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Mitsuo Kawato

Publications and source records attributed to Mitsuo Kawato.

17 recordsLinked to original sources
PubMed2003

Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable dynamics.

This study compared the mechanisms of adaptation to stable and unstable dynamics from the perspective of changes in joint mechanics. Subjects were instructed to make point to point movements in force fields generated by a robotic manipulandum which interacted with the arm in either a stable or an unstable manner. After subjects adjusted to the initial disturbing effects of the force fields they were able to produce normal straight movements to the target. In the case of the stable interaction, subjects modified the joint torques in order to appropriately compensate for the force field. No change in joint torque or endpoint force was required or observed in the case of the unstable interaction. After adaptation, the endpoint stiffness of the arm was measured by applying displacements to the hand in eight different directions midway through the movements. This was compared to the stiffness measured similarly during movements in a null force field. After adaptation, the endpoint stiffness under both the stable and unstable dynamics was modified relative to the null field. Adaptation to unstable dynamics was achieved by selective modification of endpoint stiffness in the direction of the instability. To investigate whether the change in endpoint stiffness could be accounted for by change in joint torque or endpoint force, we estimated the change in stiffness on each trial based on the change in joint torque relative to the null field. For stable dynamics the change in endpoint stiffness was accurately predicted. However, for unstable dynamics the change in endpoint stiffness could not be reproduced. In fact, the predicted endpoint stiffness was similar to that in the null force field. Thus, the change in endpoint stiffness seen after adaptation to stable dynamics was directly related to changes in net joint torque necessary to compensate for the dynamics in contrast to adaptation to unstable dynamics, where a selective change in endpoint stiffness occurred without any modification of net joint torque.

Adaptation, Physiological↗
PubMed2003

Modular organization of internal models of tools in the human cerebellum.

Human capabilities in manipulating many different tools with dexterity suggest modular neural organization at functional levels, but anatomical modularity underlying the capabilities has yet to be demonstrated. Although modularity in phylogenetically older parts of the cerebellum is well known, comparable modularity in the lateral cerebellum for cognitive functions remains unknown. We investigated these issues by functional MRI (fMRI) based on our previous findings of a cerebellar internal model of a tool. After subjects intensively learned to manipulate two novel tools (the rotated mouse whose cursor appeared at a rotated position, and the velocity mouse whose cursor velocity was proportional to the mouse position), they could easily switch between the two. The lateral and posterior cerebellar activities for the two different tools were spatially segregated, and their overlaps were <10%, even at low statistical thresholds. Activities of the rotated mouse were more anterior and lateral than the velocity mouse activities. These results were consistent with predictions by the MOdular Selection And Identification Controller (MOSAIC) model that multiple internal models compete to partition sensory-motor experiences and their outputs are linearly combined for a particular context.

Adult↗
PubMed2003

A unifying computational framework for motor control and social interaction.

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.

Humans↗
PubMed2003

Internal forward models in the cerebellum: fMRI study on grip force and load force coupling.

Internal models are neural mechanisms that can mimic the input-output or output-input properties of the motor apparatus and external objects. Forward internal models predict sensory consequences from efference copies of motor commands. There is growing acceptance of the idea that forward models are important in sensorimotor integration as well as in higher cognitive function, but their anatomical loci and neural mechanisms are still largely unknown. Some of the most convincing evidence that the central nervous system (CNS) makes use of forward models in sensory motor control comes from studies on grip force-load force coupling. We first present a brief review of recent computational and behavioral studies that provide decisive evidence for the utilization of forward models in grip force-load force coupling tasks. Then, we used functional magnetic resonance imaging (fMRI) to measure the brain activity related to this coupling and demonstrate that the cerebellum is the most likely site for forward models to be stored.

Adult↗
PubMed2003

Acquisition and contextual switching of multiple internal models for different viscous force fields.

Humans can learn an enormous number of motor behaviors in different environments. To explain this, the MOSAIC model proposes that multiple internal models are acquired in the brain, which can be switched. However, previous behavioral studies that examined arm-movement adaptations to multiple environments reported a rather limited learning capability. Hitherto, humans have been believed incapable of learning two opposite viscous force fields, which are both dynamic transformations and depend on the same state variable, presented in a random order with only a visual cue. In contrast, this study found that humans are capable of this. Elbow joint movements to specified targets were perturbed by either resistive or assistive viscous force fields generated by a single degree-of-freedom manipulandum. The resistive or assistive viscous force fields were cued by a blue or red color on a CRT screen, respectively. The squared distance between the end point and the target, and the variance of the joint angular velocities were used as kinematic performance indices. These movement errors decreased significantly as a function of the training days. Aftereffects and learning consolidation were demonstrated in the random presentation of the two force fields. Consequently, humans were able to learn the multiple and distinct internal models of the two force fields and appropriately switch them even for a random presentation cued only by color after several days of training. This study suggests that none of the previously proposed conditions for multiple internal model learning are necessary prerequisites, and indicates that the difficulty in learning is determined by the balance between the effectiveness of contextual information and the similarity of force fields.

Adaptation, Psychological↗
PubMed2002

A method for estimating torque-vector directions of shoulder muscles using surface EMGs.

In this study, a new method is proposed to estimate the torque-vector directions of each shoulder muscle. The method is based on a multiple regression model that reconstructs shoulder torque, which is calculated from the hand force and posture, from the surface EMG of many muscles recorded simultaneously. The torque-vector directions of eleven shoulder muscles of four subjects were obtained at up to 30 different arm postures with this method. The mean confidence interval ( p< 0.05) of the estimated torque-vector direction of each subject was 7.7-10.6 degrees. The correlation coefficient between the measured shoulder torque and reconstructed shoulder torque was between 0.76-0.84. The results for majority of the muscles were in accordance with previous studies, and reasonable from the viewpoint of anatomy. The torque-vector directions of a muscle, which are estimated with this method, have more of a functional meaning than a pure anatomical or mechanical one. These indicate the direction of the shoulder torque accompanying the muscle activation for a normal shoulder action that involves the cooperative contraction of many muscles.

Adult↗
PubMed2002

The neural substrates of biological motion perception: an fMRI study.

We used fMRI to identify the brain areas related to the perception of biological motion (4 T EPI; whole brain). In experiment 1, 10 subjects viewed biological motion (a human figure jumping up and down, composed of 21 dots), alternating with a control stimulus created by applying autoregressive models to the biological motion stimulus (such that the dots' speeds and amplitudes were preserved whereas their linking structure was not). The lengths of the stimulus bouts varied, and therefore the transitions between biological motion and control stimuli were unpredictable. Subjects had to indicate with a button press when each transition occurred. In a related biological motion task, subjects detected short (1 s) disturbances within these displays. We also examined the neural substrates of motion and shape perception, as well as motor imagery, to determine whether or not the cortical regions involved in these processes are also recruited during biological motion perception. Subjects viewed linear motion displays alternating with static dots and a series of common objects alternating with band-limited white noise patterns. Subjects also generated imagery of their own arm movements alternating with visual imagery of common objects. Biological motion specific BOLD signal was found within regions of the lingual gyrus at the cuneus border, showing little overlap with object recognition, linear motion or motion imagery areas. The lingual gyrus activation was replicated in a second experiment that also mapped retinotopic visual areas in three subjects. The results suggest that a region of the lingual gyrus within VP is involved in higher-order processing of motion information.

Adult↗
PubMed2002

Cerebellar plasticity and the ocular following response.

We constructed a realistic simulation model to elucidate whether the characteristics of the cerebellar synaptic plasticity reported in vitro guide the acquisition and adaptation of the ocular following response (OFR). The model reconstructed the firing frequency of the inputs of granule cell axons (GCA), inhibitory cells (IC), and climbing fibers (CF) to cerebellar Purkinje cells for the OFR, to simulate the reported cerebellar plasticity, including long-term depression, long-term potentiation, and rebound potentiation. When the model used the same visual inputs as reported for monkeys, it successfully simulated the real characteristics of simple spikes in Purkinje cells of adult monkeys and adaptation of gain and direction. The success of our simulation relied on the temporal relationship of the synaptic weight changes when CF inputs preceded GCA and IC inputs, corresponding to the relationship reported by Chen and Thompson and reanalysis of the data of Karachot et al. The success of our simulation strongly suggests that acquisition and adaptation of the OFR arise from cerebellar plasticity.

Animals↗
PubMed2002

Computational studies on acquisition and adaptation of ocular following responses based on cerebellar synaptic plasticity.

To investigate how cerebellar synaptic plasticity guides the acquisition and adaptation of ocular following response (OFR), a large-scale network model was developed. The model includes the cerebral medial superior temporal area (MST), Purkinje cells (P cells) of the ventral paraflocculus, the accessory optic and climbing fiber systems, the brain stem oculomotor network, and the oculomotor plant. The model reconstructed temporal profiles of both firing patterns of MST neurons and P cells and eye movements. Model MST neurons (n = 1,080) were set to be driven by retinal error and exhibited 12 preferred directions, 30 preferred velocities, and 3 firing waveforms. Correspondingly, each model P cell contained 1,080 excitatory synapses from granule cell axons (GCA) and 1,080 inhibitory synapses. P cells (n = 40) were classified into four groups by their laterality (hemisphere) and by preferred directions of their climbing fiber inputs (CF) (contralateral or upward). The brain stem neural circuit and the oculomotor plant were modeled on the work of Yamamoto et al. The initial synaptic weights on the P cells were set randomly. At the beginning, P cell simple spikes were not well modulated by visual motion, and the eye was moved only slightly by the accessory optic system. The synaptic weights were updated according to integral-differential equation models of physiologically demonstrated synaptic plasticity: long-term depression and long-term potentiation for GCA synapses and rebound potentiation for inhibitory synapses. We assumed that maximum plasticity was induced when GCA inputs preceded CF inputs by 200 ms. After more than 10,000 presentations of ramp-step visual motion, the strengths of both the excitatory and inhibitory synapses were modified. Subsequently, the simple spike responses became well developed, and ordinary OFRs were acquired. The preferred directions of simple spikes became the opposite of those of CFs. Although the model MST neurons were set to possess a wide variety of firing characteristics, the model P cells acquired only downward or ipsilateral preferred directions, high preferred velocities and stereotypical firing waveforms. Therefore the drastic transition of the neural representation from the population codes in the MST to the firing-rate codes of simple spikes were learned at the GCA-P cell synapses and inhibitory cells-P cell synapses. Furthermore, the model successfully reproduced the gain- and directional-adaptation of OFR, which was demonstrated by manipulating the velocity and direction of visual motion, respectively. When we assumed that synaptic plasticity could only occur if CF inputs preceded GCA inputs, the ordinary OFR were acquired but neither the gain-adaptation nor the directional adaptation could be reproduced.

Adaptation, Physiological↗
PubMed2002

Computational study on monkey VOR adaptation and smooth pursuit based on the parallel control-pathway theory.

Much controversy remains about the site of learning and memory for vestibuloocular reflex (VOR) adaptation in spite of numerous previous studies. One possible explanation for VOR adaptation is the flocculus hypothesis, which assumes that this adaptation is caused by synaptic plasticity in the cerebellar cortex. Another hypothesis is the model proposed by Lisberger that assumes that the learning that occurs in both the cerebellar cortex and the vestibular nucleus is necessary for VOR adaptation. Lisberger's model is characterized by a strong positive feedback loop carrying eye velocity information from the vestibular nucleus to the cerebellar cortex. This structure contributes to the maintenance of a smooth pursuit driving command with zero retinal slip during the steady-state phase of smooth pursuit with gain 1 or during the target blink condition. Here, we propose an alternative hypothesis that suggests that the pursuit driving command is maintained in the medial superior temporal (MST) area based on MST firing data during target blink and during ocular following blank, and as a consequence, we assume a much smaller gain for the positive feedback from the vestibular nucleus to the cerebellar cortex. This hypothesis is equivalent to assuming that there are two parallel neural pathways for controlling VOR and smooth pursuit: a main pathway of the semicircular canals to the vestibular nucleus for VOR, and a main pathway of the MST-dorsolateral pontine nuclei (DLPN)-flocculus/ventral paraflocculus to the vestibular nucleus for smooth pursuit. First, we theoretically demonstrate that this parallel control-pathway theory can reproduce the various firing patterns of horizontal gaze velocity Purkinje cells in the flocculus/ventral paraflocculus dependent on VOR in the dark, smooth pursuit, and VOR cancellation as reported in Miles et al. at least equally as well as the gaze velocity theory, which is the basic framework of Lisberger's model. Second, computer simulations based on our hypothesis can stably reproduce neural firing data as well as behavioral data obtained in smooth pursuit, VOR cancellation, and VOR adaptation, even if only plasticity in the cerebellar cortex is assumed. Furthermore, our computer simulation model can reproduce VOR adaptation automatically based on a heterosynaptic interaction model between parallel fiber inputs and climbing fiber inputs. Our results indicate that different assumptions about the site of pursuit driving command maintenance computationally lead to different conclusions about where the learning for VOR adaptation occurs. Finally, we propose behavioral and physiological experiments capable of discriminating between these two possibilities for the site of pursuit driving command maintenance and hence for the sites of learning and memory for VOR adaptation.

Adaptation, Physiological↗
PubMed2002

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

Adult↗
PubMed2002

Multiple model-based reinforcement learning.

We propose a modular reinforcement learning architecture for nonlinear, nonstationary control tasks, which we call multiple model-based reinforcement learning (MMRL). The basic idea is to decompose a complex task into multiple domains in space and time based on the predictability of the environmental dynamics. The system is composed of multiple modules, each of which consists of a state prediction model and a reinforcement learning controller. The "responsibility signal," which is given by the softmax function of the prediction errors, is used to weight the outputs of multiple modules, as well as to gate the learning of the prediction models and the reinforcement learning controllers. We formulate MMRL for both discrete-time, finite-state case and continuous-time, continuous-state case. The performance of MMRL was demonstrated for discrete case in a nonstationary hunting task in a grid world and for continuous case in a nonlinear, nonstationary control task of swinging up a pendulum with variable physical parameters.

Humans↗
PubMed2002

A method for estimating torque-vector directions of shoulder muscles using surface EMGs.

In this study, a new method is proposed to estimate the torque-vector directions of each shoulder muscle. The method is based on a multiple regression model that reconstructs shoulder torque, which is calculated from the hand force and posture, from the surface EMG of many muscles recorded simultaneously. The torque-vector directions of eleven shoulder muscles of four subjects were obtained at up to 30 different arm postures with this method. The mean confidence interval (p < 0.05) of the estimated torque-vector direction of each subject was 7.7-10.6 degrees. The correlation coefficient between the measured shoulder torque and reconstructed shoulder torque was between 0.76-0.84. The results for majority of the muscles were in accordance with previous studies, and reasonable from the viewpoint of anatomy. The torque-vector directions of a muscle, which are estimated with this method, have more of a functional meaning than a pure anatomical or mechanical one. These indicate the direction of the shoulder torque accompanying the muscle activation for a normal shoulder action that involves the cooperative contraction of many muscles.

Adult↗
PubMed1998

A tennis serve and upswing learning robot based on bi-directional theory.

We experimented on task-level robot learning based on bi-directional theory. The via-point representation was used for 'learning by watching'. In our previous work, we had a robot learn kendama (a Japanese game) in order to demonstrate a single simple task. Our approach can be applied to a wide variety of motor behavior. However, some difficulties still remain. In this paper, we address two problems: (1) how to attain a final goal of complex movement when it consists of a sequence of subgoals, and (2) how to adapt to changes in behavior and the environment. To examine how to solve these problems, we propose two methods: (1) selecting the proper via-points for a control variable for each subgoal, and (2) re-estimating the relation between the via-points and the task during learning without conducting extra trials. We adopted a tennis serve and a pendulum upswing for our complicated tasks.

Journal Article↗
PubMed1996

A Kendama Learning Robot Based on Bi-directional Theory.

A general theory of movement-pattern perception based on bi-directional theory for sensory-motor integration can be used for motion capture and learning by watching in robotics. We demonstrate our methods using the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has a very similar kinematic structure to the human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients are (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution. In order to test the validity of the via-point representation, we utilized a numerical model of the SARCOS arm, and examined the behavior of the system under several conditions. Copyright 1996 Elsevier Science Ltd.

Journal Article↗