The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability.

The naturally occurring variability in our movements often poses a significant challenge when attempting to produce precise and accurate actions, which is readily evident when playing a game of darts. Two differing, yet potentially complementary, control strategies that the sensorimotor system may use to regulate movement variability are impedance control and feedback control. Greater muscular co-contraction leads to greater impedance that acts to stabilize the hand, while visuomotor feedback responses can be used to rapidly correct for unexpected deviations when reaching toward a target. Here, we examined the independent roles and potential interplay of impedance control and visuomotor feedback control when regulating movement variability. Participants were instructed to perform a precise reaching task by moving a cursor through a narrow visual channel. We manipulated cursor feedback by visually amplifying movement variability and/or delaying the visual feedback of the cursor. We found that participants decreased movement variability by increasing muscular co-contraction, aligned with an impedance control strategy. Participants displayed visuomotor feedback responses during the task but, unexpectedly, there was no modulation between conditions. However, we did find a relationship between muscular co-contraction and visuomotor feedback responses, suggesting that participants modulated impedance control relative to feedback control. Taken together, our results highlight that the sensorimotor system modulates muscular co-contraction, relative to visuomotor feedback responses, to regulate movement variability and produce accurate actions.NEW & NOTEWORTHY The sensorimotor system has the constant challenge of dealing with the naturally occurring variability in our movements. Here, we investigated the potential roles of muscular co-contraction and visuomotor feedback responses to regulate movement variability. When we visually amplified movements, we found that the sensorimotor system primarily uses muscular co-contraction to regulate movement variability. Interestingly, we found that muscular co-contraction was modulated relative to inherent visuomotor feedback responses, suggesting an interplay between impedance and feedback control.

The energetic basis for smooth human arm movements.

The central nervous system plans human reaching movements with stereotypically smooth kinematic trajectories and fairly consistent durations. Smoothness seems to be explained by accuracy as a primary movement objective, whereas duration seems to economize energy expenditure. But the current understanding of energy expenditure does not explain smoothness, so that two aspects of the same movement are governed by seemingly incompatible objectives. Here, we show that smoothness is actually economical, because humans expend more metabolic energy for jerkier motions. The proposed mechanism is an underappreciated cost proportional to the rate of muscle force production, for calcium transport to activate muscle. We experimentally tested that energy cost in humans (N = 10) performing bimanual reaches cyclically. The empirical cost was then demonstrated to predict smooth, discrete reaches, previously attributed to accuracy alone. A mechanistic, physiologically measurable, energy cost may therefore explain both smoothness and duration in terms of economy, and help resolve motor redundancy in reaching movements.

Increasing the gradient of energetic cost does not initiate adaptation in human walking.

When in a new situation, the nervous system may benefit from adapting its control policy. In determining whether or not to initiate this adaptation, the nervous system may rely on some features of the new situation. Here, we tested whether one such feature is salient cost savings. We changed cost saliency by manipulating the gradient of participants' energetic cost landscape during walking. We hypothesized that steeper gradients would cause participants to spontaneously adapt their step frequency to lower costs. To manipulate the gradient, a mechatronic system applied controlled fore-aft forces to the waist of participants as a function of their step frequency as they walked on a treadmill. These forces increased the energetic cost of walking at high step frequencies and reduced it at low step frequencies. We successfully created three cost landscapes of increasing gradients, where the natural variability in participants' step frequency provided cost changes of 3.6% (shallow), 7.2% (intermediate), and 10.2% (steep). Participants did not spontaneously initiate adaptation in response to any of the gradients. Using metronome-guided walking-a previously established protocol for eliciting initiation of adaptation-participants next experienced a step frequency with a lower cost. Participants then adapted by -1.41 ± 0.81 (P = 0.007) normalized units away from their originally preferred step frequency obtaining cost savings of 4.80% ± 3.12%. That participants would adapt under some conditions, but not in response to steeper cost gradients, suggests that the nervous system does not solely rely on the gradient of energetic cost to initiate adaptation in novel situations.NEW & NOTEWORTHY People can adapt to novel conditions but often require cues to initiate the adaptation. Using a mechatronic system to reshape energetic cost gradients during treadmill walking, we tested whether the nervous system can use information present in the cost gradient to spontaneously initiate adaptation. We found that our participants did not spontaneously initiate adaptation even in the steepest gradient. The nervous system does not rely solely on the cost gradient when initiating adaptation.

Ubiquitin receptors are required for substrate-mediated activation of the proteasome's unfolding ability.

The ubiquitin-proteasome system (UPS) is responsible for the bulk of protein degradation in eukaryotic cells, but the factors that cause different substrates to be unfolded and degraded to different extents are still poorly understood. We previously showed that polyubiquitinated substrates were degraded with greater processivity (with a higher tendency to be unfolded and degraded than released) than ubiquitin-independent substrates. Thus, even though ubiquitin chains are removed before unfolding and degradation occur, they affect the unfolding of a protein domain. How do ubiquitin chains activate the proteasome's unfolding ability? We investigated the roles of the three intrinsic proteasomal ubiquitin receptors - Rpn1, Rpn10 and Rpn13 - in this activation. We find that these receptors are required for substrate-mediated activation of the proteasome's unfolding ability. Rpn13 plays the largest role, but there is also partial redundancy between receptors. The architecture of substrate ubiquitination determines which receptors are needed for maximal unfolding ability, and, in some cases, simultaneous engagement of ubiquitin by multiple receptors may be required. Our results suggest physical models for how ubiquitin receptors communicate with the proteasomal motor proteins.

How humans initiate energy optimization and converge on their optimal gaits.

A central principle in motor control is that the coordination strategies learned by our nervous system are often optimal. Here, we combined human experiments with computational reinforcement learning models to study how the nervous system navigates possible movements to arrive at an optimal coordination. Our experiments used robotic exoskeletons to reshape the relationship between how participants walk and how much energy they consume. We found that while some participants used their relatively high natural gait variability to explore the new energetic landscape and spontaneously initiate energy optimization, most participants preferred to exploit their originally preferred, but now suboptimal, gait. We could nevertheless reliably initiate optimization in these exploiters by providing them with the experience of lower cost gaits, suggesting that the nervous system benefits from cues about the relevant dimensions along which to re-optimize its coordination. Once optimization was initiated, we found that the nervous system employed a local search process to converge on the new optimum gait over tens of seconds. Once optimization was completed, the nervous system learned to predict this new optimal gait and rapidly returned to it within a few steps if perturbed away. We then used our data to develop reinforcement learning models that can predict experimental behaviours, and applied these models to inductively reason about how the nervous system optimizes coordination. We conclude that the nervous system optimizes for energy using a prediction of the optimal gait, and then refines this prediction with the cost of each new walking step.

A Mechatronic System for Studying Energy Optimization During Walking.

A general principle of human movement is that our nervous system is able to learn optimal coordination strategies. However, how our nervous system performs this optimization is not well understood. Here we design, build, and test a mechatronic system to probe the algorithms underlying the optimization of energetic cost in walking. The system applies controlled fore-aft forces to a hip-belt worn by a user, decreasing their energetic cost by pulling forward, or increasing it by pulling backward. The system controls the forces, and thus energetic cost as a function of how the user is moving. In testing, we found that the system can quickly, accurately, and precisely apply target forces within a walking step. We next controlled the forces as a function of the user's step frequency and found that we could predictably reshape their energetic cost landscape. Finally, we tested whether users adapted their walking in response to the new cost landscapes created by our system, and found that users shifted their step frequency toward the new energetic minima. Our system design appears to be effective for reshaping energetic cost landscapes in human walking to study how the nervous system optimizes movement.

Is natural variability in gait sufficient to initiate spontaneous energy optimization in human walking?

In new walking contexts, the nervous system can adapt preferred gaits to minimize energetic cost. During treadmill walking, this optimization is not usually spontaneous but instead requires experience with the new energetic cost landscape. Experimenters can provide subjects with the needed experience by prescribing new gaits or instructing them to explore new gaits. Yet in familiar walking contexts, people naturally prefer energetically optimal gaits: the nervous system can optimize cost without an experimenter's guidance. Here we test the hypothesis that the natural gait variability of overground walking provides the nervous system with sufficient experience with new cost landscapes to initiate spontaneous minimization of energetic cost. We had subjects walk over paths of varying terrain while wearing knee exoskeletons that penalized walking as a function of step frequency. The exoskeletons created cost landscapes with minima that were, on average, 8% lower than the energetic cost at the initially preferred gaits and achieved at walking speeds and step frequencies that were 4% lower than the initially preferred values. We found that our overground walking trials amplified gait variability by 3.7-fold compared with treadmill walking, resulting in subjects gaining greater experience with new cost landscapes, including frequent experience with gaits at the new energetic minima. However, after 20 min and 2.0 km of walking in the new cost landscapes, we observed no consistent optimization of gait, suggesting that natural gait variability during overground walking is not always sufficient to initiate energetic optimization over the time periods and distances tested in this study. NEW & NOTEWORTHY While the nervous system can continuously optimize gait to minimize energetic cost, what initiates this optimization process during every day walking is unknown. Here we tested the hypothesis that the nervous system leverages the natural variability in gait experienced during overground walking to converge on new energetically optimal gaits created using exoskeletons. Contrary to our hypothesis, we found that participants did not adapt toward optimal gaits: natural variability is not always sufficient to initiate spontaneous energy optimization.

Contribution of blood oxygen and carbon dioxide sensing to the energetic optimization of human walking.

People can adapt their gait to minimize energetic cost, indicating that walking's neural control has access to ongoing measurements of the body's energy use. In this study we tested the hypothesis that an important source of energetic cost measurements arises from blood gas receptors that are sensitive to O2 and CO2 concentrations. These receptors are known to play a role in regulating other physiological processes related to energy consumption, such as ventilation rate. Given the role of O2 and CO2 in oxidative metabolism, sensing their levels can provide an accurate estimate of the body's total energy use. To test our hypothesis, we simulated an added energetic cost for blood gas receptors that depended on a subject's step frequency and determined if subjects changed their behavior in response to this simulated cost. These energetic costs were simulated by controlling inspired gas concentrations to decrease the circulating levels of O2 and increase CO2 We found this blood gas control to be effective at shifting the step frequency that minimized the ventilation rate and perceived exertion away from the normally preferred frequency, indicating that these receptors provide the nervous system with strong physiological and psychological signals. However, rather than adapt their preferred step frequency toward these lower simulated costs, subjects persevered at their normally preferred frequency even after extensive experience with the new simulated costs. These results suggest that blood gas receptors play a negligible role in sensing energetic cost for the purpose of optimizing gait.NEW & NOTEWORTHY Human gait adaptation implies that the nervous system senses energetic cost, yet this signal is unknown. We tested the hypothesis that the blood gas receptors sense cost for gait optimization by controlling blood O2 and CO2 with step frequency as people walked. At the simulated energetic minimum, ventilation and perceived exertion were lowest, yet subjects preferred walking at their original frequency. This suggests that blood gas receptors are not critical for sensing cost during gait.

A generalized method for controlling end-tidal respiratory gases during nonsteady physiological conditions.

While forcing of end-tidal gases by regulating inspired gas concentrations is a common technique for studying cardiorespiratory physiology, independently controlling end-tidal gases is technically challenging. Feedforward control methods are challenging because end-tidal values vary as a dynamic function of both inspired gases and other nonregulated physiological parameters. Conventional feedback control is limited by delays within the lungs and body tissues and within the end-tidal forcing system itself. Consequently, modern end-tidal forcing studies have generally restricted their analysis to simple time courses of end-tidal gases and to resting steady-state conditions. To overcome these limitations, we have designed and validated a more generalized end-tidal forcing system that removes the need for manual tuning and rule-of-thumb based control heuristics, while allowing for accurate control of gases along spontaneous and complicated time courses and under nonsteady physiological conditions. On average during resting, steady walking, and walking with time varying speed, our system achieved step changes in PetCO2 within 3.0 ± 0.9 (mean ± SD) breaths and PetO2 within 4.4 ± 0.9 breaths, while also maintaining small steady-state errors of 0.1 ± 0.2 mmHg for PetCO2 and 0.3 ± 0.8 mmHg for PetO2 The system also accurately tracked more complicated changes in end-tidal values through a bandwidth of 1/10 the respiratory (sampling) frequency, a practical limit of feedback control systems. The primary mechanism enabling this controller performance is a generic mathematical model of the cardiopulmonary system that captures the breath-by-breath relationship between inspired and end-tidal gas concentrations, with secondary contributions from reduced delays in controlled air delivery.

Optimizing the Distribution of Leg Muscles for Vertical Jumping.

A goal of biomechanics and motor control is to understand the design of the human musculoskeletal system. Here we investigated human functional morphology by making predictions about the muscle volume distribution that is optimal for a specific motor task. We examined a well-studied and relatively simple human movement, vertical jumping. We investigated how high a human could jump if muscle volume were optimized for jumping, and determined how the optimal parameters improve performance. We used a four-link inverted pendulum model of human vertical jumping actuated by Hill-type muscles, that well-approximates skilled human performance. We optimized muscle volume by allowing the cross-sectional area and muscle fiber optimum length to be changed for each muscle, while maintaining constant total muscle volume. We observed, perhaps surprisingly, that the reference model, based on human anthropometric data, is relatively good for vertical jumping; it achieves 90% of the jump height predicted by a model with muscles designed specifically for jumping. Alteration of cross-sectional areas-which determine the maximum force deliverable by the muscles-constitutes the majority of improvement to jump height. The optimal distribution results in large vastus, gastrocnemius and hamstrings muscles that deliver more work, while producing a kinematic pattern essentially identical to the reference model. Work output is increased by removing muscle from rectus femoris, which cannot do work on the skeleton given its moment arm at the hip and the joint excursions during push-off. The gluteus composes a disproportionate amount of muscle volume and jump height is improved by moving it to other muscles. This approach represents a way to test hypotheses about optimal human functional morphology. Future studies may extend this approach to address other morphological questions in ethological tasks such as locomotion, and feature other sets of parameters such as properties of the skeletal segments.

Humans Can Continuously Optimize Energetic Cost during Walking.

People prefer to move in ways that minimize their energetic cost. For example, people tend to walk at a speed that minimizes energy use per unit distance and, for that speed, they select a step frequency that makes walking less costly. Although aspects of this preference appear to be established over both evolutionary and developmental timescales, it remains unclear whether people can also optimize energetic cost in real time. Here we show that during walking, people readily adapt established motor programs to minimize energy use. To accomplish this, we used robotic exoskeletons to shift people's energetically optimal step frequency to frequencies higher and lower than normally preferred. In response, we found that subjects adapted their step frequency to converge on the new energetic optima within minutes and in response to relatively small savings in cost (<5%). When transiently perturbed from their new optimal gait, subjects relied on an updated prediction to rapidly re-converge within seconds. Our collective findings indicate that energetic cost is not just an outcome of movement, but also plays a central role in continuously shaping it.

The snooze of lose: Rapid reaching reveals that losses are processed more slowly than gains.

Decision making revolves around weighing potential gains and losses. Research in economic decision making has emphasized that humans exercise disproportionate caution when making explicit choices involving loss. By comparison, research in perceptual decision making has revealed a processing advantage for targets associated with potential gain, though the effects of loss have been explored less systematically. Here, we use a rapid reaching task to measure the relative sensitivity (Experiment 1) and the time course (Experiments 2 and 3) of rapid actions with regard to the reward valence and probability of targets. We show that targets linked to a high probability of gain influence actions about 100 ms earlier than targets associated with equivalent probability and value of loss. These findings are well accounted for by a model of stimulus response in which reward modulates the late, postpeak phase of the activity. We interpret our results within a neural framework of biased competition that is resolved in spatial maps of behavioral relevance. As implied by our model, all visual stimuli initially receive positive activation. Gain stimuli can build off of this initial activation when selected as a target, whereas loss stimuli have to overcome this initial activation in order to be avoided, accounting for the observed delay between valences. Our results bring clarity to the perceptual effects of losses versus gains and highlight the importance of considering the timeline of different biasing factors that influence decisions.

The cost of moving optimally: kinematic path selection.

It is currently unclear whether the brain plans movement kinematics explicitly or whether movement paths arise implicitly through optimization of a cost function that takes into account control and/or dynamic variables. Several cost functions are proposed in the literature that are very different in nature (e.g., control effort, torque change, and jerk), yet each can predict common movement characteristics. We set out to disentangle predictions of the different variables using a combination of modeling and empirical studies. Subjects performed goal-directed arm movements in a force field (FF) in combination with visual perturbations of seen hand position. This FF was designed to have distinct optimal movements for muscle-input and dynamic costs while leaving kinematic cost unchanged. Visual perturbations in turn changed the kinematic cost but left the dynamic and muscle-input costs unchanged. An optimally controlled, physiologically realistic arm model was used to predict movements under the various cost variables. Experimental results were not consistent with a cost function containing any of the control and dynamic costs investigated. Movement patterns of all experimental conditions were adequately predicted by a kinematic cost function comprising both visually and somatosensory perceived jerk. The present study provides clear behavioral evidence that the brain solves kinematic and mechanical redundancy in separate steps: in a first step, movement kinematics are planned; and in a second, separate step, muscle activation patterns are generated.

Bimanual proprioception: are two hands better than one?

Information about the position of an object that is held in both hands, such as a golf club or a tennis racquet, is transmitted to the human central nervous system from peripheral sensors in both left and right arms. How does the brain combine these two sources of information? Using a robot to move participant's passive limbs, we performed psychophysical estimates of proprioceptive function for each limb independently and again when subjects grasped the robot handle with both arms. We compared empirical estimates of bimanual proprioception to several models from the sensory integration literature: some that propose a combination of signals from the left and right arms (such as a Bayesian maximum-likelihood estimate), and some that propose using unimanual signals alone. Our results are consistent with the hypothesis that the nervous system both has knowledge of and uses the limb with the best proprioceptive acuity for bimanual proprioception. Surprisingly, a Bayesian model that postulates optimal combination of sensory signals could not predict empirically observed bimanual acuity. These findings suggest that while the central nervous system seems to have information about the relative sensory acuity of each limb, it uses this information in a rather rudimentary fashion, essentially ignoring information from the less reliable limb.

Observing object lifting errors modulates cortico-spinal excitability and improves object lifting performance.

Observing the actions of others has been shown to modulate cortico-spinal excitability and affect behaviour. However, the sensorimotor consequences of observing errors are not well understood. Here, participants watched actors lift identically weighted large and small cubes which typically elicit expectation-based fingertip force errors. One group of participants observed the standard overestimation and underestimation-style errors that characterise early lifts with these cubes (Error video--EV). Another group watched the same actors performing the well-adapted error-free lifts that characterise later, well-practiced lifts with these cubes (No error video--NEV). We then examined actual object lifting performance in the subjects who watched the EV and NEV. Despite having similar cognitive expectations and perceptions of heaviness, the group that watched novice lifters making errors themselves made fewer overestimation-style errors than those who watched the expert lifts. To determine how the observation of errors alters cortico-spinal excitability, we measured motor evoked potentials in separate group of participants while they passively observed these EV and NEV. Here, we noted a novel size-based modulation of cortico-spinal excitability when observing the expert lifts, which was eradicated when watching errors. Together, these findings suggest that individuals' sensorimotor systems are sensitive to the subtle visual differences between observing novice and expert performance.

Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback.

Whereas muscle spindles play a prominent role in current theories of human motor control, Golgi tendon organs (GTO) and their associated tendons are often neglected. This is surprising since there is ample evidence that both tendons and GTOs contribute importantly to neuromusculoskeletal dynamics. Using detailed musculoskeletal models, we provide evidence that simple feedback using muscle spindles alone results in very poor control of joint position and movement since muscle spindles cannot sense changes in tendon length that occur with changes in muscle force. We propose that a combination of spindle and GTO afferents can provide an estimate of muscle-tendon complex length, which can be effectively used for low-level feedback during both postural and movement tasks. The feasibility of the proposed scheme was tested using detailed musculoskeletal models of the human arm. Responses to transient and static perturbations were simulated using a 1-degree-of-freedom (DOF) model of the arm and showed that the combined feedback enabled the system to respond faster, reach steady state faster, and achieve smaller static position errors. Finally, we incorporated the proposed scheme in an optimally controlled 2-DOF model of the arm for fast point-to-point shoulder and elbow movements. Simulations showed that the proposed feedback could be easily incorporated in the optimal control framework without complicating the computation of the optimal control solution, yet greatly enhancing the system's response to perturbations. The theoretical analyses in this study might furthermore provide insight about the strong physiological couplings found between muscle spindle and GTO afferents in the human nervous system.

Can proprioceptive training improve motor learning?

Recent work has investigated the link between motor learning and sensory function in arm movement control. A number of findings are consistent with the idea that motor learning is associated with systematic changes to proprioception (Haith A, Jackson C, Mial R, Vijayakumar S. Adv Neural Inf Process Syst 21: 593-600, 2008; Ostry DJ, Darainy M, Mattar AA, Wong J, Gribble PL. J Neurosci 30: 5384-5393, 2010; Vahdat S, Darainy M, Milner TE, Ostry DJ. J Neurosci 31: 16907-16915, 2011). Here, we tested whether motor learning could be improved by providing subjects with proprioceptive training on a desired hand trajectory. Subjects were instructed to reproduce both the time-varying position and velocity of novel, complex hand trajectories. Subjects underwent 3 days of training with 90 movement trials per day. Active movement trials were interleaved with demonstration trials. For control subjects, these interleaved demonstration trials consisted of visual demonstration alone. A second group of subjects received visual and proprioceptive demonstration simultaneously; this group was presented with the same visual stimulus, but, in addition, their limb was moved through the target trajectory by a robot using servo control. Subjects who experienced the additional proprioceptive demonstration of the desired trajectory showed greater improvements during training movements than control subjects who only received visual information. This benefit of adding proprioceptive training was seen in both movement speed and position error. Interestingly, additional control subjects who received proprioceptive guidance while actively moving their arm during demonstration trials did not show the same improvement in positional accuracy. These findings support the idea that the addition of proprioceptive training can augment motor learning, and that this benefit is greatest when the subject passively experiences the goal movement.

Spatially selective enhancement of proprioceptive acuity following motor learning.

It is well recognized that the brain uses sensory information to accurately produce motor commands. Indeed, most research into the relationship between sensory and motor systems has focused on how sensory information modulates motor function. In contrast, recent studies have begun to investigate the reverse: how sensory and perceptual systems are tuned based on motor function, and specifically motor learning. In the present study we investigated changes to human proprioceptive acuity following recent motor learning. Sensitivity to small displacements of the hand was measured before and after 10 min of motor learning, during which subjects grasped the handle of a robotic arm and guided a cursor to a series of visual targets randomly located within a small workspace region. We used a novel method of assessing proprioceptive acuity that avoids active movement, interhemispheric transfer, and intermodality coordinate transformations. We found that proprioceptive acuity improved following motor learning, but only in the region of the arm's workspace explored during learning. No proprioceptive improvement was observed when motor learning was performed in a different location or when subjects passively experienced limb trajectories matched to those of subjects who actively performed motor learning. Our findings support the idea that sensory changes occur in parallel with changes to motor commands during motor learning.

The central nervous system does not minimize energy cost in arm movements.

It has been widely suggested that the many degrees of freedom of the musculoskeletal system may be exploited by the CNS to minimize energy cost. We tested this idea by having subjects making point-to-point movements while grasping a robotic manipulandum. The robot created a force field chosen such that the minimal energy hand path for reaching movements differed substantially from those observed in a null field. The results show that after extended exposure to the force field, subjects continued to move exactly as they did in the null field and thus used substantially more energy than needed. Even after practicing to move along the minimal energy path, subjects did not adapt their freely chosen hand paths to reduce energy expenditure. The results of this study indicate that for point-to-point arm movements minimization of energy cost is not a dominant factor that influences how the CNS arrives at kinematics and associated muscle activation patterns.