, respectively. The frequency of each mode of behavior was evaluated over the course of learning. It was found that by reducing the target width, participants used a greater variety of different clusters and displayed more exploration than exploitation. Excessive exploration relative to exploitation was found to be detrimental to subsequent motor learning.NEW & NOTEWORTHY The choice of exploration versus exploitation is a fundamental problem in learning new motor skills through reinforcement. In this study, we employed a data-driven approach to characterize movements on a trial-by-trial basis with an unsupervised clustering algorithm. Using this technique, we found that changes in task demands and, in particular, in the required accuracy of movements, influenced the ratio of exploration to exploitation. This analysis framework provides an attractive tool to investigate mechanisms of explorative and exploitative behavior while studying motor learning.

Self-operated stimuli improve subsequent visual motion integration.

Evidences of perceptual changes that accompany motor activity have been limited primarily to audition and somatosensation. Here we asked whether motor learning results in changes to visual motion perception. We designed a reaching task in which participants were trained to make movements along several directions, while the visual feedback was provided by an intrinsically ambiguous moving stimulus directly tied to hand motion. We find that training improves coherent motion perception and that changes in movement are correlated with perceptual changes. No perceptual changes are observed in passive training even when observers were provided with an explicit strategy to facilitate single motion perception. A Bayesian model suggests that movement training promotes the fine-tuning of the internal representation of stimulus geometry. These results emphasize the role of sensorimotor interaction in determining the persistent properties in space and time that define a percept.

Neural Development of Speech Sensorimotor Learning.

The development of the human brain continues through to early adulthood. It has been suggested that cortical plasticity during this protracted period of development shapes circuits in associative transmodal regions of the brain. Here we considered how cortical plasticity during development might contribute to the coordinated brain activity required for speech motor learning. Specifically, we examined patterns of brain functional connectivity (FC), whose strength covaried with the capacity for speech audio-motor adaptation in children ages 5-12 and in young adults of both sexes. Children and adults showed distinct patterns of the encoding of learning in the brain. Adult performance was associated with connectivity in transmodal regions that integrate auditory and somatosensory information, whereas children rely on basic somatosensory and motor circuits. A progressive reliance on transmodal regions is consistent with human cortical development and suggests that human speech motor adaptation abilities are built on cortical remodeling, which is observable in late childhood and is stabilized in adults.SIGNIFICANCE STATEMENT A protracted period of neuro plasticity during human development is associated with extensive reorganization of associative cortex. We examined how the relationship between FC and speech motor learning capacity are reconfigured in conjunction with this cortical reorganization. Young adults and children aged 5-12 years showed distinctly different patterns. Mature brain networks related to learning included associative cortex, which integrates auditory and somatosensory feedback in speech, whereas the immature networks in children included motor regions of the brain. These patterns are consistent with the cortical reorganization that is initiated in late childhood. The result provides insights into the human biology of speech as well as to the mature neural mechanisms for multisensory integration in motor learning.

Recognition memory for human motor learning.

Motor skill retention is typically measured by asking participants to reproduce previously learned movements from memory. The analog of this retention test (recall memory) in human verbal memory is known to underestimate how much learning is actually retained. Here we asked whether information about previously learned movements, which can no longer be reproduced, is also retained. Following visuomotor adaptation, we used tests of recall that involved reproduction of previously learned movements and tests of recognition in which participants were asked whether a candidate limb displacement, produced by a robot arm held by the subject, corresponded to a movement direction that was experienced during active training. The main finding was that 24 h after training, estimates of recognition memory were about twice as accurate as those of recall memory. Thus, there is information about previously learned movements that is not retrieved using recall testing but can be accessed in tests of recognition. We conducted additional tests to assess whether, 24 h after learning, recall for previously learned movements could be improved by presenting passive movements as retrieval cues. These tests were conducted immediately prior to recall testing and involved the passive playback of a small number of movements, which were spread across the workspace and included both adapted and baseline movements, without being marked as such. This technique restored recall memory for movements to levels close to those of recognition memory performance. Thus, somatic information may enable retrieval of otherwise inaccessible motor memories.

Contribution of sensory memory to speech motor learning.

Speech learning requires precise motor control, but it likewise requires transient storage of information to enable the adjustment of upcoming movements based on the success or failure of previous attempts. The contribution of somatic sensory memory for limb position has been documented in work on arm movement; however, in speech, the sensory support for speech production comes from both somatosensory and auditory inputs, and accordingly sensory memory for either or both of sounds and somatic inputs might contribute to learning. In the present study, adaptation to altered auditory feedback was used as an experimental model of speech motor learning. Participants also underwent tests of both auditory and somatic sensory memory. We found that although auditory memory for speech sounds is better than somatic memory for speechlike facial skin deformations, somatic sensory memory predicts adaptation, whereas auditory sensory memory does not. Thus even though speech relies substantially on auditory inputs and in the present manipulation adaptation requires the minimization of auditory error, it is somatic inputs that provide the memory support for learning.NEW & NOTEWORTHY In speech production, almost everyone achieves an exceptionally high level of proficiency. This is remarkable because speech involves some of the smallest and most carefully timed movements of which we are capable. The present paper demonstrates that sensory memory contributes to speech motor learning. Moreover, we report the surprising result that somatic sensory memory predicts speech motor learning, whereas auditory memory does not.

Speakers are able to categorize vowels based on tongue somatosensation.

Auditory speech perception enables listeners to access phonological categories from speech sounds. During speech production and speech motor learning, speakers' experience matched auditory and somatosensory input. Accordingly, access to phonetic units might also be provided by somatosensory information. The present study assessed whether humans can identify vowels using somatosensory feedback, without auditory feedback. A tongue-positioning task was used in which participants were required to achieve different tongue postures within the /e, ε, a/ articulatory range, in a procedure that was totally nonspeech like, involving distorted visual feedback of tongue shape. Tongue postures were measured using electromagnetic articulography. At the end of each tongue-positioning trial, subjects were required to whisper the corresponding vocal tract configuration with masked auditory feedback and to identify the vowel associated with the reached tongue posture. Masked auditory feedback ensured that vowel categorization was based on somatosensory feedback rather than auditory feedback. A separate group of subjects was required to auditorily classify the whispered sounds. In addition, we modeled the link between vowel categories and tongue postures in normal speech production with a Bayesian classifier based on the tongue postures recorded from the same speakers for several repetitions of the /e, ε, a/ vowels during a separate speech production task. Overall, our results indicate that vowel categorization is possible with somatosensory feedback alone, with an accuracy that is similar to the accuracy of the auditory perception of whispered sounds, and in congruence with normal speech articulation, as accounted for by the Bayesian classifier.

Somatosensory cortex participates in the consolidation of human motor memory.

Newly learned motor skills are initially labile and then consolidated to permit retention. The circuits that enable the consolidation of motor memories remain uncertain. Most work to date has focused on primary motor cortex, and although there is ample evidence of learning-related plasticity in motor cortex, direct evidence for its involvement in memory consolidation is limited. Learning-related plasticity is also observed in somatosensory cortex, and accordingly, it may also be involved in memory consolidation. Here, by using transcranial magnetic stimulation (TMS) to block consolidation, we report the first direct evidence that plasticity in somatosensory cortex participates in the consolidation of motor memory. Participants made movements to targets while a robot applied forces to the hand to alter somatosensory feedback. Immediately following adaptation, continuous theta-burst transcranial magnetic stimulation (cTBS) was delivered to block retention; then, following a 24-hour delay, which would normally permit consolidation, we assessed whether there was an impairment. It was found that when mechanical loads were introduced gradually to engage implicit learning processes, suppression of somatosensory cortex following training almost entirely eliminated retention. In contrast, cTBS to motor cortex following learning had little effect on retention at all; retention following cTBS to motor cortex was not different than following sham TMS stimulation. We confirmed that cTBS to somatosensory cortex interfered with normal sensory function and that it blocked motor memory consolidation and not the ability to retrieve a consolidated motor memory. In conclusion, the findings are consistent with the hypothesis that in adaptation learning, somatosensory cortex rather than motor cortex is involved in the consolidation of motor memory.

Somatosensory cortical excitability changes precede those in motor cortex during human motor learning.

Motor learning is associated with plasticity in both motor and somatosensory cortex. It is known from animal studies that tetanic stimulation to each of these areas individually induces long-term potentiation in its counterpart. In this context it is possible that changes in motor cortex contribute to somatosensory change and that changes in somatosensory cortex are involved in changes in motor areas of the brain. It is also possible that learning-related plasticity occurs in these areas independently. To better understand the relative contribution to human motor learning of motor cortical and somatosensory plasticity, we assessed the time course of changes in primary somatosensory and motor cortex excitability during motor skill learning. Learning was assessed using a force production task in which a target force profile varied from one trial to the next. The excitability of primary somatosensory cortex was measured using somatosensory evoked potentials in response to median nerve stimulation. The excitability of primary motor cortex was measured using motor evoked potentials elicited by single-pulse transcranial magnetic stimulation. These two measures were interleaved with blocks of motor learning trials. We found that the earliest changes in cortical excitability during learning occurred in somatosensory cortical responses, and these changes preceded changes in motor cortical excitability. Changes in somatosensory evoked potentials were correlated with behavioral measures of learning. Changes in motor evoked potentials were not. These findings indicate that plasticity in somatosensory cortex occurs as a part of the earliest stages of motor learning, before changes in motor cortex are observed.NEW & NOTEWORTHY We tracked somatosensory and motor cortical excitability during motor skill acquisition. Changes in both motor cortical and somatosensory excitability were observed during learning; however, the earliest changes were in somatosensory cortex, not motor cortex. Moreover, the earliest changes in somatosensory cortical excitability predict the extent of subsequent learning; those in motor cortex do not. This is consistent with the idea that plasticity in somatosensory cortex coincides with the earliest stages of human motor learning.

Movements following force-field adaptation are aligned with altered sense of limb position.

Previous work has shown that motor learning is associated with changes to both movements and to the somatosensory perception of limb position. In an earlier study that motivates the current work, it appeared that following washout trials, movements did not return to baseline but rather were aligned with associated changes to sensed limb position. Here, we provide a systematic test of this relationship, examining the idea that adaptation-related changes to sensed limb position and to the path of the limb are linked, not only after washout trials but at all stages of the adaptation process. We used a force-field adaptation paradigm followed by washout trials in which subjects performed movements without visual feedback of the limb. Tests of sensed limb position were conducted at each phase of adaptation, specifically before and after baseline movements in a null field, after force-field adaptation, and following washout trials in a null field. As in previous work, sensed limb position changed in association with force-field adaptation. At each stage of adaptation, we observed a correlation between the sensed limb position and associated path of the limb. At a group level, there were differences between the clockwise and counter-clockwise conditions. However, whenever there were changes in sensed limb position, movements following washout did not return to baseline. This suggests that adaptation in sensory and motor systems is not independent processes but rather sensorimotor adaptation is linked to sensory change. Sensory change and limb movement remain in alignment throughout adaptation such that the path of the limb is aligned with the altered sense of limb position.

A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke.

BACKGROUND: Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of-principle study investigates whether these effects can be observed in stroke patients. METHODS: A total of 10 chronic stroke patients with a range of stable motor and sensory deficits (Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory Assessment [NSA] 10-40) underwent resting-state functional magnetic resonance imaging before and after a single session of robot-controlled proprioceptive training with feedback. Changes in FC were identified in each patient using independent component analysis as well as a seed region-based approach. FC changes were related to impairment and changes in task performance were assessed. RESULTS: A single training session improved average arm reaching accuracy in 6 and proprioception in 8 patients. Two networks showing training-associated FC change were identified. Network C1 was present in all patients and network C2 only in patients with FM scores >7. Relatively larger C1 volume in the ipsilesional hemisphere was associated with less impairment ( r = 0.83 for NSA, r = 0.73 for FMA). This association was driven by specific regions in the contralesional hemisphere and their functional connections (supramarginal gyrus with FM scores r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with NSA score r = -0.82). CONCLUSION: A single session of robot-controlled proprioceptive training with feedback improved movement accuracy and induced FC changes in sensory motor networks of chronic stroke patients. FC changes are related to functional impairment and comprise bilateral sensory and motor network nodes.

Neural Basis of Sensorimotor Plasticity in Speech Motor Adaptation.

When we speak, we get correlated sensory feedback from speech sounds and from the muscles and soft tissues of the vocal tract. Here we dissociate the contributions of auditory and somatosensory feedback to identify brain networks that underlie the somatic contribution to speech motor learning. The technique uses a robotic device that selectively alters somatosensory inputs in combination with resting-state fMRI scans that reveal learning-related changes in functional connectivity. A partial correlation analysis is used to identify connectivity changes that are not explained by the time course of activity in any other learning-related areas. This analysis revealed changes related to behavioral improvements in movement and separately, to changes in auditory perception: Speech motor adaptation itself was associated with connectivity changes that were primarily in non-motor areas of brain, specifically, to a strengthening of connectivity between auditory and somatosensory cortex and between presupplementary motor area and the inferior parietal lobule. In contrast, connectively changes associated with alterations to auditory perception were restricted to speech motor areas, specifically, primary motor cortex and inferior frontal gyrus. Overall, our findings show that during adaptation, somatosensory inputs result in a broad range of changes in connectivity in areas associated with speech motor control and learning.

Somatosensory working memory in human reinforcement-based motor learning.

Recent studies using visuomotor adaptation and sequence learning tasks have assessed the involvement of working memory in the visuospatial domain. The capacity to maintain previously performed movements in working memory is perhaps even more important in reinforcement-based learning to repeat accurate movements and avoid mistakes. Using this kind of task in the present work, we tested the relationship between somatosensory working memory and motor learning. The first experiment involved separate memory and motor learning tasks. In the memory task, the participant's arm was displaced in different directions by a robotic arm, and the participant was asked to judge whether a subsequent test direction was one of the previously presented directions. In the motor learning task, participants made reaching movements to a hidden visual target and were provided with positive feedback as reinforcement when the movement ended in the target zone. It was found that participants that had better somatosensory working memory showed greater motor learning. In a second experiment, we designed a new task in which learning and working memory trials were interleaved, allowing us to study participants' memory for movements they performed as part of learning. As in the first experiment, we found that participants with better somatosensory working memory also learned more. Moreover, memory performance for successful movements was better than for movements that failed to reach the target. These results suggest that somatosensory working memory is involved in reinforcement motor learning and that this memory preferentially keeps track of reinforced movements. NEW & NOTEWORTHY The present work examined somatosensory working memory in reinforcement-based motor learning. Working memory performance was reliably correlated with the extent of learning. With the use of a paradigm in which learning and memory trials were interleaved, memory was assessed for movements performed during learning. Movements that received positive feedback were better remembered than movements that did not. Thus working memory does not track all movements equally but is biased to retain movements that were rewarded.

Different adaptation rates to abrupt and gradual changes in environmental dynamics.

Adaptation to an abrupt change in the dynamics of the interaction between the arm and the physical environment has been reported as occurring more rapidly but with less retention than adaptation to a gradual change in interaction dynamics. Faster adaptation to an abrupt change in interaction dynamics appears inconsistent with kinematic error sensitivity which has been shown to be greater for small errors than large errors. However, the comparison of adaptation rates was based on incomplete adaptation. Furthermore, the metric which was used as a proxy of the changing internal state, namely the linear regression between the force disturbance and the compensatory force (the adaptation index), does not distinguish between internal state inaccuracy resulting from amplitude or temporal errors. To resolve the apparent inconsistency, we compared the evolution of the internal state during complete adaptation to an abrupt and gradual change in interaction dynamics. We found no difference in the rate at which the adaptation index increased during adaptation to a gradual compared to an abrupt change in interaction dynamics. In addition, we separately examined amplitude and temporal errors using different metrics, and found that amplitude error was reduced more rapidly under the gradual than the abrupt condition, whereas temporal error (quantified by smoothness) was reduced more rapidly under the abrupt condition. We did not find any significant change in phase lag during adaptation under either condition. Our results also demonstrate that even after adaptation is complete, online feedback correction still plays a significant role in the control of reaching.

Error-related Persistence of Motor Activity in Resting-state Networks.

The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

From known to unknown: moving to unvisited locations in a novel sensorimotor map.

Sensorimotor learning requires knowledge of the relationship between movements and sensory effects: a sensorimotor map. Generally, these mappings are not innate but have to be learned. During learning, the challenge is to build a continuous map from a set of discrete observations, that is, predict locations of novel targets. One hypothesis is that the learner linearly interpolates among discrete observations that are already in the map. Here, this hypothesis is tested by exposing human subjects to a novel mapping between arm movements and sounds. Participants were passively moved to the edges of the workspace receiving the corresponding sounds and then were presented intermediate sounds and asked to make movements to locations they thought corresponded to those sounds. It is observed that average movements roughly match linear interpolation of the space. However, the actual distribution of participants' movements is best described by a bimodal reaching strategy in which they move to one of two locations near the workspace edge where they had prior exposure to the sound-movement pairing. These results suggest that interpolation happens to a limited extent only and that the acquisition of sensorimotor maps may not be driven by interpolation but instead relies on a flexible recombination of instance-based learning.