Sensory Consequences Of Motor Commands Research Articles

Mental practice modulates functional connectivity between the cerebellum and the primary motor cortex

SummaryOur brain has the extraordinary capacity to improve motor skills through mental practice. Conceptually, this ability is attributed to internal forward models, which are cerebellar neural networks that can predict the sensory consequences of motor commands. In our study, we employed single and dual-coil transcranial magnetic stimulations to probe the level of corticospinal excitability and cerebellar-brain inhibition, respectively, before and after a mental practice session or a control session. Motor skill (i.e., accuracy and speed) was measured using a sequential finger tapping-task. We found that mental practice enhanced both speed and accuracy. In parallel, the functional connectivity between the cerebellum and the primary motor cortex changed, with less inhibition from the first to the second. These findings reveal the existence of neuroplastic changes within the cerebellum, supporting the involvement of internal models after mental practice.

Modeling Sensory Preference in Speech Motor Planning: A Bayesian Modeling Framework.

Experimental studies of speech production involving compensations for auditory and somatosensory perturbations and adaptation after training suggest that both types of sensory information are considered to plan and monitor speech production. Interestingly, individual sensory preferences have been observed in this context: subjects who compensate less for somatosensory perturbations compensate more for auditory perturbations, and vice versa. We propose to integrate this sensory preference phenomenon in a model of speech motor planning using a probabilistic model in which speech units are characterized both in auditory and somatosensory terms. Sensory preference is implemented in the model according to two approaches. In the first approach, which is often used in motor control models accounting for sensory integration, sensory preference is attributed to the relative precision (i.e., inverse of the variance) of the sensory characterization of the speech motor goals associated with phonological units (which are phonemes in the context of this paper). In the second, “more original” variant, sensory preference is implemented by modulating the sensitivity of the comparison between the predicted sensory consequences of motor commands and the sensory characterizations of the phonemes. We present simulation results using these two variants, in the context of the adaptation to an auditory perturbation, implemented in a 2-dimensional biomechanical model of the tongue. Simulation results show that both variants lead to qualitatively similar results. Distinguishing them experimentally would require precise analyses of partial compensation patterns. However, the second proposed variant implements sensory preference without changing the sensory characterizations of the phonemes. This dissociates sensory preference and sensory characterizations of the phonemes, and makes the account of sensory preference more flexible. Indeed, in the second variant the sensory characterizations of the phonemes can remain stable, when sensory preference varies as a response to cognitive or attentional control. This opens new perspectives for capturing speech production variability associated with aging, disorders and speaking conditions.

Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation

Humans predict the sensory consequences of motor commands by learning internal models of the body and of environment perturbations. When facing a sensory prediction error, should we attribute this error to a change in our body, and update the body internal model, or to a change in the environment? In the latter case, should we update an existing perturbation model or create a new model? Here, we propose that a decision-making process compares the models' prediction errors, weighted by their precisions, to select and update either the body model or an existing perturbation model. When no model can predict a perturbation, a new perturbation model is created and selected. When a model is selected, both the prediction's mean estimate and uncertainty are updated to minimize future prediction errors and to increase the precision of the predictions. Results from computer simulations, which we verified in an arm visuomotor adaptation experiment with subjects of both sexes, account for short aftereffects and large savings after adaptation to large, but not small, perturbations. Results also clarify previous data in the absence of errors (error-clamp): motor memories show an initial lack of decay after a large perturbation, but gradual decay after a small perturbation. Finally, qualitative individual differences in adaptation were explained by subjects selecting and updating either the body model or a perturbation model. Our results suggest that motor adaptation belongs to a general class of learning according to which memories are created when no existing memories can predict sensory data accurately and precisely.SIGNIFICANCE STATEMENT When movements are followed by unexpected outcomes, such as following the introduction of a visuomotor or a force field perturbation, or the sudden removal of such perturbations, it is unclear whether the CNS updates existing memories or creates new memories. Here, we propose a novel model of adaptation, and investigate, via computer simulations and behavioral experiments, how the amplitude and schedule of the perturbation, as well as the characteristics of the learner, lead to the selection and update of existing memories or the creation of new memories. Our results provide insights into a number of puzzling and contradictory motor adaptation data, as well as into qualitative individual differences in adaptation.

Reduced transfer of visuomotor adaptation is associated with aberrant sense of agency in schizophrenia

One deficit associated with schizophrenia (SZ) is the reduced ability to distinguish self-caused sensations from those due to external sources. This reduced sense of agency (SoA, subjective awareness of control over one’s actions) is hypothesized to result from a diminished utilization of internal monitoring signals of self-movement (i.e., efference copy) which subsequently impairs forming and utilizing sensory prediction errors (differences between the predicted and actual sensory consequences resulting from movement). Another important function of these internal monitoring signals is the facilitation of higher-order mechanisms related to motor learning and control. Current predictive-coding models of adaptation postulate that the sensory consequences of motor commands are predicted based on internal action-related information, and that ownership and control of motor behavior is modified in various contexts based on predictive processing. Here, we investigated the connections between SoA and motor adaptation. Schizophrenia patients (SZP, N=30) and non-psychiatric control subjects (HC, N=31) adapted to altered movement visual feedback and applied the motor recalibration to untested contexts (i.e., the spatial generalization). Although adaptation was similar for SZP and controls, the extent of generalization was significantly less for SZP; movement trajectories made by patients to the furthest untrained target (135o) before and after adaptation were largely indistinguishable. Interestingly, deficits in generalization were correlated with positive symptoms of psychosis in SZP (e.g., hallucinations). Generalization was also associated with measures of SoA across both SZP and HC, emphasizing the role action awareness plays in motor behavior, and suggesting that misattributing agency, even in HC, manifests in abnormal motor performance.

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation.

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally generated ('passive') movements. However, these neurons show reduced responses during self-generated ('active') movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously established model of optimal passive self-motion estimation. The simulated sensory error and feedback signals match experimentally measured neuronal responses during active and passive head and trunk rotations and translations. We conclude that a single sensory internal model can combine motor commands with vestibular and proprioceptive signals optimally. Thus, although neurons carrying sensory prediction error or feedback signals show attenuated modulation, the sensory cues and internal model are both engaged and critically important for accurate self-motion estimation during active head movements.

Variance in exposed perturbations impairs retention of visuomotor adaptation.

Sensorimotor control requires an accurate estimate of the state of the body. The brain optimizes state estimation by combining sensory signals with predictions of the sensory consequences of motor commands using a forward model. Given that both sensory signals and predictions are uncertain (i.e., noisy), the brain optimally weights the relative reliance on each source of information during adaptation. In support, it is known that uncertainty in the sensory predictions influences the rate and generalization of visuomotor adaptation. We investigated whether uncertainty in the sensory predictions affects the retention of a new visuomotor relationship. This was done by exposing three separate groups to a visuomotor rotation whose mean was common at 15° counterclockwise but whose variance around the mean differed (i.e., SD of 0°, 3.2°, or 4.5°). Retention was assessed by measuring the persistence of the adapted behavior in a no-vision phase. Results revealed that mean reach direction late in adaptation was similar across groups, suggesting it depended mainly on the mean of exposed rotations and was robust to differences in variance. However, retention differed across groups, with higher levels of variance being associated with a more rapid reversion toward nonadapted behavior. A control experiment ruled out the possibility that differences in retention were accounted for by differences in success rates. Exposure to variable rotations may have increased the uncertainty in sensory predictions, making the adapted forward model more labile and susceptible to change or decay.NEW & NOTEWORTHY The brain predicts the sensory consequences of motor commands through a forward model. These predictions are subject to uncertainty. We use visuomotor adaptation and modulate uncertainty in the sensory predictions by manipulating the variance in exposed rotations. Results reveal that variance does not influence the final extent of adaptation but selectively impairs the retention of motor memories. These results suggest that a more uncertain forward model is more susceptible to change or decay.

Structural Learning in a Visuomotor Adaptation Task Is Explicitly Accessible.

Structural learning is a phenomenon characterized by faster learning in a new situation that shares features of previously experienced situations. One prominent example within the sensorimotor domain is that human participants are faster to counter a novel rotation following experience with a set of variable visuomotor rotations. This form of learning is thought to occur implicitly through the updating of an internal forward model, which predicts the sensory consequences of motor commands. However, recent work has shown that much of rotation learning occurs through an explicitly accessible process, such as movement re-aiming. We sought to determine if structural learning in a visuomotor rotation task is purely implicit (e.g., driven by an internal model) or explicitly accessible (i.e., re-aiming). We found that participants exhibited structural learning: following training with a variable set of rotations, they more quickly learned a novel rotation. This benefit was entirely conferred by the explicit re-aiming of movements. Implicit learning offered little to no contribution. Next, we investigated the specificity of this learning benefit by exposing participants to a novel perturbation drawn from a statistical structure either congruent or incongruent with their prior experience. We found that participants who experienced congruent training and test phase structure (i.e., rotations to rotation) learned more quickly than participants exposed to incongruent training and test phase structure (i.e., gains to rotation) and a control group. These results suggest that structural learning in a visuomotor rotation task is specific to previously experienced statistical structure and expressed via explicit re-aiming of movements.

Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations

Internal models play a key role in cognitive agents by providing on the one hand predictions of sensory consequences of motor commands (forward models), and on the other hand inverse mappings (inverse models) to realize tasks involving control loops, such as imitation tasks. The ability to predict and generate new actions in continuously evolving environments intrinsically requiring the use of different sensory modalities is particularly relevant for autonomous robots, which must also be able to adapt their models online. We present a learning architecture based on self-learned multimodal sensorimotor representations. To attain accurate forward models, we propose an online heterogeneous ensemble learning method that allows us to improve the prediction accuracy by leveraging differences of multiple diverse predictors. We further propose a method to learn inverse models on-the-fly to equip a robot with multimodal learning skills to perform imitation tasks using multiple sensory modalities. We have evaluated the proposed methods on an iCub humanoid robot. Since no assumptions are made on the robot kinematic/dynamic structure, the method can be applied to different robotic platforms.

Daily update of motor predictions by physical activity.

Motor prediction, i.e., the ability to predict the sensory consequences of motor commands, is critical for adapted motor behavior. Like speed or force, the accuracy of motor prediction varies in a 24-hour basis. Although the prevailing view is that basic biological markers regulate this circadian modulation, behavioral factors such as physical activity, itself modulated by the alternation of night and day, can also regulate motor prediction. Here, we propose that physical activity updates motor prediction on a daily basis. We tested our hypothesis by up- and down-regulating physical activity via arm-immobilization and high-intensity training, respectively. Motor prediction was assessed by measuring the timing differences between actual and mental arm movements. Results show that although mental movement time was modulated during the day when the arm was unconstrained, it remained constant when the arm was immobilized. Additionally, increase of physical activity, via release from immobilization or intense bout of training, significantly reduced mental movement time. Finally, mental and actual times were similar in the afternoon in the unconstrained condition, indicating that predicted and actual movements match after sufficient amount of physical activity. Our study supports the view that physical activity calibrates motor predictions on a daily basis.

Plastic corollary discharge predicts sensory consequences of movements in a cerebellum-like circuit.

The capacity to predict the sensory consequences of movements is critical for sensory, motor, and cognitive function. Though it is hypothesized that internal signals related to motor commands, known as corollary discharge, serve to generate such predictions, this process remains poorly understood at the neural circuit level. Here we demonstrate that neurons in the electrosensory lobe (ELL) of weakly electric mormyrid fish generate negative images of the sensory consequences of the fish's own movements based on ascending spinal corollary discharge signals. These results generalize previous findings describing mechanisms for generating negative images of the effects of the fish's specialized electric organ discharge (EOD) and suggest that a cerebellum-like circuit endowed with associative synaptic plasticity acting on corollary discharge can solve the complex and ubiquitous problem of predicting sensory consequences of movements.

Sensory processing and corollary discharge effects in posterior caudal lobe Purkinje cells in a weakly electric mormyrid fish.

Although it has been suggested that the cerebellum functions to predict the sensory consequences of motor commands, how such predictions are implemented in cerebellar circuitry remains largely unknown. A detailed and relatively complete account of predictive mechanisms has emerged from studies of cerebellum-like sensory structures in fish, suggesting that comparisons of the cerebellum and cerebellum-like structures may be useful. Here we characterize electrophysiological response properties of Purkinje cells in a region of the cerebellum proper of weakly electric mormyrid fish, the posterior caudal lobe (LCp), which receives the same mossy fiber inputs and projects to the same target structures as the electrosensory lobe (ELL), a well-studied cerebellum-like structure. We describe patterns of simple spike and climbing fiber activation in LCp Purkinje cells in response to motor corollary discharge, electrosensory, and proprioceptive inputs and provide evidence for two functionally distinct Purkinje cell subtypes within LCp. Protocols that induce rapid associative plasticity in ELL fail to induce plasticity in LCp, suggesting differences in the adaptive functions of the two structures. Similarities and differences between LCp and ELL are discussed in light of these results.

A temporal basis for predicting the sensory consequences of motor commands in an electric fish

Mormyrid electric fish are a model system for understanding how neural circuits predict the sensory consequences of motor acts. Medium ganglion cells in the electrosensory lobe create negative images that predict sensory input resulting from the fish's electric organ discharge (EOD). Previous studies have shown that negative images can be created through plasticity at granule cell-medium ganglion cell synapses, provided that granule cell responses to the brief EOD command are sufficiently varied and prolonged. Here we show that granule cells indeed provide such a temporal basis and that it is well-matched to the temporal structure of self-generated sensory inputs, allowing rapid and accurate sensory cancellation and explaining paradoxical features of negative images. We also demonstrate an unexpected and critical role of unipolar brush cells (UBCs) in generating the required delayed responses. These results provide a mechanistic account of how copies of motor commands are transformed into sensory predictions.

A New Proposal on How Motor Memory Is Consolidated

A New Proposal on How Motor Memory Is Consolidated

Alteration in forward model prediction of sensory outcome of motor action in focal hand dystonia

Focal hand dystonia in musicians is a movement disorder affecting highly trained movements. Rather than being a pure motor disorder related to movement execution only, movement planning, error prediction, and sensorimotor integration are also impaired. Internal models (IMs), of which two types, forward and inverse models have been described and most likely processed in the cerebellum, are known to be involved in these tasks. Recent results indicate that the cerebellum may be involved in the pathophysiology of focal dystonia (FD). Thus, the aim of our study was to investigate whether an IM deficit plays a role in FD. We focused on the forward model (FM), which predicts sensory consequences of motor commands and allows the discrimination between external sensory input and input deriving from motor action. We investigated 19 patients, aged 19–59 and 19 healthy musicians aged 19–36 as controls. Tactile stimuli were applied to fingers II–V of both hands by the experimenter or the patient. After each stimulus the participant rated the stimulus intensity on a scale between 0 (no sensation) and 1 (maximal intensity). The difference of perceived intensity between self- and externally applied (EA) stimuli was then calculated for each finger. For assessing differences between patients and controls we performed a cluster analysis of the affected hand and the corresponding hand of the controls using the fingers II–V as variables in a 4-dimensional hyperspace (chance level = 0.5). Using a cluster analysis, we found a correct classification of the affected finger in 78.9–94.7%. There was no difference between patients and healthy controls of the absolute value of the perceived stimulus intensity. Our results suggest an altered FM function in focal hand dystonia. It has the potential of suggesting a neural correlate within the cerebellum and of helping integrate findings with regard to altered sensorimotor processing and altered prediction in FD in a single framework.

Cerebellar Contributions to Reach Adaptation and Learning Sensory Consequences of Action

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

Learning from Sensory and Reward Prediction Errors during Motor Adaptation

Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

Cerebellum Predicts the Future Motor State

Feed forward control and estimates of the future state of the motor system are critical for fast and coordinated movements. One framework for generating these predictive signals is based on the central nervous system implementing internal models. Internal models provide for representations of the input-output properties of the motor apparatus or their inverses. It has been widely hypothesized that the cerebellum acquires and stores internal models of the motor apparatus. The results of psychophysical, functional imaging and transcranial magnetic stimulation studies in normal subjects support this hypothesis. Also, the deficits in patients with cerebellar dysfunction can be attributed to a failure of predictive feed forward control and/or to accurately estimate the consequences of motor commands. Furthermore, the computation performed by the cerebellar-like electrosensory lobes in several groups of fishes is to predict the sensory consequences of motor commands. However, only a few electrophysiological investigations have directly tested whether neurons in the cerebellar cortex have the requisite signals compatible with either an inverse or forward internal model. Our studies in the monkey performing manual pursuit tracking demonstrate that the simple spike discharge of Purkinje cells does not have the dynamics-related signals required to be the output of an inverse dynamics model. However, Purkinje cell firing has several of the characteristics of a forward internal model of the arm. A synthesis of the evidence suggests that the cerebellum is involved in integrating the current state of the motor system with internally generated motor commands to predict the future state.

Acquisition of internal models of motor tasks in children with autism

Children with autism exhibit a host of motor disorders including poor coordination, poor tool use and delayed learning of complex motor skills like riding a tricycle. Theory suggests that one of the crucial steps in motor learning is the ability to form internal models: to predict the sensory consequences of motor commands and learn from errors to improve performance on the next attempt. The cerebellum appears to be an important site for acquisition of internal models, and indeed the development of the cerebellum is abnormal in autism. Here, we examined autistic children on a range of tasks that required a change in the motor output in response to a change in the environment. We first considered a prism adaptation task in which the visual map of the environment was shifted. The children were asked to throw balls to visual targets with and without the prism goggles. We next considered a reaching task that required moving the handle of a novel tool (a robotic arm). The tool either imposed forces on the hand or displaced the cursor associated with the handle position. In all tasks, the children with autism adapted their motor output by forming a predictive internal model, as exhibited through after-effects. Surprisingly, the rates of acquisition and washout were indistinguishable from normally developing children. Therefore, the mechanisms of acquisition and adaptation of internal models in self-generated movements appeared normal in autism. Sparing of adaptation suggests that alternative mechanisms contribute to impaired motor skill development in autism. Furthermore, the findings may have therapeutic implications, highlighting a reliable mechanism by which children with autism can most effectively alter their behaviour.

Motor Adaptation as a Process of Reoptimization

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.

Why Does the Brain Predict Sensory Consequences of Oculomotor Commands? Optimal Integration of the Predicted and the Actual Sensory Feedback

When the brain initiates a saccade, it uses a copy of the oculomotor commands to predict the visual consequences: for example, if one fixates a reach target, a peripheral saccade will produce an internal estimate of the new retinal location of the target, a process called remapping. In natural settings, the target likely remains visible after the saccade. So why should the brain predict the sensory consequence of the saccade when after its completion, the image of the target remains visible? We hypothesized that in the post-saccadic period, the brain integrates target position information from two sources: one based on remapping and another based on the peripheral view of the target. The integration of information from these two sources could produce a less variable target estimate than is possible from either source alone. Here, we show that reaching toward targets that were initially foveated and remapped had significantly less variance than reaches relying on peripheral target information. Furthermore, in a more natural setting where both sources of information were available simultaneously, variance of the reaches was further reduced as predicted by integration. This integration occurred in a statistically optimal manner, as demonstrated by the change in integration weights when we manipulated the uncertainty of the post-saccadic target estimate by varying exposure time. Therefore, the brain predicts the sensory consequences of motor commands because it integrates its prediction with the actual sensory information to produce an estimate of sensory space that is better than possible from either source alone.