Handedness can be explained by a serial hybrid control scheme

V. Yadav, R. L. Sainburg

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Our previous studies on healthy individuals and stroke patients led us to propose that the dominant and nondominant arms are specialized for distinct motor control processes. We hypothesize that the dominant arm is specialized for predictive control of limb dynamics, and the nondominant arm is specialized for impedance control. We previously introduced a hybrid control scheme to explain lateralization of single-joint elbow movements. In this paper we apply a similar computational framework to explore interlimb differences in multi-joint reaching movements: the movements of both arms are initiated using predictive control mechanisms, and terminated using impedance mechanisms. Four parameters characterize predictive mechanisms, four parameters characterize impedance mechanisms, and the ninth parameter describes the instant of switch between the two modes of control. Based on our hypothesis of motor lateralization, we predict an early switch to impedance control for the nondominant arm, but a late switch, near the end of motion, for the dominant arm. We fit our model to multi-joint reaching movements of each arm, made in the horizontal plane. Our results reveal that the more curved trajectories of the nondominant arm are characterized by an early switch to impedance mechanisms, in the initial phase of motion near peak velocity. In contrast, the trajectories of the dominant arm were best fit, when the switch to impedance mechanisms occurred late in the deceleration phase of motion. These results support a model of motor lateralization in which the dominant controller is specialized for predictive control of task dynamics, while the nondominant arm is specialized for impedance control mechanisms. For the first time, we are able to operationally define handedness expressed during multi-joint movements by applying a computational control model.

Original languageEnglish (US)
Pages (from-to)385-396
Number of pages12
StatePublished - Oct 1 2014

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)


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