Internal Representations for the Generalization of Motor Memories

Movement and memory are two of the most fundamental components of our existence. From the moment of birth, we rely on a variety of movements to interact with people and objects around us, and as we grow, we continuously form new motor memories to improve the fidelity of these interactions by exploring and learning more about our environment, especially in unfamiliar situations, ultimately becoming better equipped to handle novel and unknown environments. In this dissertation, we explore four facets of motor memory associated with voluntary movement and postural control in the upper limbs: (1) Optimal motor memory formation via sensorimotor integration. We ask whether the motor system combines prior memories with new sensory information to produce statistically-optimal weight estimates. We find that the weight estimate that the motor system makes in order to re-stabilize one’s arm posture when an object is rapidly removed from the hand that supports it, reflected information integration in a Bayesian, statistically-optimal fashion. Remarkably, we demonstrate that when experiencing the well-known size-weight illusion, the motor and perceptual system’s weight estimates are biased in opposite directions, suggesting two divergent modes for information integration within the central nervous system. (2) Movement features important for the learning and generalization of motor memories. We show that, velocity-dependent adaptation generalizes across different movements, even from discrete straight point-to-point to continuous circular movements, however the amount of generalization is limited and context-dependent. In a series of experiments, we quantified the contributions of different movement features to the elicited adaptation transfer. In particular, we show that other movement states (i.e. position and acceleration) make only minor contributions whereas, the contexts provided by movement geometry and movement continuity are critical. (3) Internal representation of motor memories in intrinsic-extrinsic coordinates. We show that motor memories are based not on fully intrinsic or extrinsic representations but on a gain-field (multiplicative) combination the two. This gain-field representation generalizes between actions by effectively computing movement similarity based on the Mahalanobis distance across both intrinsic and extrinsic coordinates, in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices. (4) Motor memories with local and global generalization. We demonstrate the existence of two distinct components of motor memory displaying different generalization footprints: One generalizes only locally, around the trained movement direction and with the trained end-effector, whereas the other generalizes broadly across both., We proceed to show that broad generalization results from a rapidly-learning adaptive process, dominates on easier-to-learn tasks, and performs high-level processing, producing adaptation vectors that integrate multiple sources of information, in line with a recent theory for perceptual learning.Engineering and Applied Science

Design and implementation of nanoscale fiber mechanical testing apparatus

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (leaf 26).The rapid growth in the synthetic manufacturing industry demands higher resolution mechanical testing devices, capable of working with nanoscale fibers. A new device has been developed to perform single-axis tensile tests on fibers with diameter ranging from 50 nm to 10 [micro]m. The device is capable of performing simple extension tests to determine the fiber's strength as well as high-frequency dynamic tests to look at fiber recovery rates, dampening, and fatigue. The force resolution obtained using a quartz strain gauge and a Zeaman interferometer was in the order of 1 nN and the forces measured by the instrument ranged over 10 orders of magnitude. This paper will present the design the Nanofiber tester, which offered better performance than any currently available commercial instruments and will discuss the subtleties around the implementation of the instrument, which is yet to be completed.by Jordan Brayanov.S.B

Bayesian and "Anti-Bayesian" Biases in Sensory Integration for Action and Perception in the Size-Weight Illusion

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.Engineering and Applied Science

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson’s Disease

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson’s disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies

Touching Curvature and Feeling Size: A Contrast Illusion

Plaisier M, Ernst MO. Touching Curvature and Feeling Size: A Contrast Illusion. Multisensory Research. 2013;26(5):457-463.We know that our eyes can be deceiving. Here we demonstrate that we should not always trust our sense of touch either. Previous studies have shown that when pinching an object between thumb and index finger, we can under many circumstances accurately perceive its size. In contrast, the current results show that the local curvature at the areas of contact between the object and the fingers causes systematic under- or overestimation of the object’s size. This is rather surprising given that local curvature is not directly related to the object’s size. We suggest an explanation in terms of a contrast between the finger separation and an inferred relationship between local curvature and size. This study provides the first demonstration of an illusory haptic size percept caused by local curvature in a pinch grip

The Binding of Learning to Action in Motor Adaptation

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.Alfred P. Sloan FoundationMcKnight Endowment Fund for Neuroscienc

Inferring Visuomotor Priors for Sensorimotor Learning

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations – the mapping between actual and visual location of the hand – during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior