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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]

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. [ABSTRACT FROM AUTHOR]

Expectation of upcoming events is an essential cognitive function on which anticipatory actions are based. The neuronal basis of this prospective representation is poorly understood. We trained rhesus monkeys in a smooth-pursuit task in which the direction of upcoming target motion was indicated using a color cue. Under these conditions, directional expectation frequently evoked anticipatory smooth movements. We found that the activity of a population of neurons in the supplementary eye fields encoded the expected future direction of the target. Neuronal activity increased after presentation of the cue, indicating future target motion in the preferred direction. Neuronal activity either remained unaltered or was reduced if the antipreferred direction was cued. In addition, ~30% of these neurons were more active during trials with anticipatory pursuit in the preferred direction than during trials when monkeys did not anticipate target motion onset. This subset of recorded neurons encoded the direction of the subsequent anticipatory pursuit. We hypothesize that the neural representation of directional expectation could be conceptualized as a competitive interaction between pools of neurons representing likely future events, with the winner of this competition determining the direction of the subsequent anticipatory movement. Similar mechanisms could drive prediction before movement initiation in other motor domains. [ABSTRACT FROM AUTHOR]

During object manipulation, humans adjust the grip force to friction, such that slippery objects are squeezed more firmly than sticky ones. This essential mechanism to keep a stable grasp relies on feedback from tactile afferents innervating the fingertips, that are sensitive to local skin strains. To test if this feedback originates from the skin-object interface, we asked participants to perform a grip-lift task with an instrumented object able to monitor skin strains at the contact through transparent plates of different frictions. We observed that, following an unbeknown change in plate across trials, participants adapted their grip force to friction. After switching from high to low friction, we found a significant increase in strain inside the contact arising ~100 ms before the modulation of grip force, suggesting that differences in strain patterns before lift-off are used by the nervous system to quickly adjust the force to the frictional properties of manipulated objects. [ABSTRACT FROM AUTHOR]