The Central Nervous System Does Not Minimize Energy Cost in Arm Movements

Abstract

. It has been widely suggested that the many degrees of freedom of the musculoskeletal system may be exploited by the CNS to minimize energy cost. We tested this idea by having subjects making point-to-point movements while grasping a robotic manipulandum. The robot created a force field chosen such that the minimal energy hand path for reaching movements differed substantially from those observed in a null field. The results show that after extended exposure to the force field, subjects continued to move exactly as they did in the null field and thus used substantially more energy than needed. Even after practicing to move along the minimal energy path, subjects did not adapt their freely chosen hand paths to reduce energy expenditure. The results of this study indicate that for point-to-point arm movements minimization of energy cost is not a dominant factor that influences how the CNS arrives at kinematics and associated muscle activation patterns. I N T R O D U C T I O NA fundamental question in motor control is how the CNS coordinates the many kinematic and mechanical degrees of freedom of the body to control posture and movement (Bernstein 1967). Several theories of motor control are based on or inspired by the idea that the CNS arrives at a set of muscle activation patterns for a given task by minimizing a cost function in which minimization of energy cost takes part (e.g., Alexander 1997;Cruse 1986;Engelbrecht 2001;Hatze and Buys 1977;Kashima and Isurugi 1998;Nelson 1983;Rasmussen et al. 2001;Soechting et al. 1995;Todorov 2004;Todorov and Jordan 2002;Torres and Zipser 2002). Such theories have been shown to predict kinematic features for movement tasks in the absence of external loads, like relatively straight hand paths and bell-shaped velocity profiles. However, at least for unperturbed arm movements, many motor control theories predict these same kinematics (e.g., d 'Avella et al. 2006; Flash and Hogan 1985;Uno et al. 1989). In addition, even if kinematics and muscle activation patterns are in accordance with energy minimization, it would not represent evidence that energy cost is minimized by the CNS to generate motor commands. For example, the motor commands that minimize energy cost could have been selected over an evolutionary timescale.The goal of the present study was to directly test the extent to which energy cost is minimized by the CNS in the context of a motor learning task. One of the most well studied motor learning tasks involves making arm movements while grasping a robotic device that applies forces to the hand that are perpendicular and proportional to the hand velocity (Caithness et al. 2004). Unfortunately, the mechanics of these so-called curl fields are such that the minimal energy path is similar to movements in the absence of forces (see METHODS). Therefore this motor learning task is unsuitable to investigate whether energy minimization plays a role in the selection of kinematics and/or muscle activation patterns by the CNS.In the present study we designed a novel force field ...

Reduction of Metabolic Cost during Motor Learning of Arm Reaching Dynamics

“…Training subjects to use nonpreferred trajectories is difficult and hinders the ability to experimentally test whether movement trajectories are chosen to minimize metabolic cost (Alexander, 1997). In a recent study, subjects continued to move in a relatively straight line path to the target, even though it required greater end-point work than a curved movement path that was designed to be the minimum end-point work path (Kistemaker et al, 2010). They concluded that the CNS does not minimize metabolic cost.…”

Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback

“…The experimental setup was similar to that used in a previous study (Kistemaker et al 2010; see Fig. 1).…”

“…The 1-DOF and 2-DOF musculoskeletal models of the arm used in this study (Fig. 2) have been described in full detail elsewhere (Kistemaker et al 2006(Kistemaker et al , 2007b(Kistemaker et al , 2010see also van Soest and Bobbert 1993). In short, the 1-DOF model consisted of two rigid segments (representing forearm and upper arm) interconnected by two hinge joints (representing glenohumeral joint and elbow joint) and driven by four lumped muscle models.…”

“…We then used a musculoskeletal model of the arm, shown previously to adequately describe the static and dynamic behavior of the real system (Kistemaker et al 2005(Kistemaker et al , 2006(Kistemaker et al , 2007a, to assess the response quality when feeding back either muscle spindle afferents alone (while ignoring tendon compliance) or a combination of spindle and GTO afferents. We assessed the dynamical response of the model to 1) a position perturbation, 2) a sudden change in cocontraction of antagonistic muscles, and 3) an external constant torque perturbation.…”

Conclusions on motor control depend on the type of model used to represent the periphery

“…To investigate these signals or motor commands, a frequently used method is perturbing goal-directed arm movements with a mechanical load. (Bhushan and Shadmehr 1999;Debicki and Gribble 2004;Gottlieb 2000;Gribble and Ostry 2000;Hinder and Milner 2003;Izawa et al 2008;Kistemaker et al 2010;Smeets et al 1990, etc.). These researchers used the changes in movement kinematics in response to a mechanical perturbation to conclude what aspects of the movement are controlled and what information is used to customize motor commands to the external conditions.…”