Motor adaptation is usually defined as the process by which our nervous system produces accurate movements, while the properties of our bodies and our environment continuously change. Numerous experimental and theoretical studies have characterized this process by assuming that the nervous system uses internal models to compensate for motor errors. Here we extend these approaches and construct a probabilistic model that not only compensates for motor errors but estimates the sources of these errors. These estimates dictate how the nervous system should generalize. For example, estimated changes of limb properties will affect movements across the workspace but not movements with the other limb. We provide evidence that many movement generalization phenomena emerge from a strategy by which the nervous system estimates the sources of our motor errors.

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Our nervous system continuously combines new information from our senses with information it has acquired throughout life. Numerous studies have found that human subjects manage this by integrating their observations with their previous experience (priors) in a way that is close to the statistical optimum. However, little is known about the way the nervous system acquires or learns priors. Here we present results from experiments where the underlying distribution of target locations in an estimation task was switched, manipulating the prior subjects should use. Our experimental design allowed us to measure a subject's evolving prior while they learned. We confirm that through extensive practice subjects learn the correct prior for the task. We found that subjects can rapidly learn the mean of a new prior while the variance is learned more slowly and with a variable learning rate. In addition, we found that a Bayesian inference model could predict the time course of the observed learning while offering an intuitive explanation for the findings. The evidence suggests the nervous system continuously updates its priors to enable efficient behavior.

The basic hypothesis of producing a range of behaviors using a small set of motor commands has been proposed in various forms to explain motor behaviors ranging from basic reflexes to complex voluntary movements. Yet many fundamental questions regarding this long-standing hypothesis remain unanswered. Indeed, given the prominent nonlinearities and high dimensionality inherent in the control of biological limbs, the basic feasibility of a lowdimensional controller and an underlying principle for its creation has remained elusive. We propose a principle for the design of such a controller, that it endeavors to control the natural dynamics of the limb, taking into account the nature of the task being performed. Using this principle, we obtained a low-dimensional model of the hindlimb and a set of muscle synergies to command it. We demonstrate that this set of synergies was capable of producing effective control, establishing the viability of this muscle synergy hypothesis. Finally, by combining the low-dimensional model and the muscle synergies we were able to build a relatively simple controller whose overall performance was close to that of the system's full-dimensional nonlinear controller. Taken together, the results of this study establish that a low-dimensional controller is capable of simplifying control without degrading performance.low-dimensional ͉ optimal control ͉ muscle pattern ͉ frog ͉ computational model C ontrolling any movement, whether it be a stereotyped reflex or a sophisticated skill, is highly complex. Fundamentally, every movement requires the detailed specification of a vast number of variables, potentially involving many thousands of motor units distributed throughout the limbs and body. Further, the relationship between these variables and the intended motion of the body is nontrivial, dictated by the intricate nonlinear dynamics of the musculoskeletal system. Elucidating control strategies that can overcome these complexities is a central issue in the neural control of movement.Many investigators have suggested that the central nervous system (CNS) might have developed strategies to simplify the control of movement (1-6). According to one common proposal, the CNS might produce movement through the flexible combination of ''muscle synergies,'' with each such synergy specifying a particular balance of activation across a set of muscles (7-16). By reducing the number of controlled variables, such a lowdimensional control strategy would simplify the production of movement.Although many experiments have found evidence to suggest that many behaviors can be produced through combinations of muscle synergies, several questions concerning this hypothesis remain unresolved. Foremost among these questions is a proof of the concept's viability: can a low-dimensional control scheme based on muscle synergies reproduce the range of observed behaviors with negligible loss of efficacy? Given the nonlinearities and high dimensionality inherent in biological motor control, the answer to this question is not ...

Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.g., force fields) are represented in intrinsic, joint-based coordinates (Shadmehr R, Mussa-Ivaldi FA. J Neurosci 14: 3208–3224, 1994), recent evidence has questioned this proposal. Here we reexamine the representation of dynamics in two experiments. By testing generalization following changes in shoulder, elbow, or wrist configurations, the first experiment tested for extrinsic, intrinsic, or object-centered representations. No single coordinate frame accounted for the pattern of generalization. Rather, generalization patterns were better accounted for by a mixture of representations or by models that assumed local learning and graded, decaying generalization. A second experiment, in which we replicated the design of an influential study that had suggested encoding in intrinsic coordinates (Shadmehr and Mussa-Ivaldi 1994), yielded similar results. That is, we could not find evidence that dynamics are represented in a single coordinate system. Taken together, our experiments suggest that internal models do not employ a single coordinate system when generalizing and may well be represented as a mixture of coordinate systems, as a single system with local learning, or both.

How to move efficiently is an optimal control problem, whose computational complexity grows exponentially with the horizon of the planned trajectory. Breaking a compound movement into a series of chunks, each planned over a shorter horizon can thus reduce the overall computational complexity and associated costs while limiting the achievable efficiency. This trade-off suggests a cost-effective learning strategy: to learn new movements we should start with many short chunks (to limit the cost of computation). As practice reduces the impediments to more complex computation, the chunking structure should evolve to allow progressively more efficient movements (to maximize efficiency). Here we show that monkeys learning a reaching sequence over an extended period of time adopt this strategy by performing movements that can be described as locally optimal trajectories. Chunking can thus be understood as a cost-effective strategy for producing and learning efficient movements.

Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance. We formalize this idea with a model that infers not only the sources of potential motor errors, but also their relevance to the current motor circumstances. In our model adaptation is the process of re-estimating parameters that represent the body and the world. The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance. As such, adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them, even after motor performance has returned to baseline. Estimates of this external disturbance should be relevant both now and in future laboratory settings. Estimated properties of our bodies on the other hand should always be relevant. Our model demonstrates savings, interference, spontaneous rebound and differences between adaptation to sudden and gradual disturbances. We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters.

The processing of sensory information is fundamental to the basic operation of the nervous system. Our nervous system uses this sensory information to gain knowledge of our bodies and the world around us. This knowledge is of great importance as it provides the coherent and accurate information necessary for successful motor control. Yet, all this knowledge is of an uncertain nature because we obtain information only through our noisy sensors. We are thus faced with the problem of integrating many uncertain pieces of information into estimates of the properties of our bodies and the surrounding world. Bayesian approaches to estimation formalize the problem of how this uncertain information should be integrated. Utilizing this approach, many studies make predictions that faithfully predict human sensorimotor behavior. WIREs Cogni Sci 2011 2 419-428 DOI: 10.1002/wcs.125 For further resources related to this article, please visit the WIREs website.