Search

Bimanual tasks involve the coordination of both arms, which often offers redundancy in the ways a task can be completed. The distribution of control across limbs is often considered from the perspective of handedness. In this context, although there are differences across dominant and nondominant arms during reaching control (Sainburg 2002), previous studies have shown that the brain tends to favor the dominant arm when performing bimanual tasks (Salimpour and Shadmehr 2014). However, biomechanical factors known to influence planning and control in unimanual tasks may also generate limb asymmetries in force generation, but their influence on bimanual control has remained unexplored. We investigated this issue in a series of experiments in which participants were instructed to generate a 20-N force with both arms, with or without perturbation of the target force during the trial. We modeled the task in the framework of optimal feedback control of a two-link model with six human-like muscles groups. The biomechanical model predicted a differential contribution of each arm dependent on the orientation of the target force and joint configuration that was quantitatively matched by the participants' behavior, regardless of handedness. Responses to visual perturbations were strongly influenced by the perturbation direction, such that online corrections also reflected an optimal use of limb biomechanics. These results show that the nervous system takes biomechanical constraints into account when optimizing the distribution of forces generated across limbs during both movement planning and feedback control of a bimanual task. [ABSTRACT FROM AUTHOR]

Movement planning consists of choosing the intended endpoint of the movement and selecting the motor program that will bring the effector on the endpoint. It is widely accepted that movement endpoint is updated on a trial-by-trial basis with respect to the observed errors and that the motor program for a given movement follows the rules of optimal feedback control. In this article, we show clear limitations of these theories. First, participants in the current study could not tune their motor program appropriately for each individual trial. This was true even when the participants selected the width of the target that they reached toward or when they had learned the appropriate motor program previously. These data are compatible with the existence of a switching cost for motor planning, which relates to the drop in performance due to an imposed switch of motor programs. This cost of switching shares many features of costs reported in cognitive task switching experiments and, when tested in the same participants, was correlated with it. Second, we found that randomly changing the width of a target over the course of a reaching experiment prevents the motor system from updating the endpoint of movements on the basis of the performance on the previous trial if the width of the target has changed. These results provide new insights into the process of motor planning and how it relates to optimal control theory and to an action selection based on the reward consequences of the motor program rather than that based on the observed error. [ABSTRACT FROM AUTHOR]

Accurate motor planning in a dynamic environment is a critical skill for humans because we are often required to react quickly and adequately to the visual motion of objects. Moreover, we are often in motion ourselves, and this complicates motor planning. Indeed, the retinal and spatial motions of an object are different because of the retinal motion component induced by self-motion. Many studies have investigated motion perception during smooth pursuit and concluded that eye velocity is partially taken into account by the brain. Here we investigate whether the eye velocity during ongoing smooth pursuit is taken into account for the planning of visually guided manual tracking. We had 10 human participants manually track a target while in steady-state smooth pursuit toward another target such that the difference between the retinal and spatial target motion directions could be large, depending on both the direction and the speed of the eye. We used a measure of initial arm movement direction to quantify whether motor planning occurred in retinal coordinates (not accounting for eye motion) or was spatially correct (incorporating eye velocity). Results showed that the eye velocity was nearly fully taken into account by the neuronal areas involved in the visuomotor velocity transformation (between 75% and 102%). In particular, these neuronal pathways accounted for the nonlinear effects due to the relative velocity between the target and the eye. In conclusion, the brain network transforming visual motion into a motor plan for manual tracking adequately uses extraretinal signals about eye velocity. [ABSTRACT FROM AUTHOR]

A saccade triggered during sustained smooth pursuit is programmed using retinal information about the relative position and velocity of the target with respect to the eye. Thus the smooth pursuit and saccadic systems are coordinated by using common retinal inputs. Yet, in the absence of retinal information about the relative motion of the eye with respect to the target, the question arises whether the smooth and saccadic systems are still able to be coordinated possibly by using extraretinal information to account for the saccadic and smooth eye movements. To address this question, we flashed a target during smooth anticipatory eye movements in darkness, and the subjects were asked to orient their visual axis to the remembered location of the flash. We observed multiple orientation saccades (typically 2-3) toward the memorized location of the flash. The first orienting saccade was programmed using only the position error at the moment of the flash, and the smooth eye movement was ignored. However, subsequent saccades executed in darkness compensated gradually for the smooth eye displacement (mean compensation congruent with 70%). This behavior revealed a 400-ms delay in the time course of orientation for the compensation of the ongoing smooth eye displacement. We conclude that extraretinal information about the smooth motor command is available to the saccadic system in the absence of visual input. There is a 400-ms delay for smooth movement integration, saccade programming and execution.