Dissociating Variability and Effort as Determinants of Coordination

O'Sullivan, Ian; Burdet, Etienne; Diedrichsen, Jörn

doi:10.1371/journal.pcbi.1000345

Cited by 107 publications

(125 citation statements)

References 21 publications

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“…For the task constraints, the results revealed that the system adopted the task efficiency strategy (minimum muscle force output) rather than the strategy of minimizing task variance. The results are consistent with previous findings showing preference on task efficiency over other minimization criteria, such as movement time (Lee et al 2007;Nelson 1983) and task variability (O'Sullivan et al 2009). On the task efficiency constraint, the results revealed that coefficient-dependent model was the best fitted model in that the optimal force sharing pattern predicted by the task constraint scales with the coefficient ratios imposed on the finger forces.…”

Section: Experimental Results and Model Predictionssupporting

confidence: 92%

Modeling constraints to redundancy in bimanual force coordination

Newell

2011

Journal of Neurophysiology

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Hu X, Newell KM. Modeling constraints to redundancy in bimanual force coordination. J Neurophysiol 105: 2169 -2180, 2011. First published February 23, 2011 doi:10.1152/jn.01086.2010.-This study investigated the interactive influence of organismic, environmental, and task constraints on the organization of redundant force coordination patterns and the hypothesis that each of the three categories of constraints is weighted based on their relative influence on coordination patterns and the realization of the task goal. In the bimanual isometric force experiment, the task constraint was manipulated via different coefficients imposed on the finger forces such that the weighted sum of the finger forces matched the target force. We examined three models of task constraints based on the criteria of task variance (minimum variance model) and efficiency of muscle force output (coefficient-independent and coefficient-dependent efficiency models). The environmental constraint was quantified by the perceived performance error, and the organismic constraint was quantified by the bilateral coupling effect (i.e., symmetric force production) between hands. The satisficing approach was used in the models to quantify the constraint weightings that reflect the interactive influence of different categories of constraints on force coordination. The findings showed that the coefficient-dependent efficiency model best predicted the redundant force coordination patterns across trials. However, the within-trial variability structure revealed that there was not a consistent coordination strategy in the online control of the individual trial. The experimental findings and model tests show that the force coordination patterns are adapted based on the principle of minimizing muscle force output that is coefficient dependent rather than on the principle of minimizing signal-dependent variance. Overall, the results support the proposition that redundant force coordination patterns are organized by the interactive influence of different categories of constraints. satisficing model; variability; redundancy; bilateral coupling; isometric force

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Section: Experimental Results and Model Predictionssupporting

confidence: 92%

Modeling constraints to redundancy in bimanual force coordination

Newell

2011

Journal of Neurophysiology

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“…Variability in force production ("motor noise") has also been hypothesized as a cost to optimize (Harris and Wolpert, 1998;Haruno and Wolpert, 2005;O'Sullivan et al, 2009;Diedrichsen et al, 2010) but it covaries with muscle activity under natural conditions (Jones et al, 2002). To dissociate these factors, we manipulated the virtual biomechanics to increase the signaldependent noise associated with muscle activation in one of the five muscles (i.e., an extensor muscle, n ϭ 3; or a flexor muscle, n ϭ 3).…”

Section: Adaptation To Novel Virtual Biomechanics With Motor Noise-exmentioning

confidence: 99%

“…Resolution of this redundancy is a fundamental problem of biological motor control (Bernstein, 1967) that must also be addressed for the design of robotic and prosthetic systems that mimic or restore human movement (Loeb and Davoodi, 2005). The computational framework of optimal control theory has gained influence as a general theory of motor coordination because it can specify uniquely how behavioral goals should be achieved by minimizing costs such as the effort or variability of movement (Pedotti et al, 1978;Crowninshield and Brand, 1981;Davy and Audu, 1987;Loeb et al, 1990;Harris and Wolpert, 1998;Todorov and Jordan, 2002;Scott, 2004;Todorov, 2004;O'Sullivan et al, 2009;Diedrichsen et al, 2010). Although some aspects of natural motor behavior, such as typical patterns of muscle activity, are consistent with the output of optimal control models (Fagg et al, 2002;Haruno and Wolpert, 2005;Diedrichsen et al, 2010), how the nervous system generates this behavior is unknown.…”

Section: Introductionmentioning

confidence: 99%

Muscle Coordination Is Habitual Rather than Optimal

Rugy¹,

Loeb²,

Carroll³

2012

J. Neurosci.

201

163

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When sharing load among multiple muscles, humans appear to select an optimal pattern of activation that minimizes costs such as the effort or variability of movement. How the nervous system achieves this behavior, however, is unknown. Here we show that contrary to predictions from optimal control theory, habitual muscle activation patterns are surprisingly robust to changes in limb biomechanics. We first developed a method to simulate joint forces in real time from electromyographic recordings of the wrist muscles. When the model was altered to simulate the effects of paralyzing a muscle, the subjects simply increased the recruitment of all muscles to accomplish the task, rather than recruiting only the useful muscles. When the model was altered to make the force output of one muscle unusually noisy, the subjects again persisted in recruiting all muscles rather than eliminating the noisy one. Such habitual coordination patterns were also unaffected by real modifications of biomechanics produced by selectively damaging a muscle without affecting sensory feedback. Subjects naturally use different patterns of muscle contraction to produce the same forces in different pronation-supination postures, but when the simulation was based on a posture different from the actual posture, the recruitment patterns tended to agree with the actual rather than the simulated posture. The results appear inconsistent with computation of motor programs by an optimal controller in the brain. Rather, the brain may learn and recall command programs that result in muscle coordination patterns generated by lower sensorimotor circuitry that are functionally "good-enough."

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“…This also contributes to movement variability. Most optimization approaches predict average behavior (Collins 1995;Engelbrecht 2001;Harris and Wolpert 1998;Scott 2004) assuming that the nervous system minimizes variability as a limiting constraint (Harris and Wolpert 1998;Körding and Wolpert 2004;O'Sullivan et al 2009). However, recent work demonstrates that humans instead often exploit redundancy to regulate variability in ways that help maximize task performance (Cusumano and Cesari 2006;Latash et al 2002;Todorov and Jordan 2002) while minimizing control effort.…”

mentioning

confidence: 99%

Trial-to-trial dynamics and learning in a generalized, redundant reaching task

Dingwell

Smallwood

Cusumano

2013

Journal of Neurophysiology

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Dingwell JB, Smallwood RF, Cusumano JP. Trial-to-trial dynamics and learning in a generalized, redundant reaching task. J Neurophysiol 109: 225-237, 2013. First published October 10, 2012 doi:10.1152/jn.00951.2011.-If humans exploit task redundancies as a general strategy, they should do so even if the redundancy is decoupled from the physical implementation of the task itself. Here, we derived a family of goal functions that explicitly defined infinite possible redundancies between distance (D) and time (T) for unidirectional reaching. All [T, D] combinations satisfying any specific goal function defined a goal-equivalent manifold (GEM). We tested how humans learned two such functions, D/T ϭ c (constant speed) and D·T ϭ c, that were very different but could both be achieved by neurophysiologically and biomechanically similar reaching movements. Subjects were never explicitly shown either relationship, but only instructed to minimize their errors. Subjects exhibited significant learning and consolidation of learning for both tasks. Initial error magnitudes were higher, but learning rates were faster, for the D·T task than for the D/T task. Learning the D/T task first facilitated subsequent learning of the D·T task. Conversely, learning the D·T task first interfered with subsequent learning of the D/T task. Analyses of trial-to-trial dynamics demonstrated that subjects actively corrected deviations perpendicular to each GEM faster than deviations along each GEM to the same degree for both tasks, despite exhibiting significantly greater variance ratios for the D/T task. Variance measures alone failed to capture critical features of trial-to-trial control. Humans actively exploited these abstract task redundancies, even though they did not have to. They did not use readily available alternative strategies that could have achieved the same performance.

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Dissociating Variability and Effort as Determinants of Coordination

Cited by 107 publications

References 21 publications

Modeling constraints to redundancy in bimanual force coordination

Modeling constraints to redundancy in bimanual force coordination

Muscle Coordination Is Habitual Rather than Optimal

Trial-to-trial dynamics and learning in a generalized, redundant reaching task

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