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Berret B, Conessa A, Schweighofer N, Burdet E. Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision. PLoS Comput Biol 2021; 17:e1009047. [PMID: 34115757 PMCID: PMC8221793 DOI: 10.1371/journal.pcbi.1009047] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 06/23/2021] [Accepted: 05/05/2021] [Indexed: 11/18/2022] Open
Abstract
Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.
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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:
| | - Adrien Conessa
- Université Paris-Saclay CIAMS, Orsay, France
- CIAMS, Université d’Orléans, Orléans, France
| | - Nicolas Schweighofer
- University of Southern California, Los Angeles, California, United States of America
| | - Etienne Burdet
- University of Southern California, Los Angeles, California, United States of America
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2
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Ikegami T, Ganesh G, Gibo TL, Yoshioka T, Osu R, Kawato M. Hierarchical motor adaptations negotiate failures during force field learning. PLoS Comput Biol 2021; 17:e1008481. [PMID: 33872304 PMCID: PMC8084335 DOI: 10.1371/journal.pcbi.1008481] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/29/2021] [Accepted: 03/24/2021] [Indexed: 11/19/2022] Open
Abstract
Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of ‘internal model adaptation’. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans. How do we improve actions after a movement failure? Although negotiating movement failures is obviously crucial, previous motor-control studies have predominantly examined human movement adaptations in the absence of failures, and it remains unclear how failures affect subsequent movement adaptations. Here we examined this issue by developing a novel force field adaptation task where the hand movement during an arm reaching is perturbed by novel forces that induce a large target error, that is a failure. Our experimental observation and computational modeling show that, in addition to the popular ‘internal model learning’ process of motor adaptations, humans also utilize a ‘failure-negotiating’ process, that enables them to quickly improve movements in the presence of failure, even at the expense of increased arm trajectory deflections, which are subsequently reduced gradually with training after the achievement of the task success. Our results suggest that a hierarchical interaction between these two processes is a key for humans to negotiate movement failures.
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Affiliation(s)
- Tsuyoshi Ikegami
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
- * E-mail:
| | - Gowrishankar Ganesh
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Centre National de la Recherche Scientifique (CNRS), Universite Montpellier (UM) Laboratoire d’Informatique, de Robotique et de Microelectronique de, Montpellier (LIRMM), Montpellier, France
| | - Tricia L. Gibo
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Emergo by UL, Utrecht, The Netherlands
| | - Toshinori Yoshioka
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
| | - Rieko Osu
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
- Faculty of Human Sciences, Waseda University, Saitama, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, ATR, Kyoto, Japan
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Boehm JR, Fey NP, Majewicz A. Inherent Kinematic Features of Dynamic Bimanual Path Following Tasks. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2020; 50:613-622. [PMID: 36238926 PMCID: PMC9555814 DOI: 10.1109/thms.2020.3016084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Bimanual coordination is critical in many robotic and haptic systems, such as surgical robots and rehabilitation robots. While these systems often incorporate two robotic manipulators for each limb, there may be a missed opportunity to leverage overarching models of human bimanual coordination to improve the way in which the robotic manipulators are controlled and respond to the dynamic human operator. In this paper, we study the influences of several bimanual motion factors (e.g., symmetry and direction) on kinematic human joint-space features and performance outcome task-space features in a user study with eleven subjects and two haptic devices. Additionally, we evaluated the ability to use joint-space features to classify types of bimanual movement, showing the potential for a robotic system to predict how users coordinate their limbs. Three classifiers: (1) likelihood ratio, (2) k-nearest neighbor, and (3) support vector machine, were evaluated for classification accuracy in regards to the factor of number of targets. Likelihood ratio resulted in an accuracy of 79.6% with the majority of correct predictions occurring immediately at the start of movement. The task-space performance results reveal that despite the relative direction of both hands, reaching two targets results in lower performance than a single target, and symmetry alone does not contribute to performance disparity. Also, dimensionless integrated absolute jerk (DIAJ) is an indicator of superior performance for this particular task. Furthermore, these results align with current bimanual coordination theory by showing manual performance disparities are a consequence of task constraints and conceptualization.
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Affiliation(s)
- Jacob R Boehm
- Human-Enabled Robotic Technology Lab, the Department of Mechanical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Nicholas P Fey
- Department of Biomedical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Ann Majewicz
- Human-Enabled Robotic Technology Lab, the Department of Mechanical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
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Maselli A, Dhawan A, Russo M, Cesqui B, Lacquaniti F, d'Avella A. A whole body characterization of individual strategies, gender differences, and common styles in overarm throwing. J Neurophysiol 2019; 122:2486-2503. [PMID: 31577474 DOI: 10.1152/jn.00011.2019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Overarm throwing is a fundamental human skill. Since paleolithic hunter-gatherer societies, the ability of throwing played a key role in brain and body co-evolution. For decades, throwing skill acquisition has been the subject of developmental and gender studies. However, due to its complex multijoint nature, whole body throwing has found little space in quantitative studies of motor behavior. In this study we examined how overarm throwing varies within and between individuals in a sample of untrained adults. To quantitatively compare whole body kinematics across throwing actions, we introduced a new combination of spatiotemporal principal component, linear discrimination, and clustering analyses. We found that the identity and gender of a thrower can be robustly inferred by the kinematics of a single throw, reflecting the characteristic features in individual throwing strategies and providing a quantitative ground for the well-known differences between males and females in throwing behavior. We also identified four main classes of throwing strategies, stable within individuals and resembling the main stages of throwing proficiency acquisition during motor development. These results support earlier proposals linking interindividual and gender differences in throwing, with skill acquisition interrupted at different stages of the typical developmental trajectory of throwing motor behavior.NEW & NOTEWORTHY Unconstrained throwing, because of its complexity, received little attention in quantitative motor control studies. By introducing a new approach to analyze whole body kinematics, we quantitatively characterized gender effects, interindividual differences, and common patterns in nontrained throwers. The four throwing styles identified across individuals resemble different stages in the acquisition of throwing skills during development. These results advance our understanding of complex motor skills, bridging the gap between motor control, motor development, and sport science.
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Affiliation(s)
- Antonella Maselli
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Aishwar Dhawan
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Marta Russo
- Department of Systems Medicine and Center of Space Biomedicine, University of Rome Tor Vergata, Rome, Italy
| | - Benedetta Cesqui
- Department of Systems Medicine and Center of Space Biomedicine, University of Rome Tor Vergata, Rome, Italy
| | - Francesco Lacquaniti
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy.,Department of Systems Medicine and Center of Space Biomedicine, University of Rome Tor Vergata, Rome, Italy
| | - Andrea d'Avella
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy.,Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
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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: 2.2] [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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Watching novice action degrades expert motor performance: causation between action production and outcome prediction of observed actions by humans. Sci Rep 2014; 4:6989. [PMID: 25384755 PMCID: PMC4227030 DOI: 10.1038/srep06989] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 10/16/2014] [Indexed: 11/30/2022] Open
Abstract
Our social skills are critically determined by our ability to understand and appropriately respond to actions performed by others. However despite its obvious importance, the mechanisms enabling action understanding in humans have remained largely unclear. A popular but controversial belief is that parts of the motor system contribute to our ability to understand observed actions. Here, using a novel behavioral paradigm, we investigated this belief by examining a causal relation between action production, and a component of action understanding - outcome prediction, the ability of a person to predict the outcome of observed actions. We asked dart experts to watch novice dart throwers and predict the outcome of their throws. We modulated the feedbacks provided to them, caused a specific improvement in the expert's ability to predict watched actions while controlling the other experimental factors, and exhibited that a change (improvement) in their outcome prediction ability results in a progressive and proportional deterioration in the expert's own darts performance. This causal relationship supports involvement of the motor system in outcome prediction by humans of actions observed in others.
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Ganesh G, Osu R, Naito E. Feeling the force: returning haptic signals influence effort inference during motor coordination. Sci Rep 2014; 3:2648. [PMID: 24026052 PMCID: PMC3770969 DOI: 10.1038/srep02648] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Accepted: 08/28/2013] [Indexed: 01/05/2023] Open
Abstract
Our brain is known to automatically optimize effort expenditure during motor coordination, such that for example, during bimanual braking of a bicycle, a well-oiled brake will automatically be used more than a corroded, heavy brake. But how does our brain infer the effort expenditure? All previous motor coordination models have believed that the effort in a task is known precisely to our brain, solely from the motor commands it generates. Here we show that this belief is incorrect. Through experiments and simulation we exhibit that in addition to the motor commands, the returning haptic signals play a crucial role in the inference of the effort during a force sharing task. Our results thus elucidate a previously unknown sensory-motor association that has major ramifications for our understanding of motor coordination and provides new insights into how sensory modifications due to ergonomics, stroke and disease can affect motor coordination in humans.
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Affiliation(s)
- G Ganesh
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, 1-4 Yamadaoka, Osaka University Campus, Suita, Japan 5650871
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Hasson CJ, Shen T, Sternad D. Energy margins in dynamic object manipulation. J Neurophysiol 2012; 108:1349-65. [PMID: 22592302 PMCID: PMC3544966 DOI: 10.1152/jn.00019.2012] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2012] [Accepted: 05/09/2012] [Indexed: 01/08/2023] Open
Abstract
Many tasks require humans to manipulate dynamically complex objects and maintain appropriate safety margins, such as placing a cup of coffee on a coaster without spilling. This study examined how humans learn such safety margins and how they are shaped by task constraints and changing variability with improved skill. Eighteen subjects used a manipulandum to transport a shallow virtual cup containing a ball to a target without losing the ball. Half were to complete the cup transit in a comfortable target time of 2 s (a redundant task with infinitely many equivalent solutions), and the other half in minimum time (a nonredundant task with one explicit cost to optimize). The safety margin was defined as the ball energy relative to escape, i.e., as an energy margin. The first hypothesis, that subjects converge to a single strategy in the minimum-time task but choose different strategies in the less constrained target-time task, was not supported. Both groups developed individualized strategies with practice. The second hypothesis, that subjects decrease safety margins in the minimum-time task but increase them in the target-time task, was supported. The third hypothesis, that in both tasks subjects modulate energy margins according to their execution variability, was partially supported. In the target-time group, changes in energy margins correlated positively with changes in execution variability; in the minimum-time group, such a relation was observed only at the end of practice, not across practice. These results show that when learning a redundant object manipulation task, most subjects increase their safety margins and shape their movement strategies in accordance with their changing variability.
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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: 39] [Impact Index Per Article: 3.0] [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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