Robotics and neuroscience: A rhythmic interaction

Ronsse, Renaud; Lefèvre, Philippe; Sepulchre, Rodolphe

doi:10.1016/j.neunet.2008.03.005

Cited by 12 publications

(12 citation statements)

References 58 publications

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“…These conclusions were derived from a mathematical model of the task where stability analyses of the periodic behavior provided support that subjects exploited stability of the rhythmic task. Establishing dynamic stability in the ball-racket system makes extensive error corrections unnecessary and makes control less costly (Ronsse et al 2008a). This is consistent with the sub-jective observation that in skilled performance less attention is needed to maintain a stable pattern.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of a Hybrid Rhythmic-Discrete Task: The Bouncing Ball Revisited

Ronsse

Wei

Sternad

2010

Journal of Neurophysiology

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Ronsse R, Wei K, Sternad D. Optimal control of a hybrid rhythmic-discrete task: the bouncing ball revisited. J Neurophysiol 103: 2482-2493, 2010. First published February 3, 2010 doi:10.1152/jn.00600.2009. Rhythmically bouncing a ball with a racket is a hybrid task that combines continuous rhythmic actuation of the racket with the control of discrete impact events between racket and ball. This study presents experimental data and a two-layered modeling framework that explicitly addresses the hybrid nature of control: a first discrete layer calculates the state to reach at impact and the second continuous layer smoothly drives the racket to this desired state, based on optimality principles. The testbed for this hybrid model is task performance at a range of increasingly slower tempos. When slowing the rhythm of the bouncing actions, the continuous cycles become separated into a sequence of discrete movements interspersed by dwell times and directed to achieve the desired impact. Analyses of human performance show increasing variability of performance measures with slower tempi, associated with a change in racket trajectories from approximately sinusoidal to less symmetrical velocity profiles. Matching results of model simulations give support to a hybrid control model based on optimality, and therefore suggest that optimality principles are applicable to the sensorimotor control of complex movements such as ball bouncing.

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Section: Introductionmentioning

confidence: 99%

Optimal Control of a Hybrid Rhythmic-Discrete Task: The Bouncing Ball Revisited

Ronsse

Wei

Sternad

2010

Journal of Neurophysiology

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“…Moreover, recent investigations revealed that the interaction between rhythmic and discrete components is necessary to finely control an external object (a bouncing ball), either to correct for unexpected perturbations (Wei, Dijkstra, & Sternad, 2007) or to adapt the control strategy to various movement frequencies or visual feedback conditions (Ronsse, Lefèvre, & Sepulchre, 2008; Ronsse, Thonnard, Lefèvre, & Sepulchre, 2008). Taken together, these findings suggest that even if they are largely nonoverlapping primitives, discrete and rhythmic movements are combined in a nonarbitrary manner by the central nervous system.…”

Section: Introductionmentioning

confidence: 99%

A Computational Model for Rhythmic and Discrete Movements in Uni- and Bimanual Coordination

2009

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Current research on discrete and rhythmic movements differs in both experimental procedures and theory, despite the ubiquitous overlap between discrete and rhythmic components in everyday behaviors. Models of rhythmic movements usually use oscillatory systems mimicking central pattern generators (CPGs). In contrast, models of discrete movements often employ optimization principles, thereby reflecting the higher-level cortical resources involved in the generation of such movements. This letter proposes a unified model for the generation of both rhythmic and discrete movements. We show that a physiologically motivated model of a CPG can not only generate simple rhythmic movements with only a small set of parameters, but can also produce discrete movements if the CPG is fed with an exponentially decaying phasic input. We further show that a particular coupling between two of these units can reproduce main findings on in-phase and antiphase stability. Finally, we propose an integrated model of combined rhythmic and discrete movements for the two hands. These movement classes are sequentially addressed in this letter with increasing model complexity. The model variations are discussed in relation to the degree of recruitment of the higher-level cortical resources, necessary for such movements.

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“…A first study applied perturbations and showed that return to steady state is faster than relying on self-correction only (Wei et al 2007, 2008). But also at steady state, theoretical analyses provided a way to tease apart indicators of closed-loop control from dynamically stable performance (Wei et al 2007; Ronsse et al 2008a, b; Wei et al 2008). Interestingly, the relative contribution of active and passive contributions changed with experience: more experienced actors relied more on dynamical stability but, at the same time, also showed more active error corrections.…”

Section: Introductionmentioning

confidence: 99%

Open-loop, closed-loop and compensatory control: performance improvement under pressure in a rhythmic task

2009

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According to explicit monitoring theories, the phenomenon of choking under pressure is due to actors focusing their attention on the execution of the skill. This step-by-step perceptually guided control may then interfere with automatic execution. In order to examine the changes in control at the sensorimotor level, we examined the rhythmic task of ball bouncing which affords detailed quantification of indicators of control based on previous research. The hypothesis was that under psychological pressure perceptually guided control should lead to decreased performance due to over-emphasis on closed-loop control and decreased compensatory control. In two experiments of different difficulty psychological stress was induced via setting up a fake competition. Results showed that, contrary to the hypothesis, performance accuracy and consistency improved together with an increase in compensatory control. Indicators for open- and closed-loop processes did not change. Only under more challenging conditions in Experiment 2, enhanced performance under pressure was accompanied by more active, closed-loop and less passive control. The results are discussed in light of task demands and the continuous rhythmic nature of the task: in more challenging tasks, control appears to be more prone to disturbance due to psychological stress. The different control demands in continuous rhythmic tasks may be less prone to interference due to psychological stress than in discrete tasks.

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Robotics and neuroscience: A rhythmic interaction

Cited by 12 publications

References 58 publications

Optimal Control of a Hybrid Rhythmic-Discrete Task: The Bouncing Ball Revisited

Optimal Control of a Hybrid Rhythmic-Discrete Task: The Bouncing Ball Revisited

A Computational Model for Rhythmic and Discrete Movements in Uni- and Bimanual Coordination

Open-loop, closed-loop and compensatory control: performance improvement under pressure in a rhythmic task

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