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Kim KS, Gaines JL, Parrell B, Ramanarayanan V, Nagarajan SS, Houde JF. Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech. PLoS Comput Biol 2023; 19:e1011244. [PMID: 37506120 PMCID: PMC10434967 DOI: 10.1371/journal.pcbi.1011244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 08/17/2023] [Accepted: 06/06/2023] [Indexed: 07/30/2023] Open
Abstract
Upon perceiving sensory errors during movements, the human sensorimotor system updates future movements to compensate for the errors, a phenomenon called sensorimotor adaptation. One component of this adaptation is thought to be driven by sensory prediction errors-discrepancies between predicted and actual sensory feedback. However, the mechanisms by which prediction errors drive adaptation remain unclear. Here, auditory prediction error-based mechanisms involved in speech auditory-motor adaptation were examined via the feedback aware control of tasks in speech (FACTS) model. Consistent with theoretical perspectives in both non-speech and speech motor control, the hierarchical architecture of FACTS relies on both the higher-level task (vocal tract constrictions) as well as lower-level articulatory state representations. Importantly, FACTS also computes sensory prediction errors as a part of its state feedback control mechanism, a well-established framework in the field of motor control. We explored potential adaptation mechanisms and found that adaptive behavior was present only when prediction errors updated the articulatory-to-task state transformation. In contrast, designs in which prediction errors updated forward sensory prediction models alone did not generate adaptation. Thus, FACTS demonstrated that 1) prediction errors can drive adaptation through task-level updates, and 2) adaptation is likely driven by updates to task-level control rather than (only) to forward predictive models. Additionally, simulating adaptation with FACTS generated a number of important hypotheses regarding previously reported phenomena such as identifying the source(s) of incomplete adaptation and driving factor(s) for changes in the second formant frequency during adaptation to the first formant perturbation. The proposed model design paves the way for a hierarchical state feedback control framework to be examined in the context of sensorimotor adaptation in both speech and non-speech effector systems.
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Affiliation(s)
- Kwang S. Kim
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Jessica L. Gaines
- Graduate Program in Bioengineering, University of California Berkeley-University of California San Francisco, San Francisco, California, United States of America
| | - Benjamin Parrell
- Department of Communication Sciences and Disorders, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Vikram Ramanarayanan
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, California, United States of America
- Modality.AI, San Francisco, California, United States of America
| | - Srikantan S. Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
| | - John F. Houde
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, California, United States of America
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2
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Ilan Y. Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 180-181:37-48. [PMID: 37068713 DOI: 10.1016/j.pbiomolbio.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/26/2023] [Accepted: 04/14/2023] [Indexed: 04/19/2023]
Abstract
The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.
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Affiliation(s)
- Yaron Ilan
- Faculty of Medicine, Hebrew University, Department of Medicine, Hadassah Medical Center, Jerusalem, Israel.
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3
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Lucchese A, Digiesi S, Mummolo C. Analytical-stochastic model of motor difficulty for three-dimensional manipulation tasks. PLoS One 2022; 17:e0276308. [PMID: 36260600 PMCID: PMC9581359 DOI: 10.1371/journal.pone.0276308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/05/2022] [Indexed: 11/05/2022] Open
Abstract
Multiple models exist for the evaluation of human motor performance; some of these rely on the Index of Difficulty (ID), a measure to evaluate the difficulty associated to simple reaching tasks. Despite the numerous applications of the ID in reaching movements, the existing formulations are functions of the geometrical features of the task and do not consider the motor behaviour of subjects performing repetitive movements in interaction with the environment. Variability of movements, length of trajectories, subject-specific strength and skill, and required interaction with the environment are all factors that contribute to the motor difficulty experienced by a moving agent (e.g., human, robot) as it repeatedly interacts with the environment during a given task (e.g., target-reaching movement, locomotion, etc.). A novel concept of motor difficulty experienced by an agent executing repetitive end-effector movements is presented in this study. A stochastic ID formulation is proposed that captures the abovementioned factors and applies to general three-dimensional motor tasks. Natural motor variability, inherent in the proposed model, is representative of the flexibility in motor synergies for a given agent-environment interaction: the smaller the flexibility, the greater the experienced difficulty throughout the movement. The quantification of experienced motor difficulty is demonstrated for the case of young healthy subjects performing three-dimensional arm movements during which different objects are manipulated. Results show that subjects’ experienced motor difficulty is influenced by the type of object. In particular, a difference in motor difficulty is observed when manipulating objects with different grasp types. The proposed model can be employed as a novel tool to evaluate the motor performance of agents involved in repetitive movements, such as in pick and place and manipulation, with application in both industrial and rehabilitation contexts.
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Affiliation(s)
- Andrea Lucchese
- Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Bari, Italy
- * E-mail:
| | - Salvatore Digiesi
- Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Bari, Italy
| | - Carlotta Mummolo
- Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Bari, Italy
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4
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What is the nature of motor adaptation to dynamic perturbations? PLoS Comput Biol 2022; 18:e1010470. [PMID: 36040962 PMCID: PMC9467354 DOI: 10.1371/journal.pcbi.1010470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/12/2022] [Accepted: 08/04/2022] [Indexed: 11/19/2022] Open
Abstract
When human participants repeatedly encounter a velocity-dependent force field that distorts their movement trajectories, they adapt their motor behavior to recover straight trajectories. Computational models suggest that adaptation to a force field occurs at the action selection level through changes in the mapping between goals and actions. The quantitative prediction from these models indicates that early perturbed trajectories before adaptation and late unperturbed trajectories after adaptation should have opposite curvature, i.e. one being a mirror image of the other. We tested these predictions in a human adaptation experiment and we found that the expected mirror organization was either absent or much weaker than predicted by the models. These results are incompatible with adaptation occurring at the action selection level but compatible with adaptation occurring at the goal selection level, as if adaptation corresponds to aiming toward spatially remapped targets. Motor adaptation is a fundamental component of the acquisition and maintenance of skilled behaviors. Yet the nature of motor adaptation remains poorly understood: when we encounter forces which repeatedly perturb our movements, do we change our actions or our plans? Current computational models of motor control favor the former, but this assumption has not been thoroughly investigated. To address this issue, we compared predictions of a model of motor adaptation based on changes at the action level with observations obtained from a group of human participants involved in a motor adaptation task. The behavior of the participants clearly differed from the model’s predictions. These results challenge contemporary perspectives on motor adaptation.
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Gori J, Rioul O. A feedback information-theoretic transmission scheme (FITTS) for modeling trajectory variability in aimed movements. BIOLOGICAL CYBERNETICS 2020; 114:621-641. [PMID: 33289880 DOI: 10.1007/s00422-020-00853-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
Trajectories in human aimed movements are inherently variable. Using the concept of positional variance profiles, such trajectories are shown to be decomposable into two phases: In a first phase, the variance of the limb position over many trajectories increases rapidly; in a second phase, it then decreases steadily. A new theoretical model, where the aiming task is seen as a Shannon-like communication problem, is developed to describe the second phase: Information is transmitted from a "source" (determined by the position at the end of the first phase) to a "destination" (the movement's end-point) over a "channel" perturbed by Gaussian noise, with the presence of a noiseless feedback link. Information-theoretic considerations show that the positional variance decreases exponentially with a rate equal to the channel capacity C. Two existing datasets for simple pointing tasks are re-analyzed and observations on real data confirm our model. The first phase has constant duration, and C is found constant across instructions and task parameters, which thus characterizes the participant's performance. Our model provides a clear understanding of the speed-accuracy tradeoff in aimed movements: Since the participant's capacity is fixed, a higher prescribed accuracy necessarily requires a longer second phase resulting in an increased overall movement time. The well-known Fitts' law is also recovered using this approach.
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Affiliation(s)
- Julien Gori
- LRI, Université Paris-Saclay, CNRS, Inria, 91400, Orsay, France.
| | - Olivier Rioul
- LTCI, Télécom Paris, Institut Polytechnique de Paris, 91120, Palaiseau, France
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6
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Time-to-Target Simplifies Optimal Control of Visuomotor Feedback Responses. eNeuro 2020; 7:ENEURO.0514-19.2020. [PMID: 32213555 PMCID: PMC7189480 DOI: 10.1523/eneuro.0514-19.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 02/13/2020] [Accepted: 03/01/2020] [Indexed: 11/21/2022] Open
Abstract
Visuomotor feedback responses vary in intensity throughout a reach, commonly explained by optimal control. Here, we show that the optimal control for a range of movements with the same goal can be simplified to a time-to-target dependent control scheme. We measure our human participants’ visuomotor responses in five reaching conditions, each with different hand or cursor kinematics. Participants only produced different feedback responses when these kinematic changes resulted in different times-to-target. We complement our experimental data with a range of finite and non-finite horizon optimal feedback control (OFC) models, finding that the model with time-to-target as one of the input parameters best replicates the experimental data. Overall, this suggests that time-to-target is a critical control parameter in online feedback control. Moreover, we propose that for a specific task and known dynamics, humans can instantly produce a control signal without any additional online computation allowing rapid response onset and close to optimal control.
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7
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Berret B, Jean F. Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction. PLoS Comput Biol 2020; 16:e1007414. [PMID: 32109941 PMCID: PMC7065824 DOI: 10.1371/journal.pcbi.1007414] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 03/11/2020] [Accepted: 12/23/2019] [Indexed: 11/22/2022] Open
Abstract
Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Stochastic optimal (closed-loop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The proposed stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement. This study presents a novel computational theory to explain the planning of force and impedance (e.g. viscoelasticity) in the neural control of movement. It assumes that one main goal of motor planning is to elaborate feedforward motor commands that determine both the force and the impedance required for the task at hand. These feedforward motor commands (i.e. that are defined prior to movement execution) are designed to minimize effort and variance costs considering the uncertainty arising from sensorimotor or environmental noise. A major outcome of this mathematical framework is the explanation of muscle co-contraction (i.e. the concurrent contraction of a group of muscles involved in a motor function). Muscle co-contraction has been shown to occur in many situations but previous modeling works struggled to account for it. Although effortful, co-contraction contributes to increase the robustness of motor behavior (e.g. small variance) upstream of sophisticated optimal closed-loop control processes that require state estimation from delayed sensory feedback to function. This work may have implications regarding our understanding of the neural control of movement in computational terms. It also provides a theoretical ground to explain how to optimally plan force and impedance within a general and versatile framework.
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Affiliation(s)
- Bastien Berret
- Université Paris-Saclay CIAMS, Orsay, France
- CIAMS, Université d’Orléans, Orléans, France
- Institut Universitaire de France, Paris, France
- * E-mail:
| | - Frédéric Jean
- Unité de Mathématiques Appliquées, ENSTA Paris, Institut Polytechnique de Paris, Palaiseau, France
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8
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Mohan V, Bhat A, Morasso P. Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics. Phys Life Rev 2019; 30:89-111. [DOI: 10.1016/j.plrev.2018.04.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 04/12/2018] [Accepted: 04/16/2018] [Indexed: 10/17/2022]
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9
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Guigon E, Chafik O, Jarrassé N, Roby-Brami A. Experimental and theoretical study of velocity fluctuations during slow movements in humans. J Neurophysiol 2019; 121:715-727. [PMID: 30649981 DOI: 10.1152/jn.00576.2018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Moving smoothly is generally considered as a higher-order goal of motor control and moving jerkily as a witness of clumsiness or pathology, yet many common and well-controlled movements (e.g., tracking movements) have irregular velocity profiles with widespread fluctuations. The origin and nature of these fluctuations have been associated with the operation of an intermittent process but in fact remain poorly understood. Here we studied velocity fluctuations during slow movements, using combined experimental and theoretical tools. We recorded arm movement trajectories in a group of healthy participants performing back-and-forth movements at different speeds, and we analyzed velocity profiles in terms of series of segments (portions of velocity between 2 minima). We found that most of the segments were smooth (i.e., corresponding to a biphasic acceleration) and had constant duration irrespective of movement speed and linearly increasing amplitude with movement speed. We accounted for these observations with an optimal feedback control model driven by a staircase goal position signal in the presence of sensory noise. Our study suggests that one and the same control process can explain the production of fast and slow movements, i.e., fast movements emerge from the immediate tracking of a global goal position and slow movements from the successive tracking of intermittently updated intermediate goal positions. NEW & NOTEWORTHY We show in experiments and modeling that slow movements could result from the brain tracking a sequence of via points regularly distributed in time and space. Accordingly, slow movements would differ from fast movement by the nature of the guidance and not by the nature of control. This result could help in understanding the origin and nature of slow and segmented movements frequently observed in brain disorders.
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Affiliation(s)
- Emmanuel Guigon
- Institut des Systèmes Intelligents et de Robotique, CNRS, Sorbonne Université , Paris , France
| | - Oussama Chafik
- Institut des Systèmes Intelligents et de Robotique, CNRS, Sorbonne Université , Paris , France
| | - Nathanaël Jarrassé
- Institut des Systèmes Intelligents et de Robotique, CNRS, Sorbonne Université , Paris , France
| | - Agnès Roby-Brami
- Institut des Systèmes Intelligents et de Robotique, CNRS, Sorbonne Université , Paris , France
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10
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Peternel L, Sigaud O, Babič J. Unifying Speed-Accuracy Trade-Off and Cost-Benefit Trade-Off in Human Reaching Movements. Front Hum Neurosci 2017; 11:615. [PMID: 29379424 PMCID: PMC5770750 DOI: 10.3389/fnhum.2017.00615] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 12/05/2017] [Indexed: 11/18/2022] Open
Abstract
Two basic trade-offs interact while our brain decides how to move our body. First, with the cost-benefit trade-off, the brain trades between the importance of moving faster toward a target that is more rewarding and the increased muscular cost resulting from a faster movement. Second, with the speed-accuracy trade-off, the brain trades between how accurate the movement needs to be and the time it takes to achieve such accuracy. So far, these two trade-offs have been well studied in isolation, despite their obvious interdependence. To overcome this limitation, we propose a new model that is able to simultaneously account for both trade-offs. The model assumes that the central nervous system maximizes the expected utility resulting from the potential reward and the cost over the repetition of many movements, taking into account the probability to miss the target. The resulting model is able to account for both the speed-accuracy and the cost-benefit trade-offs. To validate the proposed hypothesis, we confront the properties of the computational model to data from an experimental study where subjects have to reach for targets by performing arm movements in a horizontal plane. The results qualitatively show that the proposed model successfully accounts for both cost-benefit and speed-accuracy trade-offs.
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Affiliation(s)
- Luka Peternel
- HRII Lab, Advanced Robotics, Istituto Italiano di Technologia, Genoa, Italy.,Department for Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Olivier Sigaud
- Sorbonne Universités, UPMC Univ Paris 06, CNRS UMR 7222, Institut des Systèmes Intelligents et de Robotique, Paris, France
| | - Jan Babič
- Department for Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia
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11
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Abstract
Optimal control models of biological movements introduce external task factors to specify the pace of movements. Here, we present the dual to the principle of optimality based on a conserved quantity, called "drive," that represents the influence of internal motivation level on movement pace. Optimal control and drive conservation provide equivalent descriptions for the regularities observed within individual movements. For regularities across movements, drive conservation predicts a previously unidentified scaling law between the overall size and speed of various self-paced hand movements in the absence of any external tasks, which we confirmed with psychophysical experiments. Drive can be interpreted as a high-level control variable that sets the overall pace of movements and may be represented in the brain as the tonic levels of neuromodulators that control the level of internal motivation, thus providing insights into how internal states affect biological motor control.
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12
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Takemura N, Fukui T, Inui T. A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability. Front Comput Neurosci 2015; 9:143. [PMID: 26696874 PMCID: PMC4675317 DOI: 10.3389/fncom.2015.00143] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 11/09/2015] [Indexed: 11/29/2022] Open
Abstract
In human reach-to-grasp movement, visual occlusion of a target object leads to a larger peak grip aperture compared to conditions where online vision is available. However, no previous computational and neural network models for reach-to-grasp movement explain the mechanism of this effect. We simulated the effect of online vision on the reach-to-grasp movement by proposing a computational control model based on the hypothesis that the grip aperture is controlled to compensate for both motor variability and sensory uncertainty. In this model, the aperture is formed to achieve a target aperture size that is sufficiently large to accommodate the actual target; it also includes a margin to ensure proper grasping despite sensory and motor variability. To this end, the model considers: (i) the variability of the grip aperture, which is predicted by the Kalman filter, and (ii) the uncertainty of the object size, which is affected by visual noise. Using this model, we simulated experiments in which the effect of the duration of visual occlusion was investigated. The simulation replicated the experimental result wherein the peak grip aperture increased when the target object was occluded, especially in the early phase of the movement. Both predicted motor variability and sensory uncertainty play important roles in the online visuomotor process responsible for grip aperture control.
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Affiliation(s)
- Naohiro Takemura
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Yoshida-honmachi Kyoto, Japan
| | - Takao Fukui
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Yoshida-honmachi Kyoto, Japan
| | - Toshio Inui
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Yoshida-honmachi Kyoto, Japan
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13
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Okorokova E, Lebedev M, Linderman M, Ossadtchi A. A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings. Front Neurosci 2015; 9:389. [PMID: 26578856 PMCID: PMC4624865 DOI: 10.3389/fnins.2015.00389] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 10/05/2015] [Indexed: 11/13/2022] Open
Abstract
In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed.
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Affiliation(s)
- Elizaveta Okorokova
- Centre for Cognition and Decision Making, National Research University Higher School of Economics Moscow, Russia
| | | | - Michael Linderman
- Department of Biomedical Engineering, Norconnect Inc. Ogdensburg, NY, USA
| | - Alex Ossadtchi
- Centre for Cognition and Decision Making, National Research University Higher School of Economics Moscow, Russia ; Laboratory of Control of Complex Systems, Institute of Problems of Mechanical Engineering, Russian Academy of Sciences St. Petersburg, Russia
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14
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Houde JF, Chang EF. The cortical computations underlying feedback control in vocal production. Curr Opin Neurobiol 2015; 33:174-81. [PMID: 25989242 DOI: 10.1016/j.conb.2015.04.006] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Revised: 04/14/2015] [Accepted: 04/27/2015] [Indexed: 11/26/2022]
Abstract
Recent neurophysiological studies of speaking are beginning to elucidate the neural mechanisms underlying auditory feedback processing during vocalizations. Here we review how research findings impact our state feedback control (SFC) model of speech motor control. We will discuss the evidence for cortical computations that compare incoming feedback with predictions derived from motor efference copy. We will also review observations from auditory feedback perturbation studies that demonstrate clear evidence for a state estimate correction process, which drives compensatory motor behavioral responses. While there is compelling support for cortical computations in the SFC model, there are still several outstanding questions that await resolution by future neural investigations.
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Affiliation(s)
- John F Houde
- Department of Otolaryngology - Head and Neck Surgery, University of California, San Francisco, United States.
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, United States.
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15
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Cusumano JP, Dingwell JB. Movement variability near goal equivalent manifolds: fluctuations, control, and model-based analysis. Hum Mov Sci 2013; 32:899-923. [PMID: 24210574 DOI: 10.1016/j.humov.2013.07.019] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Revised: 07/09/2013] [Accepted: 07/21/2013] [Indexed: 11/30/2022]
Abstract
Fluctuations in the repeated performance of human movements have been the subject of intense scrutiny because they are generally believed to contain important information about the function and health of the neuromotor system. A variety of approaches has been brought to bear to study these fluctuations. However it is frequently difficult to understand how to synthesize different perspectives to give a coherent picture. Here, we describe a conceptual framework for the experimental study of motor variability that helps to unify geometrical methods, which focus on the role of motor redundancy, with dynamical methods that characterize the error-correcting processes regulating the performance of skilled tasks. We describe how goal functions, which mathematically specify the task strategy being employed, together with ideas from the control of redundant systems, allow one to formulate simple, experimentally testable dynamical models of inter-trial fluctuations. After reviewing the basic theory, we present a list of five general hypotheses on the structure of fluctuations that can be expected in repeated trials of goal-directed tasks. We review recent experimental applications of this general approach, and show how it can be used to precisely characterize the error-correcting control used by human subjects.
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Affiliation(s)
- Joseph P Cusumano
- Dept. of Engineering Science & Mechanics, Penn State University, University Park, PA 16802, USA.
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16
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Nguyen HP, Dingwell JB. Proximal versus distal control of two-joint planar reaching movements in the presence of neuromuscular noise. J Biomech Eng 2013; 134:061007. [PMID: 22757504 DOI: 10.1115/1.4006811] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Determining how the human nervous system contends with neuro-motor noise is vital to understanding how humans achieve accurate goal-directed movements. Experimentally, people learning skilled tasks tend to reduce variability in distal joint movements more than in proximal joint movements. This suggests that they might be imposing greater control over distal joints than proximal joints. However, the reasons for this remain unclear, largely because it is not experimentally possible to directly manipulate either the noise or the control at each joint independently. Therefore, this study used a 2 degree-of-freedom torque driven arm model to determine how different combinations of noise and/or control independently applied at each joint affected the reaching accuracy and the total work required to make the movement. Signal-dependent noise was simultaneously and independently added to the shoulder and elbow torques to induce endpoint errors during planar reaching. Feedback control was then applied, independently and jointly, at each joint to reduce endpoint error due to the added neuromuscular noise. Movement direction and the inertia distribution along the arm were varied to quantify how these biomechanical variations affected the system performance. Endpoint error and total net work were computed as dependent measures. When each joint was independently subjected to noise in the absence of control, endpoint errors were more sensitive to distal (elbow) noise than to proximal (shoulder) noise for nearly all combinations of reaching direction and inertia ratio. The effects of distal noise on endpoint errors were more pronounced when inertia was distributed more toward the forearm. In contrast, the total net work decreased as mass was shifted to the upper arm for reaching movements in all directions. When noise was present at both joints and joint control was implemented, controlling the distal joint alone reduced endpoint errors more than controlling the proximal joint alone for nearly all combinations of reaching direction and inertia ratio. Applying control only at the distal joint was more effective at reducing endpoint errors when more of the mass was more proximally distributed. Likewise, controlling the distal joint alone required less total net work than controlling the proximal joint alone for nearly all combinations of reaching distance and inertia ratio. It is more efficient to reduce endpoint error and energetic cost by selectively applying control to reduce variability in the distal joint than the proximal joint. The reasons for this arise from the biomechanical configuration of the arm itself.
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Affiliation(s)
- Hung P Nguyen
- Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712, USA
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Rigoux L, Guigon E. A model of reward- and effort-based optimal decision making and motor control. PLoS Comput Biol 2012; 8:e1002716. [PMID: 23055916 PMCID: PMC3464194 DOI: 10.1371/journal.pcbi.1002716] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Accepted: 08/10/2012] [Indexed: 11/19/2022] Open
Abstract
Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control.
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Affiliation(s)
- Lionel Rigoux
- UPMC Univ Paris 06, UMR 7222, ISIR, Paris, France
- CNRS, UMR 7222, ISIR, Paris, France
| | - Emmanuel Guigon
- UPMC Univ Paris 06, UMR 7222, ISIR, Paris, France
- CNRS, UMR 7222, ISIR, Paris, France
- * E-mail:
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Marin D, Sigaud O. A machine learning approach to reaching tasks. Comput Methods Biomech Biomed Engin 2012; 15 Suppl 1:151-2. [DOI: 10.1080/10255842.2012.713684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Mohan V, Morasso P. Passive motion paradigm: an alternative to optimal control. Front Neurorobot 2011; 5:4. [PMID: 22207846 PMCID: PMC3246361 DOI: 10.3389/fnbot.2011.00004] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2011] [Accepted: 11/29/2011] [Indexed: 11/25/2022] Open
Abstract
IN THE LAST YEARS, OPTIMAL CONTROL THEORY (OCT) HAS EMERGED AS THE LEADING APPROACH FOR INVESTIGATING NEURAL CONTROL OF MOVEMENT AND MOTOR COGNITION FOR TWO COMPLEMENTARY RESEARCH LINES: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the "degrees of freedom (DoFs) problem," the common core of production, observation, reasoning, and learning of "actions." OCT, directly derived from engineering design techniques of control systems quantifies task goals as "cost functions" and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative "softer" approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that "animates" the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints "at runtime," hence solving the "DoFs problem" without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of "potential actions." In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures.
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Affiliation(s)
- Vishwanathan Mohan
- Robotics, Brain and Cognitive Sciences Department, Istituto Italiano di Tecnologia Genoa, Italy
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20
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Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state. J Comput Neurosci 2011; 32:563-76. [DOI: 10.1007/s10827-011-0370-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2011] [Revised: 10/08/2011] [Accepted: 10/13/2011] [Indexed: 10/15/2022]
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21
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Houde JF, Nagarajan SS. Speech production as state feedback control. Front Hum Neurosci 2011; 5:82. [PMID: 22046152 PMCID: PMC3200525 DOI: 10.3389/fnhum.2011.00082] [Citation(s) in RCA: 278] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2011] [Accepted: 07/27/2011] [Indexed: 11/13/2022] Open
Abstract
Spoken language exists because of a remarkable neural process. Inside a speaker's brain, an intended message gives rise to neural signals activating the muscles of the vocal tract. The process is remarkable because these muscles are activated in just the right way that the vocal tract produces sounds a listener understands as the intended message. What is the best approach to understanding the neural substrate of this crucial motor control process? One of the key recent modeling developments in neuroscience has been the use of state feedback control (SFC) theory to explain the role of the CNS in motor control. SFC postulates that the CNS controls motor output by (1) estimating the current dynamic state of the thing (e.g., arm) being controlled, and (2) generating controls based on this estimated state. SFC has successfully predicted a great range of non-speech motor phenomena, but as yet has not received attention in the speech motor control community. Here, we review some of the key characteristics of speech motor control and what they say about the role of the CNS in the process. We then discuss prior efforts to model the role of CNS in speech motor control, and argue that these models have inherent limitations – limitations that are overcome by an SFC model of speech motor control which we describe. We conclude by discussing a plausible neural substrate of our model.
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Affiliation(s)
- John F Houde
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco San Francisco, CA, USA
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Berret B, Chiovetto E, Nori F, Pozzo T. Manifold reaching paradigm: how do we handle target redundancy? J Neurophysiol 2011; 106:2086-102. [PMID: 21734107 DOI: 10.1152/jn.01063.2010] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
How the central nervous system coordinates the many intrinsic degrees of freedom of the musculoskeletal system is a recurrent question in motor control. Numerous studies addressed it by considering redundant reaching tasks such as point-to-point arm movements, for which many joint trajectories and muscle activations are usually compatible with a single goal. There exists, however, a different, extrinsic kind of redundancy that is target redundancy. Many times, indeed, the final point to reach is neither specified nor unique. In this study, we aim to understand how the central nervous system tackles such an extrinsic redundancy by considering a reaching-to-a-manifold paradigm, more specifically an arm pointing to a long vertical bar. In this case, the endpoint is not defined a priori and, therefore, subjects are free to choose any point on the bar to successfully achieve the task. We investigated the strategies used by subjects to handle this presented choice. Our results indicate both intersubject and intertrial consistency with respect to the freedom provided by the task. However, the subjects' behavior is found to be more variable than during classical point-to-point reaches. Interestingly, the average arm trajectories to the bar and the structure of intertrial endpoint variations could be explained via stochastic optimal control with an energy/smoothness expected cost and signal-dependent motor noise. We conclude that target redundancy is first overcome during movement planning and then exploited during movement execution, in agreement with stochastic optimal feedback control principles, which illustrates how the complementary problems of goal and movement selection may be resolved at once.
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Affiliation(s)
- Bastien Berret
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy.
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Pointing with the wrist: a postural model for Donders' law. Exp Brain Res 2011; 212:417-27. [PMID: 21643712 DOI: 10.1007/s00221-011-2747-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2011] [Accepted: 05/19/2011] [Indexed: 10/18/2022]
Abstract
The central nervous system uses stereotypical combinations of the three wrist/forearm joint angles to point in a given (2D) direction in space. In this paper, we first confirm and analyze this Donders' law for the wrist as well as the distributions of the joint angles. We find that the quadratic surfaces fitting the experimental wrist configurations during pointing tasks are characterized by a subject-specific Koenderink shape index and by a bias due to the prono-supination angle distribution. We then introduce a simple postural model using only four parameters to explain these characteristics in a pointing task. The model specifies the redundancy of the pointing task by determining the one-dimensional task-equivalent manifold (TEM), parameterized via wrist torsion. For every pointing direction, the torsion is obtained by the concurrent minimization of an extrinsic cost, which guarantees minimal angle rotations (similar to Listing's law for eye movements) and of an intrinsic cost, which penalizes wrist configurations away from comfortable postures. This allows simulating the sequence of wrist orientations to point at eight peripheral targets, from a central one, passing through intermediate points. The simulation first shows that in contrast to eye movements, which can be predicted by only considering the extrinsic cost (i.e., Listing's law), both costs are necessary to account for the wrist/forearm experimental data. Second, fitting the synthetic Donders' law from the simulated task with a quadratic surface yields similar fitting errors compared to experimental data.
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Guigon E. Active Control of Bias for the Control of Posture and Movement. J Neurophysiol 2010; 104:1090-102. [DOI: 10.1152/jn.00162.2010] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Posture and movement are fundamental, intermixed components of motor coordination. Current approaches consider either that 1) movement is an active, anticipatory process and posture is a passive feedback process or 2) movement and posture result from a common passive process. In both cases, the presence of a passive component renders control scarcely robust and stable in the face of transmission delays and low feedback gains. Here we show in a model that posture and movement could result from the same active process: an optimal feedback control that drives the body from its estimated state to its goal in a given (planning) time by acting through muscles on the insertion position (bias) of compliant linkages (tendons). Computer simulations show that iteration of this process in the presence of noise indifferently produces realistic postural sway, fast goal-directed movements, and natural transitions between posture and movement.
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Affiliation(s)
- Emmanuel Guigon
- UPMC University, Paris 06, UMR 7222, ISIR, F-75005, Paris; and CNRS, UMR 7222, ISIR, F-75005, Paris, France
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Ranganathan R, Newell KM. Influence of motor learning on utilizing path redundancy. Neurosci Lett 2010; 469:416-20. [PMID: 20035835 DOI: 10.1016/j.neulet.2009.12.041] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2009] [Revised: 11/30/2009] [Accepted: 12/15/2009] [Indexed: 11/29/2022]
Affiliation(s)
- Rajiv Ranganathan
- Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802-6501, USA.
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Nishii J, Taniai Y. Evaluation of trajectory planning models for arm-reaching movements based on energy cost. Neural Comput 2009; 21:2634-47. [PMID: 19548798 DOI: 10.1162/neco.2009.06-08-798] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Computational studies have suggested that many characteristics of reaching trajectories in a horizontal plane can be effectively predicted by certain models, including, the minimum end point variance model and minimum torque change model. It has also been reported that these characteristics appear to differ from those obtained by the minimum energy cost model that has been reported to explain the characteristics of locomotor patterns. Do these results imply that the human nervous system uses different strategies to resolve the redundancy problem for different tasks? In order to reexamine the optimality of reaching trajectories from a viewpoint of energy cost, we considered the corrective submovements to compensate for positional error due to signal-dependent noise in motor commands and computed the expected value of the total energy costs required to reach a target by repetition of submovements planned by each of the following models: the minimum energy cost model, minimum end point variance model, and minimum torque change model. The results revealed that when the noise is large, the total energy cost required by the minimum end point variance model and the minimum torque change model can be lower than that required by the minimum energy cost model which assumes minimizing energy cost under noise-free condition. This result indicates that the minimization of the expected value of the energy cost would be an important factor in determining the reaching trajectories.
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Affiliation(s)
- J Nishii
- Graduate School of Science and Engineering, Yamaguchi University, Yoshida 1677-1, 753-8512 Yamaguchi, Japan.
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Abstract
Speed/accuracy trade-off is a ubiquitous phenomenon in motor behaviour, which has been ascribed to the presence of signal-dependent noise (SDN) in motor commands. Although this explanation can provide a quantitative account of many aspects of motor variability, including Fitts' law, the fact that this law is frequently violated, e.g. during the acquisition of new motor skills, remains unexplained. Here, we describe a principled approach to the influence of noise on motor behaviour, in which motor variability results from the interplay between sensory and motor execution noises in an optimal feedback-controlled system. In this framework, we first show that Fitts' law arises due to signal-dependent motor noise (SDN(m)) when sensory (proprioceptive) noise is low, e.g. under visual feedback. Then we show that the terminal variability of non-visually guided movement can be explained by the presence of signal-dependent proprioceptive noise. Finally, we show that movement accuracy can be controlled by opposite changes in signal-dependent sensory (SDN(s)) and SDN(m), a phenomenon that could be ascribed to muscular co-contraction. As the model also explains kinematics, kinetics, muscular and neural characteristics of reaching movements, it provides a unified framework to address motor variability.
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Affiliation(s)
- Emmanuel Guigon
- INSERM U742, ANIM, Université Pierre et Marie Curie (UPMC - Paris 6), 9, quai Saint-Bernard, 75005 Paris, France.
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