Evidence for a dynamic-dominance hypothesis of handedness

Abstract

Handedness is a prominent behavioral phenomenon that emerges from asymmetrical neural organization of human motor systems. However, the aspects of motor performance that correspond to handedness remain largely undetermined. A recent study examining interlimb differences in coordination of reaching demonstrated dominant arm advantages in controlling limb segment inertial dynamics (Sainburg and Kalakanis 2000). Based on these findings, I now propose the dynamic-dominance hypothesis, which states that the essential factor that distinguishes dominant from nondominant arm performance is the facility governing the control of limb dynamics. The purpose of this study is to test two predictions of this hypothesis: 1) adaptation to novel intersegmental dynamics, requiring the development of new dynamic transforms, should be more effective for the dominant arm; 2) there should be no difference in adapting to visuomotor rotations performed with the dominant as compared with the nondominant arm. The latter prediction is based on the idea that visual information about target position is translated into an internal reference frame prior to transformation of the movement plan into dynamic properties, which reflect the forces required to produce movement. To test these predictions, dominant arm adaptation is compared to nondominant arm adaptation during exposure to novel inertial loads and to novel visuomotor rotations. The results indicate substantial interlimb differences in adaptation to novel inertial dynamics, but equivalent adaptation to novel visuomotor rotations. Inverse dynamic analysis revealed better coordination of dominant arm muscle torques across both shoulder and elbow joints, as compared with nondominant arm muscle torques. As a result, dominant arm movements were produced with a fraction of the mean squared muscle torque computed for nondominant arm movements made at similar speeds. These results support the dynamic-dominance hypothesis, indicating that interlimb asymmetries in control arise downstream to visuomotor transformations, when dynamic variables that correspond to the forces required for motion are specified.

Fig. 1

A Methods side view: subjects were seated in a dental-type chair with the arm supported by an airjet system that removed the effects of friction on arm movement. Targets and the cursor representing finger position were back-projected on a screen placed above the arm. A mirror placed below this screen reflected the image, such that the projection was perceived in the plane of the arm. B Top view of the system described above. The positions of the Flock of Birds sensors and the placement of the removable mass are shown

Fig. 2

A Representative hand paths from subject 1 from all three experimental sequences are shown. The last two trials from the pre-exposure sequence are shown in gray (dashed lines), underlying all other trials. The first trials from the mass-exposure session (mass-perturbed, left column), the last two trials from the same session (mass-adapted, center column), and the first two trials from the post-exposure session (aftereffects, right column) are displayed. Dominant paths are shown above, whereas nondominant hand paths are shown below. B Mean performance measures, hand-path linearity and final position error, are averaged across all subjects and shown for individual cycles of movement. Baseline performance, measured for each subject as the average of the last eight cycles from the pre-exposure sequence, has been subtracted from each value prior to computing the average across subjects. Thus, the performance measures shown represent a change from baseline performance, and can be either positive or negative. Each cycle represents the average of a single movement to each of eight targets across all subjects (mean ± SE). The time course of each experimental sequence, pre-exposure, mass-exposure, and post-exposure, is shown. Vertically oriented gray bars mark the cycles for which the trials in Fig. 2A were extracted. Data have been fit to exponential functions, using the “CurveFit” function in Igor Pro (Wavemetrics)

Fig. 3

A Representative hand paths from subject 1 from all three experimental sequences are shown. The last two trials from the pre-exposure sequence are shown in gray (dashed lines), underlying all other trials. The first trials from the rotation exposure session (rotation-perturbed, left column), the last two trials from the same session (rotation-adapted, center column), and the first two trials from the post-exposure session (aftereffects, right column) are displayed. Dominant paths are shown above, whereas nondominant hand paths are shown below. B Mean performance measures, hand-path linearity and final position error, are averaged across all subjects and shown for individual cycles of movement. Baseline performance, measured for each subject as the average of the last eight cycles from the pre-exposure sequence, has been subtracted from each value prior to computing the average across subjects. Thus, the performance measures shown represent a change from baseline performance, and can be either positive or negative. Each cycle represents the average of a single movement to each of eight targets, across all subjects (mean±SE). The time course of each experimental sequence, pre-exposure, mass-exposure, and post-exposure, is shown. Data have been fit to exponential functions, using the “CurveFit” function in Igor Pro (Wavemetrics).Vertically oriented gray bars mark the cycles for which the trials in Fig. 2A were extracted

Fig. 4

Representative baseline trials toward target 2: shoulder, elbow, and finger paths are shown for nondominant (left) and dominant (right) arms of subject 2 (top). Sequential upper arm and forearm positions are drawn every 10 msec. Time series graphs of tangential finger velocity, shoulder joint torque components, and elbow joint torque components are shown. For ease of comparisons, only the first 250 msec (Marked by vertical dashed line in tangential finger velocity graphs) of the movement is shown for the joint torque plots. Torque components include net torque (heavy black), interaction torque (heavy gray), elbow muscle torque (light black), and shoulder muscle torque (dashed). Note that the elbow muscle torque has equal amplitude, but opposite signs at both joints

Fig. 5

Measures of peak tangential finger velocity (top), and contributions of elbow muscle torque impulse (middle) and shoulder muscle torque impulse (bottom) to shoulder net torque impulse are shown for baseline trials (left), mass-adapted trials (center), and rotation-adapted trials (right). These measures have been quantified as a percentage of shoulder net torque. Means and standard errors across all subjects for the average measure computed across all trials for the last eight cycles of the preexposure sequence (left), the mass-exposure sequence (center), and the rotation-exposure sequence (right) are shown. The results of pairwise comparisons (Bonferroni–Dunn) are reported as significant (**), or the p-value is shown