Jörn Diedrichsen

Principles of sensorimotor learning

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities — whether it is snowboarding or ballroom dancing — but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.

Neural Correlates of Reach Errors

Reach errors may be broadly classified into errors arising from unpredictable changes in target location, called target errors, and errors arising from miscalibration of internal models (e.g., when prisms alter visual feedback or a force field alters limb dynamics), called execution errors. Execution errors may be caused by miscalibration of dynamics (e.g., when a force field alters limb dynamics) or by miscalibration of kinematics (e.g., when prisms alter visual feedback). Although all types of errors lead to similar on-line corrections, we found that the motor system showed strong trial-by-trial adaptation in response to random execution errors but not in response to random target errors. We used functional magnetic resonance imaging and a compatible robot to study brain regions involved in processing each kind of error. Both kinematic and dynamic execution errors activated regions along the central and the postcentral sulci and in lobules V, VI, and VIII of the cerebellum, making these areas possible sites of plastic changes in internal models for reaching. Only activity related to kinematic errors extended into parietal area 5. These results are inconsistent with the idea that kinematics and dynamics of reaching are computed in separate neural entities. In contrast, only target errors caused increased activity in the striatum and the posterior superior parietal lobule. The cerebellum and motor cortex were as strongly activated as with execution errors. These findings indicate a neural and behavioral dissociation between errors that lead to switching of behavioral goals and errors that lead to adaptation of internal models of limb dynamics and kinematics.

The cerebellum and event timing

Abstract: Damage to the cerebellum disrupts performance on a range of tasks that require precise timing including the production of skilled movements, eyeblink conditioning, and perceptual tasks such as duration discrimination. We hypothesize that such tasks involve event timing, a form of representation in which the temporal goals are explicitly represented. For example, during finger tapping, the goal to produce evenly paced intervals invokes an explicit temporal representation of the time between successive contact points with the tapping surface. In contrast, timing can be an emergent property in other actions, reflecting temporal consistencies that arise through the control of other movement parameters. One example is continuous circle drawing, a task in which temporal consistency can be achieved by maintaining a constant angular velocity or minimizing higher‐order derivatives (e.g., jerk). Temporal consistency on event and emergent timing tasks is not correlated and patients with cerebellar damage show no increase in temporal variability during continuous circle drawing. While the cerebellum likely contributes to performance of a wide range of skilled behaviors, it appears to be especially important when the tasks entail event timing.

The coordination of movement: optimal feedback control and beyond

Optimal control theory and its more recent extension, optimal feedback control theory, provide valuable insights into the flexible and task-dependent control of movements. Here, we focus on the problem of coordination, defined as movements that involve multiple effectors (muscles, joints or limbs). Optimal control theory makes quantitative predictions concerning the distribution of work across multiple effectors. Optimal feedback control theory further predicts variation in feedback control with changes in task demands and the correlation structure between different effectors. We highlight two crucial areas of research, hierarchical control and the problem of movement initiation, that need to be developed for an optimal feedback control theory framework to characterise movement coordination more fully and to serve as a basis for studying the neural mechanisms involved in voluntary motor control.

Use-Dependent and Error-Based Learning of Motor Behaviors

Human motor behavior is constantly adapted through the process of error-based learning. When the motor system encounters an error, its estimate about the body and environment will change, and the next movement will be immediately modified to counteract the underlying perturbation. Here, we show that a second mechanism, use-dependent learning, simultaneously changes movements to become more similar to the last movement. In three experiments, participants made reaching movements toward a horizontally elongated target, such that errors in the initial movement direction did not have to be corrected. Along this task-redundant dimension, we were able to induce use-dependent learning by passively guiding movements in a direction angled by 8° from the previous direction. In a second study, we show that error-based and use-dependent learning can change motor behavior simultaneously in opposing directions by physically constraining the direction of active movements. After removal of the constraint, participants briefly exhibit an error-based aftereffect against the direction of the constraint, followed by a longer-lasting use-dependent aftereffect in the direction of the constraint. In the third experiment, we show that these two learning mechanisms together determine the solution the motor system adopts when learning a motor task.

Detecting and adjusting for artifacts in fMRI time series data

We present a new method to detect and adjust for noise and artifacts in functional MRI time series data. We note that the assumption of stationary variance, which is central to the theoretical treatment of fMRI time series data, is often violated in practice. Sporadic events such as eye, mouth, or arm movements can increase noise in a spatially global pattern throughout an image, leading to a non-stationary noise process. We derive a restricted maximum likelihood (ReML) algorithm that estimates the variance of the noise for each image in the time series. These variance parameters are then used to obtain a weighted least squares estimate of the regression parameters of a linear model. We apply this approach to a typical fMRI experiment with a block design and show that the noise estimates strongly vary across different images and that our method detects and appropriately weights images that are affected by artifacts. Furthermore, we show that the noise process has a global spatial distribution and that the variance increase is multiplicative rather than additive. The new algorithm results in significantly increased sensitivity in the ability to detect regions of activation. The new method may be particularly useful for studies that involve special populations (e.g., children or elderly) where sporadic, artifact-generating events are more likely.

Callosotomy patients exhibit temporal uncoupling during continuous bimanual movements

Rhythmic bimanual movements are highly constrained in the temporal domain, with the gestures of the two hands tightly synchronized. Previous studies have implicated a subcortical locus for temporal coupling based on the observation that these constraints persist in callosotomy patients. We now report that such coupling is restricted to movements entailing a discrete event (such as a movement onset). Three callosotomy patients exhibited a striking lack of temporal coupling during continuous movements, with the two hands oscillating at non-identical frequencies. We propose a subcortical locus of temporal coupling for movements involving discrete events. In contrast, synchronization between the hands during continuous movements depends on interhemispheric transmission across the corpus callosum.

Reach Adaptation: What Determines Whether We Learn an Internal Model of the Tool or Adapt the Model of Our Arm?

We make errors when learning to use a new tool. However, the cause of error may be ambiguous: is it because we misestimated properties of the tool or of our own arm? We considered a well-studied adaptation task in which people made goal-directed reaching movements while holding the handle of a robotic arm. The robot produced viscous forces that perturbed reach trajectories. As reaching improved with practice, did people recalibrate an internal model of their arm, or did they build an internal model of the novel tool (robot), or both? What factors influenced how the brain solved this credit assignment problem? To investigate these questions, we compared transfer of adaptation between three conditions: catch trials in which robot forces were turned off unannounced, robot-null trials in which subjects were told that forces were turned off, and free-space trials in which subjects still held the handle but watched as it was detached from the robot. Transfer to free space was 40% of that observed in unannounced catch trials. We next hypothesized that transfer to free space might increase if the training field changed gradually, rather than abruptly. Indeed, this method increased transfer to free space from 40 to 60%. Therefore although practice with a novel tool resulted in formation of an internal model of the tool, it also appeared to produce a transient change in the internal model of the subjects arm. Gradual changes in the tools dynamics increased the extent to which the nervous system recalibrated the model of the subjects own arm.

Optimal Task-Dependent Changes of Bimanual Feedback Control and Adaptation

Summary The control and adaptation of bimanual movements is often considered to be a function of a fixed set of mechanisms [1, 2]. Here, I show that both feedback control and adaptation change optimally with task goals. Participants reached with two hands to two separate spatial targets (two-cursor condition) or used the same bimanual movements to move a cursor presented at the spatial average location of the two hands to a single target (one-cursor condition). A force field was randomly applied to one of the hands. In the two-cursor condition, online corrections occurred only on the perturbed hand, whereas the other movement was controlled independently. In the one-cursor condition, online correction could be detected on both hands as early as 190 ms after the start. These changes can be shown to be optimal in respect to a simple task-dependent cost function [3]. Adaptation, the influence of a perturbation onto the next movement, also depended on task goals. In the two-cursor condition, only the perturbed hand adapted to a force perturbation [2], whereas in the one-cursor condition, both hands adapted. These findings demonstrate that the central nervous system changes bimanual feedback control and adaptation optimally according to the current task requirements.

Surface-Based Information Mapping Reveals Crossmodal Vision–Action Representations in Human Parietal and Occipitotemporal Cortex

Many lines of evidence point to a tight linkage between the perceptual and motoric representations of actions. Numerous demonstrations show how the visual perception of an action engages compatible activity in the observers motor system. This is seen for both intransitive actions (e.g., in the case of unconscious postural imitation) and transitive actions (e.g., grasping an object). Although the discovery of “mirror neurons” in macaques has inspired explanations of these processes in human action behaviors, the evidence for areas in the human brain that similarly form a crossmodal visual/motor representation of actions remains incomplete. To address this, in the present study, participants performed and observed hand actions while being scanned with functional MRI. We took a data-driven approach by applying whole-brain information mapping using a multivoxel pattern analysis (MVPA) classifier, performed on reconstructed representations of the cortical surface. The aim was to identify regions in which local voxelwise patterns of activity can distinguish among different actions, across the visual and motor domains. Experiment 1 tested intransitive, meaningless hand movements, whereas experiment 2 tested object-directed actions (all right-handed). Our analyses of both experiments revealed crossmodal action regions in the lateral occipitotemporal cortex (bilaterally) and in the left postcentral gyrus/anterior parietal cortex. Furthermore, in experiment 2 we identified a gradient of bias in the patterns of information in the left hemisphere postcentral/parietal region. The postcentral gyrus carried more information about the effectors used to carry out the action (fingers vs. whole hand), whereas anterior parietal regions carried more information about the goal of the action (lift vs. punch). Taken together, these results provide evidence for common neural coding in these areas of the visual and motor aspects of actions, and demonstrate further how MVPA can contribute to our understanding of the nature of distributed neural representations.