In literature, it has been suggested that the CNS anticipates spontaneous change in body position during quiet stance and continuously modulates ankle extensor muscle activity to compensate for the change. The purpose of this study was to investigate whether velocity feedback contributes by modulating ankle extensor activities in an anticipatory fashion, facilitating effective control of quiet stance. Both theoretical analysis and experiments were carried out to investigate to what extent velocity feedback contributes to controlling quiet stance. The experiments were carried out with 16 healthy subjects who were asked to stand quietly with their eyes open or closed. During the experiments, the center of pressure (COP) displacement (COPdis), the center of mass (COM) displacement (COMdis), and COM velocity (COMvel) in the anteroposterior direction were measured. Rectified electromyograms (EMGs) were used to measure muscle activity in the right soleus muscle, the medial gastrocnemius muscle, and the lateral gastrocnemius muscle. The simulations were performed using an inverted pendulum model that described the anteroposterior kinematics and dynamics of quiet stance. In the simulations, an assumption was made that the COMdis of the body would be regulated using a proportional-derivative (PD) controller. Two different PD controllers were evaluated in these simulations: 1) a controller with the high-derivative/velocity gain (HDG) and 2) a controller with the low-derivative/velocity gain (LDG). Cross-correlation analysis was applied to investigate the relationships between time series obtained in experiments 1) COMdis and EMGs and 2) COMvel and EMGs. Identical cross-correlation analysis was applied to investigate the relationships between time series obtained in simulations 3) COMdis and ankle torque and 4) COMvel and ankle torque. The results of these analyses showed that the COMdis was positively correlated with all three EMGs and that the EMGs temporally preceded the COMdis. These findings agree with the previously published studies in which it was shown that the lateral gastrocnemius muscle is actively modulated in anticipation of the body's COM position change. The COMvel and all three EMGs were also correlated and the cross-correlation function (CCF) had two peaks: one that was positive and another that was negative. The positive peaks were statistically significant, unlike the negative ones; they were larger than the negative peaks; and their time shifts were much shorter compared with the time shifts of the negative peaks. When these results were compared with the CCF results obtained for simulated time series, it was discovered that the cross-correlation results for the HDG controller closely matched cross-correlation results for the experimental time series. On the other hand, the simulation result obtained for LDG controller did not match the experimental results. These findings suggest that the actual postural control system during quiet stance adopts a control strategy that relies notably on velocity inform...

. A sensorimotor control task often requires an accurate estimation of the timing of the arrival of an external target (e.g., when hitting a pitched ball). Conventional studies of human timing processes have ignored the stochastic features of target timing: e.g., the speed of the pitched ball is not generally constant, but is variable. Interestingly, based on Bayesian theory, it has been recently shown that the human sensorimotor system achieves the optimal estimation by integrating sensory information with prior knowledge of the probabilistic structure of the target variation. In this study, we tested whether Bayesian integration is also implemented while performing a coincidencetiming type of sensorimotor task by manipulating the trial-by-trial variability (i.e., the prior distribution) of the target timing. As a result, within several hundred trials of learning, subjects were able to generate systematic timing behavior according to the width of the prior distribution, as predicted by the optimal Bayesian model. Considering the previous studies showing that the human sensorimotor system uses Bayesian integration in spacing and forcegrading tasks, our result indicates that Bayesian integration is fundamental to all aspects of human sensorimotor control. Moreover, it was noteworthy that the subjects could adjust their behavior both when the prior distribution was switched from wide to narrow and vice versa, although the adjustment was slower in the former case. Based on a comparison with observations in a previous study, we discuss the flexibility and adaptability of Bayesian sensorimotor learning. I N T R O D U C T I O NOur sensorimotor system often requires that motor responses are timed precisely in accordance with the behavior of a certain external target. For example, to hit a pitched ball while playing baseball or cricket, the batter has to control the timing of the swing based on the speed of the ball. Such timing behavior, which is referred to as coincidence timing, has been studied extensively in sports science (e.g., Ripoll and Latiri 1997; Williams et al. 2002). Because the external environment that we usually encounter is variable, not constant, we need to monitor visual and other sensory signals to estimate the current behavior of the external target accurately. Naturally, these sensory signals are exposed to internal and external noise (Körding and Wolpert 2004; van Beers et al. 2002), so they cannot always provide sufficient information for precise estimates. To compensate for the sensory uncertainty, prior knowledge or experience of the target behavior is helpful information for the estimation. By observing the external target behavior for a long time, we can determine its predictable probabilistic structure. Considering various phenomena in our world, such as human behavior and physical events, the trial-by-trial variability has a certain probabilistic structure, such as a Gaussian distribution (e.g., Chen et al. 1997). That is, the speeds of all pitched balls do not appear with the same probab...

Although a limb's motion appears to be similar across unimanual and bimanual movements, here we demonstrate partial, but not complete, transfer of learning across these behavioral contexts, hidden learning that remains intact (but invisible) until the original context is again encountered, and the ability to associate two conflicting force fields simultaneously, one with each context. These results suggest partial, but not complete, overlap in the learning processes involved in the acquisition of unimanual and bimanual skills.

Noise can assist neurons in the detection of weak signals via a mechanism known as stochastic resonance (SR). We demonstrate experimentally that SR-type effects can be obtained in rat sensory neurons with white noise, 1͞f noise, or 1͞f 2 noise. For low-frequency input noise, we show that the optimal noise intensity is the lowest and the output signal-to-noise ratio the highest for conventional white noise. We also show that under certain circumstances, 1͞f noise can be better than white noise for enhancing the response of a neuron to a weak signal. We present a theory to account for these results and discuss the biological implications of 1͞f noise. [S0031-9007(99)08727-X]

We provide the first evidence that stochastic resonance within the human brain can enhance behavioral responses to weak sensory inputs. We asked subjects to adjust handgrip force to a slowly changing, subthreshold gray level signal presented to their right eye. Behavioral responses were optimized by presenting randomly changing gray levels separately to the left eye. The results indicate that observed behavioral stochastic resonance was mediated by neural activity within the human brain where the information from both eyes converges.

We can adapt movements to a novel dynamic environment (e.g., tool use, microgravity, and perturbation) by acquiring an internal model of the dynamics. Although multiple environments can be learned simultaneously if each environment is experienced with different limb movement kinematics, it is controversial as to whether multiple internal models for a particular movement can be learned and flexibly retrieved according to behavioral contexts. Here, we address this issue by using a novel visuomotor task. While participants reached to each of two targets located at a clockwise or counter-clockwise position, a gradually increasing visual rotation was applied in the clockwise or counter-clockwise direction, respectively, to the on-screen cursor representing the unseen hand position. This procedure implicitly led participants to perform physically identical pointing movements irrespective of their intentions (i.e., movement plans) to move their hand toward two distinct visual targets. Surprisingly, if each identical movement was executed according to a distinct movement plan, participants could readily adapt these movements to two opposing force fields simultaneously. The results demonstrate that multiple motor memories can be learned and flexibly retrieved, even for physically identical movements, according to distinct motor plans in a visual space.

Human quiet standing is often modeled as a single inverted pendulum rotating around the ankle joint, under the assumption that movement around the hip joint is quite small. However, several recent studies have shown that movement around the hip joint can play a significant role in the efficient maintenance of the center of body mass (COM) above the support area. The aim of this study was to investigate how coordination between the hip and ankle joints is controlled during human quiet standing. Subjects stood quietly for 30 s with their eyes either opened (EO) or closed (EC), and we measured subtle angular displacements around the ankle (thetaa) and hip (thetah) joints using three highly sensitive CCD laser displacement sensors. Reliable data were obtained for both angular displacement and angular velocity (the first derivative of the angular displacement). Further, measurement error was not predominant, even among the angular acceleration data, which were obtained by taking the second derivative of the angular displacement. The angular displacement, velocity, and acceleration of the hip were found to be significantly greater (P<0.001) than those of the ankle, confirming that hip-joint motion cannot be ignored, even during quiet standing. We also found that a consistent reciprocal relationship exists between the angular accelerations of the hip and ankle joints, namely positive or negative angular acceleration of ankle joint is compensated for by oppositely directed angular acceleration of the hip joint. Principal component analysis revealed that this relationship can be expressed as: thetah=gammathetaa with gamma=-3.15+/-1.24 and gamma=-3.12+/-1.46 (mean +/-SD) for EO and EC, respectively, where theta is the angular acceleration. There was no significant difference in the values of y for EO and EC, and these values were in agreement with the theoretical value calculated assuming the acceleration of COM was zero. On the other hand, such a consistent relationship was never observed for angular displacement itself. These results suggest that the angular motions around the hip and ankle joints are not to keep the COM at a constant position, but rather to minimize acceleration of the COM.

Series cross-section images of the upper extremity were obtained for four men by magnetic resonance imaging (MRI) and anatomical cross-sectional areas (ACSA) of elbow flexor muscles [biceps brachii (BIC), brachialis (BRA), brachioradialis (BRD)] and extensor muscles [triceps brachii (TRI)] were measured. Physiological cross-sectional area (PCSA) was calculated from the muscle volume and muscle fibre length, the former from the series ACSA and the latter from the muscle length multiplied by previously reported fibre/muscle length ratios. Elbow flexion/extension torque was measured using an isokinetic dynamometer and the force at the tendons was calculated from the torque and moment arms of muscles measured by MRI. Maximal ACSA of TRI was comparable to that of total flexors, while PCSA of TRI was greater by 1.9 times. Within flexors, BRA had the greatest contribution to torque (47%), followed by BIC (34%) and BRD (19%). Specific tension related to the estimated velocity of muscle fibres were similar for elbow flexors and extensors, suggesting that the capacity of tension development is analogous between two muscle groups.