Lateralized Sensitivity of Motor Memories to the Kinematics of the Opposite Arm Reveals Functional Specialization during Bimanual Actions

Abstract

It is generally believed that the dominant arm exhibits greater functional advantages over the nondominant arm in every respect, including muscular strength and movement accuracy. Recent studies have proposed that this laterality is due to different underlying control strategies for each limb rather than different limb capabilities constraining performance. However, the functional role and mechanisms of these different control strategies have yet to be elucidated. Here, we report a specialized function of the nondominant arm that plays a significant role only during bimanual movements. Right-handed human participants performed bimanual reaching movements while only one arm was subjected to a force field. Consistent with our previous study, adaptation to the force field decreased gradually as the movement direction of the opposite arm deviated from the trained direction. We also observed that the decrement of the adaptation was significantly greater for the nondominant left arm. According to our previously proposed theory, this poorer generalization of the left arm originated from a difference in parameters characterizing motor memory; the nondominant arm's motor memory was more strongly influenced by the opposite arm's kinematics. Remarkably, a model incorporating this lateralized memory predicted that the nondominant arm would demonstrate greater adaptability to force fields associated with the opposite arm's movement. We confirmed this prediction experimentally and found that this advantage of the left arm disappeared in left-handed human participants. We concluded that the secondary supporting role often played by the nondominant arm in bimanual actions reflects its specialization rather than its inferiority.

Context-dependent memory decay is evidence of effort minimization in motor learning: a computational study

“…Following previous motor primitive models (Thoroughman and Shadmehr, 2000 ; Donchin et al, 2003 ; Tanaka et al, 2009 ; Yokoi et al, 2011 ; Brayanov et al, 2012 ; Taylor et al, 2012 ; Yokoi et al, 2014 ), we assumed that the recruitment pattern of the motor primitives or the motor primitive activities are determined by a target direction θ and the Gaussian where the scaling parameter σ i = σ is independent of i ; φ i is the preferred direction (PD) of the i -th primitive, which is randomly sampled from a uniform distribution in the range [−π, π]; ||θ|| is a periodic function ||θ|| = ||θ + 2π|| such that ||θ|| = θ for −π ≤ θ < π (θ = 0 is defined as the target for a straight-forward reaching movement, Figure 1A ), and i = 1, …, N , and N is the number of primitives. The scaling parameter σ controls the number of primitives responsible for a reaching movement toward θ because the i -th primitive is activated independent of θ when σ is sufficiently large (i.e., the number of recruited primitives is large) and because the primitive shows its activity only in the case θ = φ i when σ is sufficiently small (i.e., the number of recruited primitives is small).…”

“…The aim of the task was to accurately reach to a given target by generating an additional motor command x to compensate for the movement error, i.e., e = p − x . Here, following several previous studies, I assumed that x represents the lateral force in the force adaptation (Yokoi et al, 2011 ; Brayanov et al, 2012 ; Yokoi et al, 2014 ) or the movement angle in the visuomotor rotation (Tanaka et al, 2009 ; Taylor et al, 2012 ) at the time of the peak velocity because these values are considered to represent the degree of adaptation in a feedforward controller, which many motor learning studies have focused on. That is, p represents an applied lateral force in a curl force field paradigm (the units of p , x , and e are newtons) and a rotated movement angle in a visuomotor rotation paradigm (the units of p , x , and e are degrees).…”

“…The motor primitive framework can naturally reproduce the generalization because a group of motor primitives recruited in reaching movements toward a target direction θ overlaps with a group of primitives recruited in movements toward other directions, θ′(≠ θ), which results in learning effects embedded in the motor primitives responsible for reaching movements toward θ are partially available in other reaching movements. Recent studies have revealed that the motor primitive framework can reproduce generalization in various situations, such as motor learning in a force field (Thoroughman and Shadmehr, 2000 ; Donchin et al, 2003 ) or visuomotor rotation (Tanaka et al, 2009 ), motor learning of bimanual reaching movements (Yokoi et al, 2011 ), motor learning of unimanual reaching movements when the both target direction and shoulder posture change (Brayanov et al, 2012 ), the dependence of the generalization on error-feedback information (Taylor et al, 2012 ), and the generalization between reaching movements of the right hand and those of the left hand (Yokoi et al, 2014 ). Thus, the properties of error minimization or generalization in a wide range of situations can be explained by the motor primitive framework.…”

“…In the following, I refer to the memory of learning effects as motor memory. Nearly all the models of motor primitives assume that the rate of motor memory decay is context independent (Thoroughman and Shadmehr, 2000 ; Scheidt et al, 2001 ; Donchin et al, 2003 ; Smith et al, 2006 ; Tanaka et al, 2009 ; Yokoi et al, 2011 ; Brayanov et al, 2012 ; Hirashima and Nozaki, 2012 ; Taylor et al, 2012 ; Takiyama and Okada, 2012a ; Yokoi et al, 2014 ), i.e., the motor memory learned with reaching movements toward θ decays during the trials of the reaching movements at the same speed as during trials of reaching movements toward other directions, θ′(≠ θ). In the motor primitive framework, the context-independent decay is equivalent to weight decay (Hirashima and Nozaki, 2012 ).…”

“…Thus, the reason that memory decay is context dependent remains unclear. Although motor learning has conventionally been modeled as an optimization framework (Thoroughman and Shadmehr, 2000 ; Scheidt et al, 2001 ; Donchin et al, 2003 ; Smith et al, 2006 ; Tanaka et al, 2009 ; Yokoi et al, 2011 ; Brayanov et al, 2012 ; Hirashima and Nozaki, 2012 ; Taylor et al, 2012 ; Takiyama and Okada, 2012a , b ; Yokoi et al, 2014 ), e.g., movement error minimization, no conventional optimization framework can reproduce the context-dependent memory decay, i.e., context-dependent memory decay raises the question of what is optimized in motor learning.…”