Introduction

Speed accuracy trade-off, the inverse relationship between movement speed and task accuracy, is a ubiquitous feature of skilled motor performance in both human and non-human animals (Fitts 1954; Fitts and Peterson 1964; Yarrow et al. 2009; Heitz and Schall 2012; Heitz 2014). Depending on the demands of the task, we tend to move at a slower speed to gain greater accuracy or sacrifice accuracy to complete the task sooner. For example, when threading a needle, we tend to move very slowly to achieve the fine accuracy prescribed by the eye of the needle, while the main goal of hammering a nail is to achieve maximum momentum at adequate accuracy. As we become more skilled at each task, we can increase speed for the required accuracy. In fact, it has been suggested that motor skill acquisition is, in essence, the modification of this speed-accuracy relationship, in which we learn to perform a task with the same accuracy (i.e. hitting a nail) at a higher velocity, or we maintain velocity while improving accuracy (i.e. more often getting the thread through the eye of the needle) (Shmuelof et al. 2012).

Many previous studies of speed-accuracy trade-off in humans have focused on dominant arm unimanual performance in both simple tasks, such as target reaching (Plamondon and Alimi 1997), and complex tasks, such as overarm throwing (Sachlikidis and Salter 2007). However, a prominent feature of skilled motor performance is that the two arms exhibit different proficiencies that seem to reflect how we distribute components of a functional action to each arm. For example, when performing bimanual tasks, the non-dominant hand tends to stabilize the needle or the nail, while the dominant hand moves the thread or the hammer. This distribution of function is predicted by our bihemispheric model of motor lateralization, termed Dynamic Dominance. This model, based on empirical findings in typical adults and patients with unilateral brain damage (Bagesteiro and Sainburg 2002; Sainburg and Schaefer 2004; Schaefer et al. 2007, 2009; Haaland et al. 2009; Yadav and Sainburg 2014b), attributes predictive control mechanisms that can specify energetically efficient and spatially precise trajectories under consistent task conditions to the hemisphere contralateral to the dominant arm. Conversely, the non-dominant hemisphere appears specialized for achieving robust and stable positions under inconsistent and unpredictable environmental conditions, such as when the impending movement is not well-practiced or when unpredictable perturbations arise from environmental mechanics (Sainburg and Kalakanis 2000; Bagesteiro and Sainburg 2006; Wang and Sainburg 2007; Yadav and Sainburg 2014b). We have previously operationalized this model through computational simulations of these processes, combining them serially in different proportions to differentially simulate dominant and non-dominant arm movements (Yadav and Sainburg 2011,2014a,b). Our results showed that empirical movements of the dominant arm were best-fit by our model when predictive control mechanisms were employed throughout most of the movement. In this model, predictive control mechanisms were based on an optimization model that minimized mechanical work, spatial error, and speed. In contrast, the simulated non-dominant arm trajectories were best fit to empirical data when an impedance controller played a greater role in control (Yadav and Sainburg 2011, 2014a). While processes that predict impending limb dynamics should benefit substantially from experience, impedance control mechanisms might show less benefit from the experience. This is because control of trajectory through prediction of limb and task dynamics requires accurate estimates of limb and environmental conditions within the context of the task, and variations in these predictions can produce substantial variations in the resulting trajectories. On the other hand, an impedance controller that specifies steady state or transient stiffness and viscosity values should be robust to a large range of unexpected environmental and limb mechanical conditions.

You are viewing: Why Is My Non Dominant Arm Stronger

Read more : Why Put Rice In Braids

In contrast to complementary dominance, the more prominent Global dominance models purport that the dominant hemisphere/hand is specialized for all features of motor planning and control, and thus predicts dominant arm superiority for all aspects of behavior (Liepmann 1908; Annett et al. 1979; Volkmann et al. 1998; Ziemann and Hallett 2001). Thus, the most striking prediction of our complimentary dominance model is that the non-dominant arm should demonstrate superior performance, or “dominance,” when task conditions favor non-dominant modes of control. The predictive nature of the dominant controller is dependent on accurate information about mechanical conditions, especially with regard to initial conditions, prior to movement. In addition, such control should be susceptible to inaccurate estimations or variations in mechanical conditions and/or perturbations that can arise from unfamiliar conditions. In contrast, an impedance controller should be robust to changes in sensory information and task experience.

We now test these models of lateralization within the context of motor skill, assessed through speed-accuracy trade-offs. While the global dominance hypothesis predicts the superior skill of the dominant arm under all conditions, our model predicts the better performance of the non-dominant arm when sensory information and task experience are lowest. Thus, we predict that the non-dominant arm should demonstrate a more advantageous speed-accuracy relationship when the controller is naive to the task and sensory information is limited. We also predict that the dominant arm should improve speed-accuracy relations with experience to a greater degree than the non-dominant arm, because estimates of limb and task dynamics should become more accurate for the dominant arm controller.

Source: https://t-tees.com
Category: WHY