Motor variability as a characteristic of the control of reaching movements: Influence of sensory input and task constraints

i Overview . iii 1 General Introduction . 1 1.1 About the control of movement variability . 2 1.2 Motor control under changing environmental conditions . 4 1.3 Excursus: Optimal feedback control . 7 1.4 Methodological considerations . 9 1.5 Aim of the Thesis . 11 2 Cumulative Thesis . 13 Joint angle variability in the time course of reaching movements . 15 Rapid adjustment of human motor control strategies in reaching movements under temporal proprioceptive deafferentation. 33 Synergistic control of joint angle variability: Influence of target shape . 51 Age-related differences in the stabilization of important task variables in reaching movements . 69 3 General Discussion . 79 3.1 Flexibility in movement control . 80 3.2 Critical Discussion on the Method . 83 3.4 Prospective future research directions . 85 3.5 Concluding remarks . 86 Bibliography . 87 Acknowledgements. 93 List of Publications . 955 1 General Introduction 1 1 General Introduction When swimming through open water, each arm stroke can be characterized by two seemingly opposing features: flexibility and stability in movement execution. Both, flexibility and stability refer to the existence of variability in repeated arm strokes. Thereby, flexibility reflects the variability in the coordination of the redundant degrees of freedom (DoF) of the arm. This is of importance for the adjustment of the swimming technique to the everchanging environmental conditions as for example the height of the waves. In contrast, stability refers to the variability in the movement outcome. Ideally, each arm stroke is executed stable and nearly optimal to provide the greatest propulsion. Interestingly, flexibility and stability in movement execution are related to each other, as the first grants the second. To better understand this seeming contradiction one has to think about the two different levels of analysis when talking about flexibility and stability in movement execution. While flexibility, as described above, refers to the effector space, which means for example joint angles for arm movements, stability refers to the task space, as for example final arm posture. The distinction between effector and task space is of special interest when investigating the influence of sensory input or external task constraints (e.g. visual input for estimating drift or wave height, respectively) on the control of well-trained movements, where task performance changes little in the presence of changing environmental conditions. Then, analyzing the effector space can bring out differences in the control of movements that did yield similar task performances. In recent times, the distinction between effector and task space became especially meaningful as the advantages of motor redundancy foregrounded (Latash, Scholz & Schöner, 2007; Todorov & Jordan, 2002). Motor redundancy describes the phenomenon that the number of DoF within the effector space is greater than the one within the task space, resulting in an infinite number of possible task solutions. How the healthy human motor system takes use of motor redundancy to account for different environmental conditions in movement control is a question still under debate. In this thesis, experimental work on the control of complex reaching movements under different sensory inputs and different external task constraints will be presented. In that regard, analyzing movement variability served as an approach to be able to investigate movement control. Reaching movements were chosen as the experimental task as they represent an elemental part of human’s every-day motor behavior, as for example reachto-grasp a cup of coffee or reach-to-grasp the door handle. Due to that, they are usually performed with high quality, even under changing sensory inputs or changing external constraints (Cisek, Grossberg & Bullock, 1998; Fitts, 1954). At the same time, reaching movements are complex as they are requiring the coordination of numerous DoF in 3D. The sum of these points makes reaching movements to a very interesting and suitable motor task to study. 1 General Introduction 2 In the following, I will introduce the two main theoretical columns on which my thesis is based. First, the existing knowledge about the control of movement variability will be presented. Within that context, the distinction between effector and task space will be introduced. This will clarify some of the observed inconsistencies in the existing empirical evidence on the control of reaching movements by assigning them to the analysis of either one of the two levels. Further, the “problem of redundancy” will be introduced. This subsection describes how the human motor control system can take advantage of the superior number of effector DoF in the control of complex movements. Representing the second theoretical column, empirical evidence about the ability of the human motor control system to adjust to changing internal and external conditions will be reviewed. Subsequently and before stating the aims of this thesis, a short excursion on optimal feedback control will be made and different methodological approaches in analyzing movement variability will be considered. Finally, the aim of this thesis will be defined. 1.1 About the control of movement variability Movement variability is an inherent characteristic of human motor behavior. Within the last recent years it has attracted a lot of scientific attention and its examination has become much more nuanced since then. In the following, a short overview about the current state of empirical and theoretical knowledge will be given. 1.1.1 Movement variability in the effector and the task space With respect to the frame of reference, movement variability can be considered as either a sign of healthy or impaired motor control (Berardelli, et al., 1996; Cirstea & Levin, 2000; Latash & Anson, 2006; Latash, Scholz & Schöner, 2007). It is important to note that the seemingly contrasting positive or negative attribution of movement variability is often due to an analysis of movement execution on different levels. In principal, two levels of analyzing movement variability have to be distinguished: effector space and task space (also termed as intrinsic and extrinsic space; see e.g. Desmurget, et al., 1995). Thereby, variability in the effector space is usually referred to as flexibility in movement execution (Latash, Scholz & Schöner, 2007; Scholz & Schöner, 1999) or as stereotype when variability is absent (Müller & Sternad, 2009). On the other hand, there is variability in the task space that defines the quality in movement execution. In motor control research, much of the theoretical and experimental work has focused on analyzing movement variability in the task space. Recently, the “minimum variance” model was supposed, assuming that in reaching movements the motor control system tries to minimize variability of e.g. final hand position (Harris & Wolpert, 1998; see also 1.3 below). This theory builds the frame within much of the existing empirical evidence about the control of reaching movements can be ranged (see for example Grea, Desmurget & Prablanc, 2000; Simmons & Demiris, 2006). Though, it seems as if for redundant effector systems minimizing movement variability at movement end 1 General Introduction 3 explains only parts of the strategy underlying human movement control (Latash, Scholz & Schöner, 2002; see also 1.3). 1.1.2 The problem of redundancy Motor redundancy is a long-known phenomenon in motor control research. It describes the fact that the number of DoF in the effector space is often superfluous to the number of DoF in the task space. As a result, there are an infinite number of possible solutions of the motor task. To exemplify this phenomenon let’s assume to reach towards an object. The location of the object in space can be described by three dimensions (dimensionality of the task space): position in horizontal and vertical direction and in depth. The posture of the arm when grasping this object can be described by seven joint angles (i.e. three shoulder angles, two angles of the elbow, and three wrist angles; dimensionality of the effector space). As the dimensionality of the effector space is greater than the one of the task space there are theoretically infinite possible combinations of joint angles that would all result in successful grasping of the object. Fig. 1: Illustration of the Hammering-example of Bernstein (1967). The least variable points during the hammering trajectories were the locations of the two nails. The figure is obtained from Müller & Sternad, 2009. For a long time, redundancy was considered as a problem for the motor control system, as it requires a complex stra