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Brayanov, Jordan

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.

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.