Inter-Trial Dynamics of Repeated Skilled Movements

John, Jaison Jacob; Cusumano, Joseph P.

doi:10.1115/detc2007-35380

Cited by 8 publications

(9 citation statements)

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“…Our results demonstrate that applying the appropriate control (i.e., distal versus proximal) in the presence of noise could lead to lower energy consumption. Thus, our findings support the idea that people adopt control strategies based, at least partly, on the "minimum intervention principle" [17,19,20,24,25]. That is, they expend far less effort to minimize fluctuations at the proximal joints because those fluctuations have only minimal effects on the final task performance.…”

Section: Discussionsupporting

confidence: 82%

“…These might include the biomechanical structure of the system, the signal-dependent nature of neuromuscular noise, or a desire by the nervous system to satisfy other task goals such as minimizing fatigue or exploring alternative control strategies. Theoretical approaches that tie these ideas to stochastic optimal control theory [17,19,20] provide a more concrete computational framework for properly interpreting the specific control implications of such experimental observations [16,24,25]. These concepts have not yet been applied to specifically address the question of how the nervous system might partition control across proximal versus distal joints in specific goal-directed tasks.…”

Section: Introductionmentioning

confidence: 97%

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Proximal Versus Distal Control of Two-Joint Planar Reaching Movements in the Presence of Neuromuscular Noise

Nguyen

Dingwell

2012

Journal of Biomechanical Engineering

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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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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 97%

Proximal Versus Distal Control of Two-Joint Planar Reaching Movements in the Presence of Neuromuscular Noise

Nguyen

Dingwell

2012

Journal of Biomechanical Engineering

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“…In redundant, high-dimensional, and noisy tasks it is not enough to characterize the mean and the covariance of the action distribution to fully capture the relationship between action variability and performance. In agreement with earlier computational approaches addressing variability in multivariate actions [23,15,21,12], our method highlights the key role of the geometry of the mapping between actions and outcomes or scores to assess how action variability affects performance. Differently from first-order methods such as UCM, GEM, or the more recent approach in [31], which characterize the local geometry with a linear approximation (expressed through the Jacobian matrix or the gradient vector), our method relies on a secondorder approximation (based on the Hessian matrix).…”

Section: Discussionsupporting

confidence: 76%

A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability

Tommasino

Maselli

Campolo

et al. 2019

Preprint

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In complex real-life motor skills such as unconstrained throwing, performance depends on how accurate is on average the outcome of noisy, high-dimensional, and redundant actions. What characteristics of the action distribution relate to performance and how different individuals select specific action distributions are key questions in motor control. Previous computational approaches have highlighted that variability along the directions of first order derivatives of the action-to-outcome mapping affects performance the most, that different mean actions may be associated to regions of the actions space with different sensitivity to noise, and that action covariation in addition to noise magnitude matters. However, a method to relate individual high-dimensional action distribution and performance is still missing. Here we introduce a decomposition of performance into a small set of indicators that compactly and directly characterize the key performance-related features of the distribution of high-dimensional redundant actions. Central to the method is the observation that, if performance is quantified as a mean score, the Hessian (second order derivatives) of the action-to-score function determines the noise sensitivity of the action distribution. We can then approximate the mean score as the sum of the score of the mean action and a tolerance-variability index which depends on both Hessian and action covariance. Such index can be expressed as the product of three terms capturing overall noise magnitude, overall noise sensitivity, and alignment of the most variable and most noise sensitive directions. We apply this method to the analysis of unconstrained throwing actions by non-expert participants and show that, consistently across four different throwing targets, each participant shows a specific selection of mean action score and tolerance-variability index as well as specific selection of noise magnitude and alignment indicators. Thus, participants with 1 different strategies may display the same performance because they can trade off suboptimal mean action for better tolerance-variability and higher action variability for better alignment with more tolerant directions in action space. Author summaryWhy do people differ in their performance of complex motor skills? In many real-life motor tasks achieving a goal requires selecting an appropriate high-dimensional action out of infinitely many goal-equivalent actions. Because of sensorimotor noise, we are unable to execute the exact same action twice and our performance depends on how accurate we are on average. Thus, to understand why people perform differently we need to characterize how their action distribution relates to their mean task score. While better performance is often associated to smaller variability around a more accurate mean action, performance also depends on the relationship between the directions of highest variability in action space and the directions in which action variability affects the most the outcome of the action. However, characteriz...

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“…Additionally, statistical measures of variance do not capture the temporal structure of observed intertrial fluctuations and so cannot quantify how errors evolve from trial to trial. One solution is to develop computational models that directly predict how variance becomes structured as a result of specific control policies John and Cusumano 2007;Todorov and Jordan 2002). Experimentally, additional insights can be gained by supplementing variance analyses with more detailed temporal analyses that directly quantify how fluctuations on any one trial are subsequently corrected on the next .…”

mentioning

confidence: 99%

“…The MIP ties the idea of task geometry to stochastic optimal control theory to construct computational models that predict how movements are regulated in redundant motor systems (Todorov and Jordan 2002;Valero-Cuevas et al 2009). The related goal-equivalent manifold (GEM) approach provides an analysis that maps the observed dynamics of task performance, at the level of the body, onto an independently defined goal space (Cusumano and Cesari 2006;John and Cusumano 2007). Thus, in the GEM approach, task manifolds are defined in a way more similar to the TNC approach than to the UCM approach.…”

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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Inter-Trial Dynamics of Repeated Skilled Movements

Cited by 8 publications

References 0 publications

Proximal Versus Distal Control of Two-Joint Planar Reaching Movements in the Presence of Neuromuscular Noise

Proximal Versus Distal Control of Two-Joint Planar Reaching Movements in the Presence of Neuromuscular Noise

A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability

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

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