On the encoding capacity of human motor adaptation.

Primitive-based models of motor learning suggest that adaptation occurs by tuning the responses of motor primitives. Based on this idea, we consider motor learning as an information encoding procedure, that is, a procedure of encoding a motor skill into primitives. The capacity of encoding is determined by the number of recruited primitives, which depends on how many primitives are "visited" by the movement, and this leads to a rather counterintuitive prediction that faster movement, where a larger number of motor primitives are involved, allows learning more complicated motor skills. Here, we provide a set of experimental results that support this hypothesis. First, we show that learning occurs only with movement, that is, only with nonzero encoding capacity. When participants were asked to counteract a rotating force applied to a robotic handle, they were unable to do so when maintaining a static posture but were able to adapt when making small circular movements. Our second experiment further investigated how adaptation is affected by movement speed. When adapting to a simple (low-information-content) force field, fast (high-capacity) movement did not have an advantage over slow (low-capacity) movement. However, for a complex (high-information-content) force field, the fast movement showed a significant advantage over slow movement. Our final experiment confirmed that the observed benefit of high-speed movement is only weakly affected by mechanical factors. Taken together, our results suggest that the encoding capacity is a genuine limiting factor of human motor adaptation.NEW & NOTEWORTHY We propose a novel concept called "encoding capacity" of motor adaptation, which describes an inherent limiting-factor of our brain's ability to learn new motor skills, just like any other storage system. By reinterpreting the existing primitive-based models of motor learning, we hypothesize that the encoding capacity is determined by the size of the movement, and present a set of experimental evidence suggesting that such limiting effect of encoding capacity does exist in human motor adaptation.

SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises.

In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have recently been proposed for compressed images, they do not provide satisfactory performance on the challenging compressed Poisson noises. This is mainly due to (i) inaccurate modeling regarding the image degradation, (ii) the signal-dependent noise property, and (iii) the lack of analysis on intercorrelation distortion. In this paper, we focused on the challenging issues in practical image coding systems and propose a compressed Poisson noise reduction scheme based on a secondary domain intercorrelation enhanced network. Specifically, we introduced a compressed Poisson noise corruption model and combined the secondary domain intercorrelation prior with a deep neural network especially designed for signal-dependent compression noise reduction. Experimental results showed that the proposed network is superior to the existing state-of-the-art restoration alternatives on classical images, the LIVE1 dataset, and the SIDD dataset.

Optimal feedback control to describe multiple representations of primary motor cortex neurons.

Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor output, such as the hand force, like a population response of M1 neurons. Moreover, a distribution of the preferred directions (PDs) of M1 neurons can be predicted via feedback gain. Therefore, we suggest that neural activity in M1 is optimised for the musculoskeletal system. Furthermore, if the feedback controller is represented in M1, OFC can describe multiple representations of M1, including not only the distribution of PDs but also the response of the neuronal population.

Altering Effort Costs in Parkinson's Disease with Noninvasive Cortical Stimulation.

In Parkinson's disease (PD), the human brain is capable of producing motor commands, but appears to require greater than normal subjective effort, particularly for the more-affected side. What is the nature of this subjective effort and can it be altered? We used an isometric task in which patients produced a goal force by engaging both arms, but were free to assign any fraction of that force to each arm. The patients preferred their less-affected arm, but only in some directions. This preference was correlated with lateralization of signal-dependent noise: the direction of force for which the brain was less willing to assign effort to an arm was generally the direction for which that arm exhibited greater noise. Therefore, the direction-dependent noise in each arm acted as an implicit cost that discouraged use of that arm. To check for a causal relationship between noise and motor cost, we used bilateral transcranial direct current stimulation of the motor cortex, placing the cathode on the more-affected side and the anode on the less-affected side. This stimulation not only reduced the noise on the more-affected arm, it also increased the willingness of the patients to assign force to that arm. In a 3 d double-blind study and in a 10 d repeated stimulation study, bilateral stimulation of the two motor cortices with cathode on the more-affected side reduced noise and increased the willingness of the patients to exert effort. This stimulation also improved the clinical motor symptoms of the disease. SIGNIFICANCE STATEMENT: In Parkinson's disease, patients are less willing to assign force to their affected arm. Here, we find that this pattern is direction dependent: directions for which the arm is noisier coincide with directions for which the brain is less willing to assign force. We hypothesized that if we could reduce the noise on the affected arm, then we may increase the willingness for the brain to assign force to that arm. We found a way to do this via noninvasive cortical stimulation. In addition to reducing effort costs associated with the affected arm, the cortical stimulation also improved clinical motor symptoms of the disease.

The duration of reaching movement is longer than predicted by minimum variance.

Whether the central nervous system minimizes variability or effort in planning arm movements can be tested by measuring the preferred movement duration and end-point variability. Here we conducted an experiment in which subjects performed arm reaching movements without visual feedback in fast-, medium-, slow-, and preferred-duration conditions. Results show that 1) total end-point variance was smallest in the medium-duration condition and 2) subjects preferred to carry out movements that were slower than this medium-duration condition. A parsimonious explanation for the overall pattern of end-point errors across fast, medium, preferred, and slow movement durations is that movements are planned to minimize effort as well as end-point error due to both signal-dependent and constant noise.

Motor costs and the coordination of the two arms.

We have two arms, many muscles in each arm, and numerous neurons that contribute to their control. How does the brain assign responsibility to each of these potential actors? We considered a bimanual task in which people chose how much force to produce with each arm so that the sum would equal a target. We found that the dominant arm made a greater contribution, but only for specific directions. This was not because the dominant arm was stronger. Rather, it was less noisy. A cost that included unimanual noise and strength accounted for both direction- and handedness-dependent choices that young people made. To test whether there was a causal relationship between unimanual noise and bimanual control, we considered elderly people, whose unimanual noise is comparable in the two arms. We found that, in bimanual control, the elderly showed no preference for their dominant arm. We noninvasively stimulated the motor cortex to produce a change in unimanual strength and noise, and found a corresponding change in bimanual control. Using the noise measurements, we built a neuronal model. The model explained the anisotropic distribution of preferred directions of neurons in the monkey motor cortex and predicted that, in humans, there are changes in the number of these cortical neurons with handedness and aging. Therefore, we found that coordination can be explained by the noise and strength of each effector, where noise may be a reflection of the number of task-related neurons available for control of that effector in the motor cortex.

Ankle Joint Angle Influences Relative Torque Fluctuation during Isometric Plantar Flexion.

The purpose of this study was to investigate the influence of changes in muscle length on the torque fluctuations and on related oscillations in muscle activity during voluntary isometric contractions of ankle plantar flexor muscles. Eleven healthy individuals were asked to perform voluntary isometric contractions of ankle muscles at five different contraction intensities from 10% to 70% of maximum voluntary isometric contraction (MVIC) and at three different muscle lengths, implemented by changing the ankle joint angle (plantar flexion of 26°-shorter muscle length; plantar flexion of 10°-neutral muscle length; dorsiflexion of 3°-longer muscle length). Surface electromyogram (EMG) signals were recorded from the skin surface over the triceps surae muscles, and rectified-and-smoothed EMG (rsEMG) were estimated to assess the oscillations in muscle activity. The absolute torque fluctuations (quantified by the standard deviation) were significantly higher during moderate-to-high contractions at the longer muscle length. Absolute torque fluctuations were found to be a linear function of torque output regardless of muscle length. In contrast, the relative torque fluctuations (quantified by the coefficient of variation) were higher at the shorter muscle length. However, both absolute and relative oscillations in muscle activities remained relatively consistent at different ankle joint angles for all plantar flexors. These findings suggest that the torque steadiness may be affected by not only muscle activities, but also by muscle length-dependent mechanical properties. This study provides more insights that muscle mechanics should be considered when explaining the steadiness in force output.

Forward Inverse Relaxation Model Incorporating Movement Duration Optimization.

A computational trajectory formation model based on the optimization principle, which introduces the forward inverse relaxation model (FIRM) as the hardware and algorithm, represents the features of human arm movements well. However, in this model, the movement duration was defined as a given value and not as a planned value. According to considerable empirical facts, movement duration changes depending on task factors, such as required accuracy and movement distance thus, it is considered that there are some criteria that optimize the cost function. Therefore, we propose a FIRM that incorporates a movement duration optimization module. The movement duration optimization module minimizes the weighted sum of the commanded torque change term as the trajectory cost, and the tolerance term as the cost of time. We conducted a behavioral experiment to examine how well the movement duration obtained by the model reproduces the true movement. The results suggested that the model movement duration was close to the true movement. In addition, the trajectory generated by inputting the obtained movement duration to the FIRM reproduced the features of the actual trajectory well. These findings verify the use of this computational model in measuring human arm movements.

The Functional Architecture of the Brain Underlies Strategic Deception in Impression Management.

Impression management, as one of the most essential skills of social function, impacts one's survival and success in human societies. However, the neural architecture underpinning this social skill remains poorly understood. By employing a two-person bargaining game, we exposed three strategies involving distinct cognitive processes for social impression management with different levels of strategic deception. We utilized a novel adaptation of Granger causality accounting for signal-dependent noise (SDN), which captured the directional connectivity underlying the impression management during the bargaining game. We found that the sophisticated strategists engaged stronger directional connectivity from both dorsal anterior cingulate cortex and retrosplenial cortex to rostral prefrontal cortex, and the strengths of these directional influences were associated with higher level of deception during the game. Using the directional connectivity as a neural signature, we identified the strategic deception with 80% accuracy by a machine-learning classifier. These results suggest that different social strategies are supported by distinct patterns of directional connectivity among key brain regions for social cognition.

Saccadic suppression as a perceptual consequence of efficient sensorimotor estimation.

Humans perform saccadic eye movements two to three times per second. When doing so, the nervous system strongly suppresses sensory feedback for extended periods of time in comparison to movement time. Why does the brain discard so much visual information? Here we suggest that perceptual suppression may arise from efficient sensorimotor computations, assuming that perception and control are fundamentally linked. More precisely, we show theoretically that a Bayesian estimator should reduce the weight of sensory information around the time of saccades, as a result of signal dependent noise and of sensorimotor delays. Such reduction parallels the behavioral suppression occurring prior to and during saccades, and the reduction in neural responses to visual stimuli observed across the visual hierarchy. We suggest that saccadic suppression originates from efficient sensorimotor processing, indicating that the brain shares neural resources for perception and control.

Neural Control Adaptation to Motor Noise Manipulation.

Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in 12 young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation (CV) of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g., delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability.

Speed-Accuracy Trade-Off in a Trajectory-Constrained Self-Feeding Task: A Quantitative Index of Unsuppressed Motor Noise in Children With Dystonia.

Motor speed and accuracy are both affected in childhood dystonia. Thus, deriving a speed-accuracy function is an important metric for assessing motor impairments in dystonia. Previous work in dystonia studied the speed-accuracy trade-off during point-to-point tasks. To achieve a more relevant measurement of functional abilities in dystonia, the present study investigates upper-limb kinematics and electromyographic activity of 8 children with dystonia and 8 healthy children during a trajectory-constrained child-relevant task that emulates self-feeding with a spoon and requires continuous monitoring of accuracy. The speed-accuracy trade-off is examined by changing the spoon size to create different accuracy demands. Results demonstrate that the trajectory-constrained speed-accuracy relation is present in both groups, but it is altered in dystonia in terms of increased slope and offset toward longer movement times. Findings are consistent with the hypothesis of increased signal-dependent noise in dystonia, which may partially explain the slow and variable movements observed in dystonia.