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Daniel M Wolpert

Publications and source records attributed to Daniel M Wolpert.

16 recordsLinked to original sources
PubMed2004

Consolidation of dynamic motor learning is not disrupted by rTMS of primary motor cortex.

Motor skills, once learned, are often retained over a long period of time. However, such learning first undergoes a period of consolidation after practice. During this time, the motor memory is susceptible to being disrupted by the performance of another motor-learning task. Recently, it was shown that repetitive transcranial magnetic stimulation (rTMS) over the primary motor cortex could disrupt the retention of a newly learned ballistic task in which subjects had to oppose their index finger and thumb as rapidly as possible. Here we investigate whether the motor cortex is similarly involved during the consolidation that follows learning novel dynamics. We applied rTMS to primary motor cortex shortly after subjects had either learned to compensate for a dynamic force field applied to their index finger or learned a ballistic finger abduction task. rTMS severely degraded the retention of the learning for the ballistic task but had no effect on retention of the dynamic force-field learning. This suggests that, unlike learning of simple ballistic skills, learning of dynamics may be stored in a more distributed manner, possibly outside the primary motor cortex.

Adult↗
PubMed2004

Bayesian integration in sensorimotor learning.

When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.

Bayes Theorem↗
PubMed2004

Internal models underlying grasp can be additively combined.

Our ability to additively combine two learned internal models was investigated by studying the forces people generate when lifting objects with a precision grip. Subjects were required to alternately lift two objects of identical physical appearance but differing weight. Grip force scaling prior to lift-off was used to estimate the output of the internal model associated with each object. Appropriate internal models were formed when alternately lifting two objects of different weight. The objects were then combined by stacking them one upon the other, and the combined object was lifted. Results show that subjects can additively combine internal models of object dynamics but the sum is biased by a default estimate of the object's weight.

Adult↗
PubMed2003

The role of execution noise in movement variability.

The origin of variability in goal-directed movements is not well understood. Variability can originate from several neural processes such as target localization, movement planning, and movement execution. Here we examine variability resulting from noise in movement execution. In several experiments, subjects moved their unseen hand to visual targets, under conditions which were designed to minimize the variability expected from localization and planning processes. We tested short movements in 32 directions in a center-out reaching task. The variability in the movement endpoints and in the initial movement direction varied systematically with the movement direction, with some directions having up to twice the variability of others. In a second experiment we tested four movements in the same direction but with different extents. Here, the longer movements were systematically curved, and the endpoint ellipses were not aligned with the straight line between starting and end position, but they were roughly aligned with the last part of the trajectory. We show that the variability observed in these experiments cannot be explained by planning noise but is well explained by noise in movement execution. A combination of both signal-dependent and signal-independent noise in the amplitude of the motor commands and temporal noise in their duration can explain the observed variability. Our results suggest that, in general, execution noise accounts for at least a large proportion of movement variability.

Adolescent↗
PubMed2003

A unifying computational framework for motor control and social interaction.

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.

Humans↗
PubMed2003

Prediction precedes control in motor learning.

Skilled motor behavior relies on the brain learning both to control the body and predict the consequences of this control. Prediction turns motor commands into expected sensory consequences, whereas control turns desired consequences into motor commands. To capture this symmetry, the neural processes underlying prediction and control are termed the forward and inverse internal models, respectively. Here, we investigate how these two fundamental processes are related during motor learning. We used an object manipulation task in which subjects learned to move a hand-held object with novel dynamic properties along a prescribed path. We independently and simultaneously measured subjects' ability to control their actions and to predict their consequences. We found different time courses for predictor and controller learning, with prediction being learned far more rapidly than control. In early stages of manipulating the object, subjects could predict the consequences of their actions, as measured by the grip force they used to grasp the object, but could not generate appropriate actions for control, as measured by their hand trajectory. As predicted by several recent theoretical models of sensorimotor control, our results indicate that people can learn to predict the consequences of their actions before they can learn to control their actions.

Biophysical Phenomena↗
PubMed2003

Motor learning and prediction in a variable environment.

Traditional studies of motor learning and prediction have focused on how subjects perform a single task. Recent advances have been made in our understanding of motor learning and prediction by investigating the way we learn variable tasks, which change either predictably or unpredictably over time. Similarly, studies have examined how variability in our own movements affects motor learning.

Biomechanical Phenomena↗
PubMed2003

Spatial representation of predictive motor learning.

A key feature of skilled motor behavior is the ability of the CNS to predict the consequences of its actions. Such prediction occurs when one hand pulls on an object held in the other hand; the restraining hand generates an anticipatory increase in grip force, thereby preventing the object from slipping. When manipulating a novel object, the CNS adapts its predictive response to ensure that predictions are accurately tuned to the dynamics of the object. Here we examine whether learning to predict the consequences of an action on a novel object is restricted to the actions performed during manipulation or generalizes to novel actions. A bimanual task in which subjects held an object in each hand and the relationship between actions on one object and the motion of the other could be computer controlled from trial-to-trial was used. In four conditions we varied the spatial relationship between the direction of force subjects applied to the left-hand object and the consequent direction of motion of an object held in their right hand, which subjects were required to restrain. The results show that predictive learning was local to the direction of forces experienced during learning and that the magnitude of predictive responses was greatly reduced for novel directions of action of the left hand. The pattern of generalization shows that the representation of predictive learning is spatially local and can be approximated as having a spatially narrow Gaussian basis function.

Conditioning, Psychological↗
PubMed2002

Role of uncertainty in sensorimotor control.

Neural signals are corrupted by noise and this places limits on information processing. We review the processes involved in goal-directed movements and how neural noise and uncertainty determine aspects of our behaviour. First, noise in sensory signals limits perception. We show that, when localizing our hand, the central nervous system (CNS) integrates visual and proprioceptive information, each with different noise properties, in a way that minimizes the uncertainty in the overall estimate. Second, noise in motor commands leads to inaccurate movements. We review an optimal-control framework, known as 'task optimization in the presence of signal-dependent noise', which assumes that movements are planned so as to minimize the deleterious consequences of noise and thereby minimize inaccuracy. Third, during movement, sensory and motor signals have to be integrated to allow estimation of the body's state. Models are presented that show how these signals are optimally combined. Finally, we review how the CNS deals with noise at the neural and network levels. In all of these processes, the CNS carries out the tasks in such a way that the detrimental effects of noise are minimized. This shows that it is important to consider effects at the neural level in order to understand performance at the behavioural level.

Humans↗
PubMed2002

When feeling is more important than seeing in sensorimotor adaptation.

Perception and action are based on information from multiple sensory modalities. For instance, both vision and proprioception provide information about hand position, and this information is integrated to generate a single estimate of where the hand is in space. Classically, vision has been thought to dominate this process, with the estimate of hand position relying more on vision than on proprioception. However, an optimal integration model that takes into account the precision of vision and proprioception predicts that the weighting of the two senses varies with direction and that the classical result should only hold for specific spatial directions. Using an adaptation paradigm, we show that, as predicted by this model, the visual-proprioceptive integration varies with direction. Variation with direction was so strong that, in the depth direction, the classical result was reversed: the estimate relies more on proprioception than on vision. These results provide evidence for statistically optimal integration of information from multiple modalities.

Adaptation, Physiological↗
PubMed2002

Planning movements in a simple redundant task.

There are infinitely many different combinations of arm postures which will place the hand at the same point in space. Given this abundance, how is one configuration chosen over another? Two main hypotheses have been proposed to solve this problem. Postural models suggest that the posture adopted is purely determined by the desired hand position (known as Donders' law). Transport models suggest that the adopted posture depends on where the hand has moved from. A specific transport model, the minimum work model, has been proposed in which the adopted posture is the one that minimizes the amount of work required to move the hand to the new location. The postural model predicts that the posture will be independent of where the hand has moved from, whereas the transport models predict that the posture will depend on the previous posture. We have devised a simple redundant task-touching a target bar using a hand-held virtual stick-to examine these models. The results show that neither model alone can account for the data. We propose a control planning strategy in which there is a combined cost function that has both a postural term as well as a transport term.

Arm↗
PubMed2002

Kinematics and dynamics are not represented independently in motor working memory: evidence from an interference study.

Our capacity to learn multiple dynamic and visuomotor tasks is limited by the time between the presentations of the tasks. When subjects are required to adapt to equal and opposite position-dependent visuomotor rotations (Krakauer et al., 1999) or velocity-dependent force fields (Brashers-Krug et al., 1996) in quick succession, interference occurs that prevents the first task from being consolidated in memory. In contrast, such interference is not observed between learning a position-dependent visuomotor rotation and an acceleration-dependent force field. On the basis of this finding, it has been argued that internal models of kinematic and dynamic sensorimotor transformations are learned independently (Krakauer et al., 1999). However, these findings are also consistent with the perturbations interfering only if they depend on the same kinematic variable. We evaluated this hypothesis using kinematic and dynamic transformations matched in terms of the kinematic variable on which they depend. Subjects adapted to a position-dependent visuomotor rotation followed 5 min later by a position-dependent rotary force field either with or without visual feedback of arm position. The force field tended to rotate the hand in the direction opposite to the visuomotor rotation. To assess learning, all subjects were retested 24 hr later on the visuomotor rotation, and their performance was compared with a control group exposed only to the visuomotor rotation on both days. Adapting to the position-dependent force field, both with and without visual feedback, impaired learning of the visuomotor rotation. Thus, interference between our kinematic and dynamic transformations was observed, suggesting that the key determinant of interference is the kinematic variable on which the transformation depends.

Adolescent↗
PubMed2002

Controlling the statistics of action: obstacle avoidance.

Task optimization in the presence of signal-dependent noise (TOPS) has been proposed as a general framework for planning goal-directed movements. Within this framework, the motor command is assumed to be corrupted by signal-dependent noise, which leads to a distribution of possible movements. A task can then be equated with optimizing some function of the statistics of this distribution. We found the optimal trajectory for obstacle avoidance by minimizing the mean-squared error at the end of the movement while keeping the probability of collision with the obstacle below a fixed limit. The optimal paths accurately predicted the empirical trajectories. This demonstrates that controlling the statistics of movements in the presence of signal-dependent noise may be a fundamental and unifying principle of goal-directed movements.

Avoidance Learning↗
PubMed2002

Adaptation to a visuomotor shift depends on the starting posture.

Previous studies have shown that human subjects can adapt to a new visuomotor relationship that depends on the trajectory of the arm. However, these studies have not distinguished between hand- and joint-based learning models. We have examined whether different endpoint kinematics are necessary to obtain a differential visuomotor shift. The joint trajectory was varied by changing the initial posture, while maintaining a similar finger trajectory. After learning, maximum after-effects were found when movement began with the posture used during exposure to the visuomotor shift and decreased with the difference between initial and trained posture. This was shown to be independent of the final posture attained. Our results show that adaptation to a visual remapping cannot be due to the recoding of a desired final posture and depends on the arm trajectory in joint space.

Adult↗
PubMed2002

Sources of signal-dependent noise during isometric force production.

It has been proposed that the invariant kinematics observed during goal-directed movements result from reducing the consequences of signal-dependent noise (SDN) on motor output. The purpose of this study was to investigate the presence of SDN during isometric force production and determine how central and peripheral components contribute to this feature of motor control. Peripheral and central components were distinguished experimentally by comparing voluntary contractions to those elicited by electrical stimulation of the extensor pollicis longus muscle. To determine other factors of motor-unit physiology that may contribute to SDN, a model was constructed and its output compared with the empirical data. SDN was evident in voluntary isometric contractions as a linear scaling of force variability (SD) with respect to the mean force level. However, during electrically stimulated contractions to the same force levels, the variability remained constant over the same range of mean forces. When the subjects were asked to combine voluntary with stimulation-induced contractions, the linear scaling relationship between the SD and mean force returned. The modeling results highlight that much of the basic physiological organization of the motor-unit pool, such as range of twitch amplitudes and range of recruitment thresholds, biases force output to exhibit linearly scaled SDN. This is in contrast to the square root scaling of variability with mean force present in any individual motor-unit of the pool. Orderly recruitment by twitch amplitude was a necessary condition for producing linearly scaled SDN. Surprisingly, the scaling of SDN was independent of the variability of motoneuron firing and therefore by inference, independent of presynaptic noise in the motor command. We conclude that the linear scaling of SDN during voluntary isometric contractions is a natural by-product of the organization of the motor-unit pool that does not depend on signal-dependent noise in the motor command. Synaptic noise in the motor command and common drive, which give rise to the variability and synchronization of motoneuron spiking, determine the magnitude of the force variability at a given level of mean force output.

Adult↗