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Biomedical subjects

Daniel M Wolpert

Publications and source records attributed to Daniel M Wolpert.

At least 19 recordsLinked to original sources
PubMed2005

Where does your own action influence your perception of another person's action in the brain?

Activation of premotor cortex during the observation and imitation of human actions is now increasingly accepted, but it remains unclear how the CNS is able to resolve potential conflicts between the observation of another person's action and the ongoing control of one's own action. Recent data suggest that this overlap leads to a systematic bias, where lifting a box influences participant's perceptual judgments of the weight of a box lifted by another person. We now investigate the neural basis of this bias effect using fMRI. Seventeen participants performed a perceptual weight judgment task or two control conditions while lifting a light box, a heavy box or no box during scanning. Brain regions related to perceptual bias were localized by correlating individual differences in bias with BOLD signal. Five regions were found to show correlations with psychophysical bias: left inferior frontal gyrus, left central sulcus, left extrastriate body area, left lingual gyrus and right intraparietal sulcus. The cluster in primary motor cortex was also activated by box lifting, and the cluster in extrastriate body area by the observation of hand actions and the weight judgment task. We suggest that these brain areas are part of a network where motor processing modulates perceptual judgment of observed human actions, and thus visual and motor processes cannot be thought of as two distinct systems, but instead interact at many levels.

Adolescent↗
PubMed2005

Optimal control of redundant muscles in step-tracking wrist movements.

An important question in motor neuroscience is how the nervous system controls the spatiotemporal activation patterns of redundant muscles in generating accurate movements. The redundant muscles may not only underlie the flexibility of our movements but also pose the challenging problem of how to select a specific sequence of muscle activation from the huge number of possible activations. Here, we propose that noise in the motor command that has an influence on task achievement should be considered in determining the optimal motor commands over redundant muscles. We propose an optimal control model for step-tracking wrist movements with redundant muscles that minimizes the end-point variance under signal-dependent noise. Step-tracking wrist movements of human and nonhuman primates provide a detailed data set to investigate the control mechanisms in movements with redundant muscles. The experimental EMG data can be summarized by two eminent features: 1) amplitude-graded EMG pattern, where the timing of the activity of the agonist and antagonist bursts show slight variations with changes in movement directions, and only the amplitude of activity is modulated; and 2) cosine tuning for movement directions exhibited by the agonist and antagonist bursts, and the discrepancy found between a muscle's agonist preferred direction and its pulling direction. In addition, it is also an important observation that subjects often overshoot the target. We demonstrate that the proposed model captures not only the spatiotemporal activation patterns of wrist muscles but also trajectory overshooting. This suggests that when recruiting redundant muscles, the nervous system may optimize the motor commands across the muscles to reduce the negative effects of motor noise.

Biomechanical Phenomena↗
PubMed2005

Perception of the consequences of self-action is temporally tuned and event driven.

It has been proposed that in order to increase the salience of sensations with an external cause, sensations that are predictable based on one's own actions are attenuated [1 and 2]. This may explain why self-imposed tickle [3 and 4] or constant forces [5] are perceived as less intense than the same stimuli externally imposed. Here, subjects used their right index finger to tap a force sensor mounted above their left index finger. When a motor generated a tap on the left finger synchronously with the right tap, simulating contact between the fingers, the perception of force in the left finger was attenuated compared to the same tap experienced during rest. Attenuation gradually reduced as the left tap was either delayed or advanced relative to the active right tap. However, no attenuation was seen to left taps triggered by right-finger movements that stopped above or passed wide of the sensor. We conclude that there is a window of sensory attenuation that is broadly temporally tuned and centered on the time at which the fingers would normally make contact. That is, predictive tactile sensory attenuation is linked to specific external events arising from movement rather than to the movement per se.

Adolescent↗
PubMed2005

Common encoding of novel dynamic loads applied to the hand and arm.

In manual action, the relationship between a given motor command and the ensuing movement depends on the dynamics of both the arm and hand-held objects. Skilled performance relies on the brain learning both these dynamics, and previous studies have examined how people adapt to novel loads applied to either the hand or the arm. In this study, we ask whether these different kinds of load are represented independently as a result of changes in cutaneous feedback and hand-arm coordination. We used a robotic apparatus that could either apply forces to an object held in the subject's hand or directly to the segments of the arm. We tested whether subjects could retain learning of a force field applied to the hand after subsequently experiencing the opposing field applied to the arm (or vice versa), or whether retrograde interference would be observed. In separate experiments, we used force fields and torque fields that were linearly related to either hand or joint velocities, respectively. Our finding of complete interference between opposing fields suggests that loads applied to the arm and hand are not represented independently by the sensorimotor system. This interference occurred despite markedly different cutaneous inputs that were directly related to the movement task. This result suggests that the brain represents dynamics independently of these sensory inputs. In addition, we found that the rate at which subjects adapted to a given force field, specified either in hand or joint coordinates, was independent of whether the forces were applied to the hand or arm segments.

Adult↗
PubMed2005

Interference between velocity-dependent and position-dependent force-fields indicates that tasks depending on different kinematic parameters compete for motor working memory.

Humans demonstrate motor learning when exposed to changes in the dynamics of movement or changes in the visuomotor map. However, when two opposing dynamic transformations are learned in succession, the memory of the first is overwritten by learning of the second; the same is true for two opposing visuomotor rotations. This retrograde interference is not seen for all combinations of transformations, however. When a dynamic transformation is learned subsequent to a visuomotor rotation, the presence or absence of interference appears to depend crucially on the structure of the dynamic task: a force-field dependent on the position of the hand produces interference, whereas an inertial load applied lateral to the hand does not. To explain these results, it has been hypothesized that two transformations can be learned without interference if they depend on two different kinematic parameters of movement (such as position and velocity of the hand). Here we demonstrate, contrary to this hypothesis, interference between a dynamic transformation that depends on the position of the hand and one that depends on its velocity. However, the interference was found to be incomplete, supporting the view that the ability to retain motor memories for different tasks depends on the degree to which their representations conflict in working memory.

Adaptation, Physiological↗
PubMed2005

Activation in posterior superior temporal sulcus parallels parameter inducing the percept of animacy.

An essential, evolutionarily stable feature of brain function is the detection of animate entities, and one of the main cues to identify them is their movement. We developed a model of a simple interaction between two objects, in which an increase of the correlation between their movements varied the amount of interactivity and animacy observers attributed to them. Functional magnetic resonance imaging revealed that activation in the posterior superior temporal sulcus and gyrus (pSTS/pSTG) increased in relation to the degree of correlated motion between the two objects. This activation increase was not different when subjects performed an explicit or implicit task while observing these interacting objects. These data suggest that the pSTS and pSTG play a role in the automatic identification of animate entities, by responding directly to an objective movement characteristic inducing the percept of animacy, such as the amount of interactivity between two moving objects.

Adult↗
PubMed2004

Scaling down motor memories: de-adaptation after motor learning.

Although adaptation to novel motor tasks is sometimes a very slow process, de-adaptation is usually extremely rapid. Such rapid de-adaptation is seen in dynamic learning in which subjects can take hundreds of movements to learn a novel force environment but only a few movements to de-adapt back to a normal or "null" force environment. We investigated whether this effect is unique to the null environment or reveals a more general rapid adaptation mechanism by studying how subjects behave when their dynamic environment changes. We observed that after learning a dynamic force field, subjects took longer to de-adapt when the forces were turned off than to adapt to a novel scaled-down version of the experienced field. This demonstrates that rapid adaptation is not unique to the "null" force environment. Moreover, we examined subjects' ability to adapt from a learned field to either a scaled down field or to a field in which the sign of the forces changed. Even though in both conditions the required change in force output was identical, subjects were significantly faster at adapting to the scaled down field. The result suggests that rapid de-adaptation reflects a capacity to scale down the relative contribution of existing control modules to the motor output.

Adaptation, Physiological↗
PubMed2004

Failure to consolidate the consolidation theory of learning for sensorimotor adaptation tasks.

An influential idea in human motor learning is that there is a consolidation period during which motor memories are transformed from a fragile to a permanent state, no longer susceptible to interference from new learning. The evidence supporting this idea comes from studies showing that the motor memory of a task (A) is lost when an opposing task (B) is experienced soon after, but not if sufficient time is allowed to pass (approximately 6 hr). We report results from three laboratories challenging this consolidation idea. We used an ABA paradigm in the context of a reaching task to assess the influence of experiencing B after A on the retention of A. In two experiments using visuomotor rotations, we found that B fully interferes with the retention of A even when B is experienced 24 hr after A. Contrary to previous reports, in four experiments on learning force fields, we also observed full interference between A and B when they are separated by 24 hr or even 1 week. This latter result holds for both position-dependent and velocity-dependent force fields. For both the visuomotor and force-field tasks, complete interference is still observed when the possible affects of anterograde interference are controlled through the use of washout trials. Our results fail to support the idea that motor memories become consolidated into a protected state. Rather, they are consistent with recent ideas of memory formation, which propose that memories can shift between active and inactive states.

Adaptation, Physiological↗
PubMed2004

A neuroeconomics approach to inferring utility functions in sensorimotor control.

Making choices is a fundamental aspect of human life. For over a century experimental economists have characterized the decisions people make based on the concept of a utility function. This function increases with increasing desirability of the outcome, and people are assumed to make decisions so as to maximize utility. When utility depends on several variables, indifference curves arise that represent outcomes with identical utility that are therefore equally desirable. Whereas in economics utility is studied in terms of goods and services, the sensorimotor system may also have utility functions defining the desirability of various outcomes. Here, we investigate the indifference curves when subjects experience forces of varying magnitude and duration. Using a two-alternative forced-choice paradigm, in which subjects chose between different magnitude-duration profiles, we inferred the indifference curves and the utility function. Such a utility function defines, for example, whether subjects prefer to lift a 4-kg weight for 30 s or a 1-kg weight for a minute. The measured utility function depends nonlinearly on the force magnitude and duration and was remarkably conserved across subjects. This suggests that the utility function, a central concept in economics, may be applicable to the study of sensorimotor control.

Adult↗
PubMed2004

The loss function of sensorimotor learning.

Motor learning can be defined as changing performance so as to optimize some function of the task, such as accuracy. The measure of accuracy that is optimized is called a loss function and specifies how the CNS rates the relative success or cost of a particular movement outcome. Models of pointing in sensorimotor control and learning usually assume a quadratic loss function in which the mean squared error is minimized. Here we develop a technique for measuring the loss associated with errors. Subjects were required to perform a task while we experimentally controlled the skewness of the distribution of errors they experienced. Based on the change in the subjects' average performance, we infer the loss function. We show that people use a loss function in which the cost increases approximately quadratically with error for small errors and significantly less than quadratically for large errors. The system is thus robust to outliers. This suggests that models of sensorimotor control and learning that have assumed minimizing squared error are a good approximation but tend to penalize large errors excessively.

Adaptation, Physiological↗
PubMed2004

Bayesian integration in force estimation.

When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, termed prior information. Bayesian integration defines the way in which these two sources of information should be combined to produce an optimal estimate. To investigate whether subjects use such a strategy in force estimation, we designed a novel sensorimotor estimation task. We controlled the distribution of forces experienced over the course of an experiment thereby defining the prior. We show that subjects integrate sensory information with their prior experience to generate an estimate. Moreover, subjects could learn different prior distributions. These results suggest that the CNS uses Bayesian models when estimating force requirements.

Bayes Theorem↗
PubMed2004

The scaling of motor noise with muscle strength and motor unit number in humans.

Understanding the origin of noise, or variability, in the motor system is an important step towards understanding how accurate movements are performed. Variability of joint torque during voluntary activation is affected by many factors such as the precision of the descending motor commands, the number of muscles that cross the joint, their size and the number of motor units in each. To investigate the relationship between the peripheral factors and motor noise, the maximum voluntary torque produced at a joint and the coefficient of variation of joint torque were recorded from six adult human subjects for four muscle/joint groups in the arm. It was found that the coefficient of variation of torque decreases systematically as the maximum voluntary torque increases. This decreasing coefficient of variation means that a given torque or force can be more accurately generated by a stronger muscle than a weaker muscle. Simulations demonstrated that muscles with different strengths and different numbers of motor units could account for the experimental data. In the simulations, the magnitude of the coefficient of variation of muscle force depended primarily on the number of motor units innervating the muscle, which relates positively to muscle strength. This result can be generalised to the situation where more than one muscle is available to perform a task, and a muscle activation pattern must be selected. The optimal muscle activation pattern required to generate a target torque using a group of muscles, while minimizing the consequences of signal dependent noise, is derived.

Action Potentials↗
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↗