Motor lateralization is characterized by a serial hybrid control scheme

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Abstract

Our previous studies of limb coordination in healthy right- and left-handers led to the development of a theoretical model of motor lateralization, dynamic dominance, which was recently supported by studies in patients with unilateral stroke. One of our most robust findings was on single-joint movements in young healthy subjects [. Sainburg and Schaefer (2004) J Neurophysiol 92:1374-1383]. In this study, subjects made elbow joint reaching movements toward four targets of different amplitudes with each arm. Although both arms achieved equivalent task performance, each did so through different strategies. The dominant arm strategy scaled peak acceleration with peak velocity and movement extent, while the nondominant strategy adjusted acceleration duration to achieve the different velocities and distances. We now propose that these observed interlimb differences can be explained using a serial hybrid controller in which movements are initiated using predictive control and terminated using impedance control. Further, we propose that the two arms should differ in the relative time that control switches from the predictive to the impedance mechanisms. We present a mathematical formulation of our hybrid controller and then test the plausibility of this control paradigm by investigating how well our model can explain interlimb differences in experimental data. Our findings confirm that the model predicts early shifts between controllers for left arm movements, which rely on impedance control mechanisms, and late shifts for right arm movements, which rely on predictive control mechanisms. This is the first computational model of motor lateralization and is consistent with our theoretical model that emerged from empirical findings. It represents a first step in consolidating our theoretical understanding of motor lateralization into an operational model of control.

Original languageEnglish (US)
Pages (from-to)153-167
Number of pages15
JournalNeuroscience
Volume196
DOIs
StatePublished - Nov 24 2011

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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