Biology subjects

Berret, B.

Publications and source records attributed to Berret, B..

15 recordsLinked to original sources

bioRxiv2025

Evidence for separate processes underlying movement and decision vigor in a reward-oriented task

The vigor of movement and decision-making is fundamental to many reward-oriented behaviors. While some theories propose that movement and decision are jointly invigorated to maximize a global utility (e.g., mixing reward, effort, and time), alternative perspectives suggest that the brain can separately invigorate them when advantageous. We tested these competing hypotheses using a foraging-like task that allowed experimentally assessing the vigor of movement (reaching to reward location) and that of decision-making (harvesting reward). By using a block-wise design, we measured the effects of independent manipulations of time and effort on reach and harvest durations. Decoupled effects were found on reach and harvest durations, with no inter-individual consistency between them. A model allowing separate, yet inter-dependent, optimizations of movement and decision vigor based on distinct time costs sensitive to recent temporal history predicted these results more accurately than a global utility model. These findings indicate that movement and decision vigor may flexibly depend on whether shared versus distinct time costs underlie behavior; co-regulation would arise when the vigor of movement and decision is governed by a shared time cost but decoupling may emerge when relevant for the task. Significance statementCoordinating movement and decision-making is fundamental to many reward-oriented behaviors. Current theories suggest that the brain may invigorate behavior by co-regulating movement and decision, but some experimental findings rather point to a decoupling. To shed new light on this debate, we developed a foraging-like task promoting independent and comparable manipulation of time and effort. We show that movement and decision are mostly decoupled in this task, invigorated by distinct time-cost signals. Previously experienced delays tend to amplify these time costs, thereby exerting a strong influence on behavior and indirectly linking movement and decision. Co-regulation or decoupling may hinge on whether movement and decision vigor relies on shared or distinct time-cost signals.

neuroscience↗

bioRxiv2025

Less is more: uncompensated gravity torques for intuitive EMG-based assistance with a robotic exoskeleton

Despite extensive investigation on the use of electromyographic (EMG) activity to control active exoskeletons over the past decade, designing intuitive assistive controllers that seamlessly integrate with natural human motor control have yet to be realized. While existing EMG-based controllers often achieve substantial reduction in muscle effort, they frequently incur increased cognitive and attentional load for the user, thereby compromising the overall efficacy of the assistance. Here we introduce a novel EMG-based assistive controller founded upon neuroscience principles, specifically the observation that humans naturally exploit gravity torque to facilitate movement control. Therefore, deviating from conventional compensation strategies, our approach purposely leaves a fraction of the predicted human gravity torque uncompensated so that users can still take advantage of gravity as they would without assistance. Through a load-carrying arm movement task, we show that enabling gravity exploitation improves traditional EMGbased assistance by achieving a significant reduction in muscle effort, while concurrently yielding superior kinematic performance (i.e., faster, smoother movements) and enhanced subjective user experience. These findings demonstrate that integrating principles from neural motor control into assistive controllers allows to implement a favorable tradeoff between muscle effort reduction and functional usability.

neuroscience↗

bioRxiv2025

How individual vigor shapes human-human physical interaction

The speed of voluntary movements varies systematically, with some individuals moving consistently faster than others across different actions. These variations, conceptualized as vigor, reflect a time-effort-accuracy tradeoff in motor planning. How do two mechanically coupled partners with different individual vigors collaborate, e.g. to move a table together? Here, we show that such dyads coordinate goal-directed movements with minimal interaction force, exhibiting a dyadic vigor with similar characteristics as individual vigor. The emerging dyadic motor plan is strongly influenced by the slower partner, whose vigor predicts dyadic vigor, with effects lasting beyond practice. Computational modeling with stochastic optimal control reveals the critical role of partners movement timings uncertainty and vigor in shaping coordination, allowing to predict dyadic movements from individual behavior across diverse conditions. These findings shed light on the mechanisms underlying human collaboration, and may be used in applications ranging from physical training and rehabilitation to collaborative robotics for manufacturing.

neuroscience↗

bioRxiv2025

Guiding humans towards ergonomic postures using an active exoskeleton

In the past decades, active exoskeletons have been dedicated to reducing human effort, in particular to assist workers in occupational environments. However, this approach does not promote the learning of more ergonomic postures by workers, which is critical for the long-term prevention of musculoskeletal disorders. Alternatively, we propose to use exoskeletons as biofeedback systems, generating task-relevant perturbations guiding users towards ergonomic postures. To test this approach, participants performed reach-to-hold movements towards a redundant target, allowing multiple final postures. We then introduced vibrations with posture-dependent intensity, generating a sensorimotor disturbance that canceled out either above or below each participants nominal preferred posture. Interestingly, participants adapted to minimize the vibrations, whether it increased or decreased the gravity efforts, and retained the novel posture when it induced lower effort. Finally, all participants significantly reduced effort post-exposure, demonstrating the feasibility and the benefits of using exoskeleton as biofeedback systems to improve posture.

bioengineering↗

bioRxiv2025

Model predictive game control for personalized and targeted interactive assistance

Contact robots are increasingly used to assist humans in physical training and manufacturing tasks. However, the effectiveness of current systems is limited as their control focuses on the system performance without explicitly considering the upcoming human users control. Here we present a differential game-based controller for contact robots that ensures optimal interaction with the human user by predicting their control during movement while considering their inherently finite planning horizon. Using this model-predictive game (MPG) controller, we systematically investigated human-robot co-adaptation in experiments, demonstrating that: (a) interaction with MPG remains stable for all participants while effectively reducing human effort; (b) the robot adapts to human behavior, identifying and characterizing individual motor control strategies that remain consistent over time and may be used as control biomarkers; (c) the human adapts to the robots behavior, and their interaction behavior can be modulated through an assistance meta-parameter. These findings indicate that humans can understand and adapt to a partners control strategy during movement, thereby exhibiting behavior consistent with game theory principles. Furthermore, the ability of the assistance meta-parameter to guide human users toward specific interaction behaviors can be used to develop versatile robot-assisted learning systems for physical training and rehabilitation.

bioengineering↗

bioRxiv2024

Co-Contraction Embodies Uncertainty: An Optimal Feedforward Strategy for Robust Motor Control

Despite our environment is often uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a singular relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success. Author summaryThis work presents a computational framework for predicting how humans modulate muscle co-contraction to cope with uncertainties of different origins. In our neuromusculoskeletal system, uncertainties have both internal (sensorimotor noise) and external (environmental randomness) origins. The present study focuses on the latter type of uncertainty, which had not been dealt with systematically previously despite its importance in everyday life. Therefore, we thoroughly investigated how random disturbances occurring with some probability in a motor task shape the feedforward control of mechanical impedance through muscle co-contraction. Here we provide theoretical, numerical and experimental evidence that the optimal level of co-contraction steeply increases with the uncertainty of our environment. These findings show that muscle co-contraction embodies uncertainty and optimally mitigates its consequences on task execution when feedback control is insufficient due to sensory noise and delays.

neuroscience↗

bioRxiv2024

Development of an Experimental Protocol to Study the Neural Control of Force and Impedance in Wrist Movements with Robotics and fMRI

Robotic exoskeletons have emerged as beneficial tools in the field of rehabilitation, yet their full potential is impeded by our limited knowledge of the neural control of movements during human-robot interaction. To personalize exoskeleton protocols and improve individuals motor recovery, we must advance our understanding of how the brain commands movements in physical interaction tasks. However, interpreting the neural function associated with these movements is complex due to the simultaneous expression of at least two control policies: force and impedance control. This hinders our ability to isolate these control mechanisms and pinpoint their neural origins. In this study, we evaluate the capacity of externally applied forces to decouple the expression of force and impedance in a wrist-pointing task, a necessary step in isolating their neural substrates via neuroimaging. We first conducted simulations using a neuromuscular model to examine how both force and impedance commands are updated when participants are asked to perform reaching movements in the presence of an externally applied force. Then, we recruited seven participants to perform a wrist-pointing task with the MR-SoftWrist, an MRI-compatible wrist robot. The task included four different force conditions - no force, positive constant force, negative constant force, and divergent force, each carefully selected to decouple expression of force and impedance control. Furthermore, we evaluated the efficacy of our proposed conditions for a neuroimaging experiment through simulations of neural activity. We show that these applied forces elicit distinct and predictable torque and stiffness expression, laying the groundwork for reliably identifying their associated neural activity in a future neuroimaging study.

bioengineering↗

bioRxiv2024

EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of in situ calibration

In the field of robotic exoskeleton control, it is critical to accurately predict the intention of the user. While surface electromyography (EMG) holds the potential for such precision, current limitations arise from the absence of robust EMG-to-torque model calibration procedures and a universally accepted model. This paper introduces a practical framework for calibrating and evaluating EMG-to-torque models, accompanied by a novel nonlinear model. The framework includes an in situ procedure that involves generating calibration trajectories and subsequently evaluating them using standardized criteria. A comprehensive assessment on a dataset with 17 participants, encompassing single-joint and multi-joint conditions, suggests that the novel model outperforms the others in terms of accuracy while conserving computational efficiency. This contribution introduces an efficient model and establishes a versatile framework for EMG-to-torque model calibration and evaluation, complemented by a dataset made available. This further lays the groundwork for future advancements in EMG-based exoskeleton control and human intent detection. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

neuroscience↗

bioRxiv2023

The value of time in the invigoration of human movements when interacting with a robotic exoskeleton

Time and effort are critical factors that are thought to be subjectively balanced during the planning of goal-directed actions, thereby setting the vigor of volitional movements. Theoretical models predicted that the value of time should then amount to relatively high levels of effort. However, the time-effort tradeoff has so far only been studied for a narrow range of efforts. Therefore, the extent to which humans can invest in a time-saving effort remains largely unknown. To address this issue, we used a robotic exoskeleton which significantly varied the energetic cost associated with a certain vigor during reaching movements. In this situation, minimizing the time-effort tradeoff would lead to high and low human efforts for upward and downward movements respectively. Consistent with this prediction, results showed that all participants expended substantial amounts of energy to pull on the exoskeleton during upward movements and remained essentially inactive by harnessing the work of gravity to push on the exoskeleton during downward movements, while saving time in both cases. These findings show that a common tradeoff between time and effort can determine the vigor of reaching movements for a wide range of efforts, with time cost playing a pivotal role.

neuroscience↗

bioRxiv2022

Fast reoptimization of human motor patterns in non-Earth gravity fields locally induced by a robotic exoskeleton

Gravity is a ubiquitous component of our environment that we learnt to optimally integrate in movement control. Yet, altered gravity conditions arise in numerous applications from space exploration to rehabilitation, thereby pressing the sensorimotor system to adapt. Here, we used a robotic exoskeleton to test whether humans can quickly reoptimize their motor patterns in arbitrary gravity fields, ranging from 1g to -1g and passing through Mars- and Moon-like gravities. By comparing the motor patterns of actual arm movements with those predicted by an optimal control model, we show that our participants (N = 61) quickly and optimally adapted their motor patterns to each local gravity condition. These findings show that arbitrary gravity-like fields can be efficiently apprehended by humans, thus opening new perspectives in arm weight support training in manipulation tasks, whether it be for patients or astronauts.

neuroscience↗

bioRxiv2022

Optimization of modularity during development to simplify walking control across strides

Walking in adults seems to rely on a small number of modules allowing to reduce the number of degrees of freedom effectively regulated by the central nervous system (CNS). However, the extent to which modularity evolves during development remains unknown, particularly regarding the ability to generate several strides in an optimized manner. Here we compared the modular organization of toddlers and adults during several strides of walking. We recorded the electromyographic activity of 10 bilateral (lower limbs) muscles in adults (n=12) and toddlers (n=12) during 8 gait cycles, and used non-negative matrix factorization to model the underlying modular command. While the muscular activity of all strides could be factorized into a consistent low-dimensional modular organization in adults, significantly more computational modules were needed in toddlers to account for their greater stride-by-stride variability. Activations of these modules varied more across strides and was less parsimonious in toddlers than in adults, even when balances constrained were diminished. These findings suggest that the modular control of locomotion of adults evolves as the organism develops and practices. They also suggest that new walker can flexibly activate a higher number of modules and benefit from a higher space of possible action, which could serve motor exploration.

neuroscience↗

bioRxiv2022

Generating variability from motor primitives during infant locomotor development

Motor variability is a fundamental feature of developing systems allowing motor exploration and learning. In human infants, leg movements involve a small number of basic coordination patterns called locomotor primitives, but whether and when motor variability could emerge from these primitives remains unknown. Here we longitudinally followed 10 neonates ([~]4 days old) until walking onset ([~]14 months old) and recorded the activity of their leg muscles during locomotor or rhythmic movements. Using unsupervised machine learning, we show that the structure of trial-to-trial variability changes during early development. In the neonatal period, infants own a minimal number of motor primitives but generate a maximal motor variability across trials thanks to variable activations of these primitives. A few months later, toddlers generate significantly less variability despite the existence of more primitives, due to more regularity within their activation. These results suggest that human neonates initiate motor exploration as soon as birth by variably activating a few basic locomotor primitives that later fraction and become more consistently activated by the motor system.

neuroscience↗

bioRxiv2020

The dorsal striatum sets the sensitivity to effort

The dorsal striatum (dS) has been implicated in storing and retrieving procedural memories and controlling movement kinematics (e.g., speed). Since procedural memories are expressed through movements, the exact nature of the dS function has proven difficult to delineate. Here we challenged rats in complementary tasks designed to alleviate this performance confound. Surprisingly, dS lesions spared task-specific procedural memories but altered the kinematics of their expression in motor routines. Further behavioral analyses combined with simulations in the optimal control framework indicated that these alterations reflected an increased sensitivity to effort with preserved reward-seeking and ability to modulate movement speed. By setting the sensitivity to effort, the dS contributes to the optimization of the energy invested into voluntary movements. Such an elementary function of the dS might explain its implication in both procedural decisions and the control of movement speed.

neuroscience↗

bioRxiv2019

Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction

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 explain a common phenomenon known as muscle co-contraction. Co-contraction of agonist and antagonist muscles contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint stiffness) 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 the 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. Optimal feedback (closedloop) 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 limbs mechanical impedance. The 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.\n\nAuthor summaryThis study presents a novel computational theory to explain the planning of force and impedance (e.g. stiffness) 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 noise. A major outcome of this mathematical framework is the explanation of a long-known phenomenon called muscle co-contraction (i.e. the concurrent contraction of opposing muscles). 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 feedback 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.

neuroscience↗

bioRxiv2017

Spatiotemporal organization of whole-body muscle activity during upright reaching movements in various directions: modularity or not modularity?

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements.

neuroscience↗