Person:

Smith, Maurice

In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned 'credit' for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use.

When the error signals that guide human motor learning are withheld following training, recently-learned motor memories systematically regress toward untrained performance. It has previously been hypothesized that this regression results from an intrinsic volatility in these memories, resulting in an inevitable decay in the absence of ongoing error signals. However, a recently-proposed alternative posits that even recently-acquired motor memories are intrinsically stable, decaying only if a change in context is detected. This new theory, the context-dependent decay hypothesis, makes two key predictions: (1) after error signals are withheld, decay onset should be systematically delayed until the context change is detected; and (2) manipulations that impair detection by masking context changes should result in prolonged delays in decay onset and reduced decay amplitude at any given time. Here we examine the decay of motor adaptation following the learning of novel environmental dynamics in order to carefully evaluate this hypothesis. To account for potential issues in previous work that supported the context-dependent decay hypothesis, we measured decay using a balanced and baseline-referenced experimental design that allowed for direct comparisons between analogous masked and unmasked context changes. Using both an unbiased variant of the previous decay onset analysis and a novel highly-powered group-level version of this analysis, we found no evidence for systematically delayed decay onset nor for the masked context change affecting decay amplitude or its onset time. We further show how previous estimates of decay onset latency can be substantially biased in the presence of noise, and even more so with correlated noise, explaining the discrepancy between the previous results and our findings. Our results suggest that the decay of motor memories is an intrinsic feature of error-based learning that does not depend on context change detection.

Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound) if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates.

Adaptability of reaching movements depends on a computation in the brain that transforms sensory cues, such as those that indicate the position and velocity of the arm, into motor commands. Theoretical consideration shows that the encoding properties of neural elements implementing this transformation dictate how errors should generalize from one limb position and velocity to another. To estimate how sensory cues are encoded by these neural elements, we designed experiments that quantified spatial generalization in environments where forces depended on both position and velocity of the limb. The patterns of error generalization suggest that the neural elements that compute the transformation encode limb position and velocity in intrinsic coordinates via a gain-field; i.e., the elements have directionally dependent tuning that is modulated monotonically with limb position. The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace. Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones. We confirmed these predictions experimentally.

The aim of this paper was to determine the correlation between calcium burden (expressed as a volume) and extent of stenosis of the origin of the internal carotid artery (ICA) by CT angiography (CTA). Previous studies have shown that calcification in the coronary arteries correlates with significant vessel stenosis, and severe calcification (measured by CT) in the carotid siphon correlates with significant (greater than 50% stenosis) as determined angiographically. Sixty-one patients (age range 50–85 years) underwent CT of the neck with intravenous administration of iodinated contrast for a variety of conditions. Images were obtained with a helical multidetector array CT scanner and reviewed on a three-dimensional workstation. A single observer manipulated window and level to segment calcified plaque from vascular enhancement in order to quantify vascular calcium volume (cc) in the region of the bifurcation of the common carotid artery/ICA origin, and to measure the extent of ICA stenosis near the origin. A total of 117 common carotid artery bifurcations were reviewed. A significant stenosis was defined arbitrarily as >40% (to detect lesions before they become hemodynamically significant) of luminal diameter on CTA using NASCET-like criteria. All significant stenoses (21 out of 117 carotid bifurcations) had measurable calcium. We found a relatively strong correlation between percent stenosis and the calcium volume (Pearsons r = 0.65, P<0.0001). We also found that there was an even stronger correlation between the square root of the calcium volume and the percent stenosis as measured by CTA (r= 0.77, P<0.0001). Calcium volumes of 0.01, 0.03, 0.06, 0.09 and 0.12 cc were used as thresholds to evaluate for a significant stenosis. A receiver operating characteristic (ROC) curve demonstrated that thresholds of 0.06 cc (sensitivity 88%, specificity 87%) and 0.03 cc (sensitivity 94%, specificity 76%) generated the best combinations of sensitivity and specificity. Hence, this preliminary study demonstrates a relatively strong relationship between volume of calcium at the carotid bifurcation in the neck (measured by CT) and percent stenosis of the ICA below the skull base (as measured by CTA). Use of calcium volume measurements as a threshold may be both sensitive and specific for the detection of significant ICA stenosis. The significance of the correlation between calcium volume and ICA stenosis is that potentially a score can be obtained that will identify those at risk for high grade carotid stenosis.

Which is heavier: a pound of lead or a pound of feathers? This classic trick question belies a simple but surprising truth: when lifted, the pound of lead feels heavier—a phenomenon known as the size–weight illusion. To estimate the weight of an object, our CNS combines two imperfect sources of information: a prior expectation, based on the object's appearance, and direct sensory information from lifting it. Bayes' theorem (or Bayes' law) defines the statistically optimal way to combine multiple information sources for maximally accurate estimation. Here we asked whether the mechanisms for combining these information sources produce statistically optimal weight estimates for both perceptions and actions. We first studied the ability of subjects to hold one hand steady when the other removed an object from it, under conditions in which sensory information about the object's weight sometimes conflicted with prior expectations based on its size. Since the ability to steady the supporting hand depends on the generation of a motor command that accounts for lift timing and object weight, hand motion can be used to gauge biases in weight estimation by the motor system. We found that these motor system weight estimates reflected the integration of prior expectations with real-time proprioceptive information in a Bayesian, statistically optimal fashion that discounted unexpected sensory information. This produces a motor size–weight illusion that consistently biases weight estimates toward prior expectations. In contrast, when subjects compared the weights of two objects, their perceptions defied Bayes' law, exaggerating the value of unexpected sensory information. This produces a perceptual size–weight illusion that biases weight perceptions away from prior expectations. We term this effect “anti-Bayesian” because the bias is opposite that seen in Bayesian integration. Our findings suggest that two fundamentally different strategies for the integration of prior expectations with sensory information coexist in the nervous system for weight estimation.

The motor commands required to control voluntary movements under various environmental conditions may be formed by adaptively combining a fixed set of motor primitives. Since this motor output must contend with state-dependent physical dynamics during movement, these primitives are thought to depend on the position and velocity of motion. Using a recently developed ‘‘error-clamp’’ technique, we measured the fine temporal structure of changes in motor output during adaptation. Interestingly, these measurements reveal that motor primitives echo a key feature of the neural coding of limb motion— correlated tuning to position and velocity. We show that this correlated tuning explains why initial stages of motor learning are often rapid and stereotyped, whereas later stages are slower and stimulus specific. With our new understanding of these primitives, we design dynamic environments that are intrinsically the easiest or most difficult to learn, suggesting a theoretical basis for the rational design of improved procedures for motor training and rehabilitation.

Any passive rigid inertial object that we hold in our hand, e.g., a tennis racquet, imposes a field of forces on the arm that depends on limb position, velocity, and acceleration. A fundamental characteristic of this field is that the forces due to acceleration and velocity are linearly separable in the intrinsic coordinates of the limb. In order to learn such dynamics with a collection of basis elements, a control system would generalize correctly and therefore perform optimally if the basis elements that were sensitive to limb velocity were not sensitive to acceleration, and vice versa. However, in the mammalian nervous system proprioceptive sensors like muscle spindles encode a nonlinear combination of all components of limb state, with sensitivity to velocity dominating sensitivity to acceleration. Therefore, limb state in the space of proprioception is not linearly separable despite the fact that this separation is a desirable property of control systems that form models of inertial objects. In building internal models of limb dynamics, does the brain use a representation that is optimal for control of inertial objects, or a representation that is closely tied to how peripheral sensors measure limb state? Here we show that in humans, patterns of generalization of reaching movements in acceleration-dependent fields are strongly inconsistent with basis elements that are optimized for control of inertial objects. Unlike a robot controller that models the dynamics of the natural world and represents velocity and acceleration independently, internal models of dynamics that people learn appear to be rooted in the properties of proprioception, nonlinearly responding to the pattern of muscle activation and representing velocity more strongly than acceleration.

Two different compensatory mechanisms are engaged when the nervous system senses errors during a reaching movement. First, on-line feedback control mechanisms produce in-flight corrections to reduce errors in the on-going movement. Second, these errors modify the internal model with which the motor plan is transformed into motor commands for the subsequent movements. What are the neural mechanisms of these compensatory systems? In a previous study, we reported that while on-line error correction was disturbed in patients with Huntington's disease (HD), it was largely intact in patients with cerebellar degeneration. Here we altered dynamics of reaching and studied the effect of error in one trial on the motor commands that initiated the subsequent trial. We observed that in patients with cerebellar degeneration, motor commands changed from trial-to-trial by an amount that was comparable to control subjects. However, these changes were random and were uninformed by the error in the preceding trial. In contrast, the change in motor commands of HD patients was strongly related to the error in the preceding trial. This error-dependent change had a sensitivity that was comparable to healthy controls. As a result, HD patients exhibited no significant deficits in adapting to novel arm dynamics, whereas cerebellar subjects were profoundly impaired. These results demonstrate a double dissociation between on-line and trial-to-trial error correction suggesting that these compensatory mechanisms have distinct neural bases that can be differentially affected by disease.