Tracking the Mechanisms of Short-Term Motor Adaptation within the Framework of a Two-State Model

The motor system is continuously monitoring our performance, ensuring that our actions are occurring as planned. Sensory prediction errors, which arise from a discrepancy between the expected and actual sensory consequence of a motor command (i.e., a planned action), are assumed to drive sensorimotor adaptation. Sensorimotor adaptation is thought to involve changes in motor output that allow the motor system to regain its former level of performance in perturbed circumstances. We employed experimental paradigms that involved both mechanical and visual perturbations to evoke sensory prediction errors while participants performed planar reaching movements. Movement error activates learning processes in the brain, which alter our behaviour in the future. A prominent model of short-term adaptation is built upon the theory that there appear to be at least two processes of varying timescales operating together as humans learn to counteract sensorimotor disturbances: a fast process that learns to reduce errors quickly but also quickly forgets, and a slow process that learns to reduce errors slowly but slowly forgets. The purpose of this dissertation was to track the mechanisms of short-term motor adaptation within the framework of a two-state model. Collectively, our three studies reinforce the hypothesis that short-term sensorimotor adaptation, occurring over short time scales (e.g., over a period of minutes), is supported by at least two underlying processes. Substantiated by our first and third study, we have shown that both the fast and slow adaptation processes are responsive to a history of error and both contribute to savings. The motor system receives sensory feedback about both the environment and the body on a continual basis, in addition to predictive feedforward commands. How feedback gains are changed can vary greatly, based on the state of the body and environment, as well as the behavioural context of learning. It has been routinely suggested that adaptation in response to a perturbation, results in a gradual shift over the course of error-reduction from a feedback-driven mode of control to more predictive, feedforward control. Based on the results of our second study, we demonstrate that the fast process of feedforward adaptation parallels the modulation in gain of the feedback response over the course of learning to counter a force field perturbation. We propose that the fast process, estimated from overall learning, may alternatively be an identification of the feedback controller, while the slow process is the recalibrated forward model. And lastly, while unpacking the result of our third study we further suggest that it is the slow process which stores a memory component from prior training which is then later accessed by both processes during subsequent learning

The gradient of the reinforcement landscape influences sensorimotor learning

© 2019 Cashaback et al. Consideration of previous successes and failures is essential to mastering a motor skill. Much of what we know about how humans and animals learn from such reinforcement feedback comes from experiments that involve sampling from a small number of discrete actions. Yet, it is less understood how we learn through reinforcement feedback when sampling from a continuous set of possible actions. Navigating a continuous set of possible actions likely requires using gradient information to maximize success. Here we addressed how humans adapt the aim of their hand when experiencing reinforcement feedback that was associated with a continuous set of possible actions. Specifically, we manipulated the change in the probability of reward given a change in motor action-the reinforcement gradient-to study its influence on learning. We found that participants learned faster when exposed to a steep gradient compared to a shallow gradient. Further, when initially positioned between a steep and a shallow gradient that rose in opposite directions, participants were more likely to ascend the steep gradient. We introduce a model that captures our results and several features of motor learning. Taken together, our work suggests that the sensorimotor system relies on temporally recent and spatially local gradient information to drive learning

The genetic architecture of helminth-specific immune responses in a wild population of Soay sheep (Ovis aries)

Much of our knowledge of the drivers of immune variation, and how these responses vary over time, comes from humans, domesticated livestock or laboratory organisms. While the genetic basis of variation in immune responses have been investigated in these systems, there is a poor understanding of how genetic variation influences immunity in natural, untreated populations living in complex environments. Here, we examine the genetic architecture of variation in immune traits in the Soay sheep of St Kilda, an unmanaged population of sheep infected with strongyle gastrointestinal nematodes. We assayed IgA, IgE and IgG antibodies against the prevalent nematode Teladorsagia circumcincta in the blood plasma of > 3,000 sheep collected over 26 years. Antibody levels were significantly heritable (h2 = 0.21 to 0.57) and highly stable over an individual’s lifespan. IgA levels were strongly associated with a region on chromosome 24 explaining 21.1% and 24.5% of heritable variation in lambs and adults, respectively. This region was adjacent to two candidate loci, Class II Major Histocompatibility Complex Transactivator (CIITA) and C-Type Lectin Domain Containing 16A (CLEC16A). Lamb IgA levels were also associated with the immunoglobulin heavy constant loci (IGH) complex, and adult IgE levels and lamb IgA and IgG levels were associated with the major histocompatibility complex (MHC). This study provides evidence of high heritability of a complex immunological trait under natural conditions and provides the first evidence from a genome-wide study that large effect genes located outside the MHC region exist for immune traits in the wild

Time course of changes in the long-latency feedback response parallels the fast process of short-term motor adaptation

© 2020 American Physiological Society. All rights reserved. Time course of changes in the longlatency feedback response parallels the fast process of short-term motor adaptation Adapting to novel dynamics involves modifying both feedforward and feedback control. We investigated whether the motor system alters feedback responses during adaptation to a novel force field in a manner similar to adjustments in feedforward control. We simultaneously tracked the time course of both feedforward and feedback systems via independent probes during a force field adaptation task. Participants (n 35) grasped the handle of a robotic manipulandum and performed reaches to a visual target while the hand and arm were occluded. We introduced an abrupt counterclockwise velocity-dependent force field during a block of reaching trials. We measured movement kinematics and shoulder and elbow muscle activity with surface EMG electrodes. We tracked the feedback stretch response throughout the task. Using force channel trials, we measured overall learning, which was later decomposed into a fast and slow process. We found that the longlatency feedback response (LLFR) was upregulated in the early stages of learning and was correlated with the fast component of feedforward adaptation. The change in feedback response was specific to the long-latency epoch (50-100 ms after muscle stretch) and was observed only in the triceps muscle, which was the muscle required to counter the force field during adaptation. The similarity in time course for the LLFR and the estimated time course of the fast process suggests both are supported by common neural circuits. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller. NEW & NOTEWORTHY We investigated whether changes in the feedback stretch response were related to the proposed fast and slow processes of motor adaptation. We found that the long-latency component of the feedback stretch response was upregulated in the early stages of learning and the time course was correlated with the fast process. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller

Both fast and slow learning processes contribute to savings following sensorimotor adaptation

© 2019 the American Physiological Society. Recent work suggests that the rate of learning in sensorimotor adaptation is likely not fixed, but rather can change based on previous experience. One example is savings, a commonly observed phenomenon whereby the relearning of a motor skill is faster than the initial learning. Sensorimotor adaptation is thought to be driven by sensory prediction errors, which are the result of a mismatch between predicted and actual sensory consequences. It has been proposed that during motor adaptation the generation of sensory prediction errors engages two processes (fast and slow) that differ in learning and retention rates. We tested the idea that a history of errors would influence both the fast and slow processes during savings. Participants were asked to perform the same force field adaptation task twice in succession. We found that adaptation to the force field a second time led to increases in estimated learning rates for both fast and slow processes. While it has been proposed that savings is explained by an increase in learning rate for the fast process, here we observed that the slow process also contributes to savings. Our work suggests that fast and slow adaptation processes are both responsive to a history of error and both contribute to savings. NEW & NOTEWORTHY We studied the underlying mechanisms of savings during motor adaptation. Using a two-state model to represent fast and slow processes that contribute to motor adaptation, we found that a history of error modulates performance in both processes. While previous research has attributed savings to only changes in the fast process, we demonstrated that an increase in both processes is needed to account for the measured behavioral data

Sensitivity to error during visuomotor adaptation is similarly modulated by abrupt, gradual, and random perturbation schedules

Item does not contain fulltextIt has been suggested that sensorimotor adaptation involves at least two processes (i.e., fast and slow) that differ in retention and error sensitivity. Previous work has shown that repeated exposure to an abrupt force field perturbation results in greater error sensitivity for both the fast and slow processes. While this implies that the faster relearning is associated with increased error sensitivity, it remains unclear what aspects of prior experience modulate error sensitivity. In the present study, we manipulated the initial training using different perturbation schedules, thought to differentially affect fast and slow learning processes based on error magnitude, and then observed what effect prior learning had on subsequent adaptation. During initial training of a visuomotor rotation task, we exposed three groups of participants to either an abrupt, a gradual, or a random perturbation schedule. During a testing session, all three groups were subsequently exposed to an abrupt perturbation schedule. Comparing the two sessions of the control group who experienced repetition of the same perturbation, we found an increased error sensitivity for both processes. We found that the error sensitivity was increased for both the fast and slow processes, with no reliable changes in the retention, for both the gradual and structural learning groups when compared to the first session of the control group. We discuss the findings in the context of how fast and slow learning processes respond to a history of errors.12 p