Motor Control: Creating a Natural Science of Biological Movement

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Motor control is a young and aspiring field of natural science. Over the past 40 years, it has become an established field of study with several important theoretical developments, including the equilibrium-point hypothesis and its more recent version known as the control with referent spatial coordinates, the principle of abundance, the uncontrolled manifold hypothesis, and the concept of dynamic neural field as the means of task formulation. Important experimental advances have included the exploration of the notion of synergies, the links between descending signals from the brain and referent coordinates of the effectors, and applications of motor control principles to analysis of disordered movements. Further maturation of motor control requires focusing on theory-driven studies. It promises fruitful applications to applied fields such as movement disorders and rehabilitation.

Motor control is a relatively young field under the broad umbrella of kinesiology, younger than athletic training, biomechanics, exercise physiology, motor behavior, motor learning, sport psychology, and other established areas. Forty years ago, the seminal book edited by George Brooks had only one chapter with the expression “motor control” in the title (Spirduso, 1981) included in the section “Motor Behavior.” This chapter, titled “The Emergence of Research in Motor Control and Learning,” emphasized the multidisciplinary nature of motor control, its relations to physiology, psychology, and motor behavior, and the fact that it was, indeed, an emerging field of study. Another chapter, by Stelmach et al. (1981), was titled “Current and Prospective Issues of Motor Behavior.” This chapter described the state of motor control and its relations to developments in other fields, such as control theory and information processing. It also emphasized the complexity of natural movements, which limited the application of the control theory developed for relatively simple systems. Over the past 40 years, motor control has become a recognized part of natural science in general and kinesiology. There remains confusion on what exactly motor control is and how it differs from other subfields of kinesiology. The main purpose of this article is to offer a definition for motor control and to briefly review its recent progress and hot topics.

Motor control can be defined as a field of natural science exploring laws of nature that define the interaction among the central nervous system, peripheral motor apparatus, sensory organs, and the environment during biological movements. Laws of nature are concise descriptions of our observations of objects. Commonly, they are expressed as equations that link salient variables describing states of the objects with the help of parameters. For example, Newton’s famous Second Law links force (F) and acceleration (a) of objects with inertia with the help of a parameter termed mass (m): F = m•a. Movements of biological systems obey all the laws of nature described for the inanimate world. However, there is something special about biological movement. Biological objects commonly walk uphill and swim against the current, something that inanimate objects do not do. Such actions suggest that there are biology-specific laws of nature, still unknown (or poorly known) to us, which allow animals to display such unusual behaviors. Motor control aims to discover those laws of nature and explore how they are implemented in biological systems and in the central nervous system of higher animals, including humans. Let me admit upfront that we are currently at a pre-Galilean stage (if we compare motor control with classical physics) and only attempt to formulate possible biology-specific laws of nature.

Motor control builds on the foundation of knowledge provided by subfields of kinesiology with longer history. Studies in motor control are designed with lessons from psychology, motor behavior, and motor learning in mind. Such studies frequently use tools provided by biomechanics and neurophysiology. The distinguishing feature of motor control research is its attempts to link experimental and theoretical studies to laws of nature and to strive toward exact definitions of key concepts. A major problem in the field is the tradition of using terms without exact definitions, for example, such commonly used terms as synergy, motor program, motor command, muscle tone, joint stiffness, and so forth. Consequently, a major challenge is to provide definitions for all those terms (or, if this is not feasible, to stop using them!) that would make them comparable with basic concepts in classical physics and quantifiable in experimental studies (Latash & Zatsiorsky, 2016).

Recent Developments in Motor Control

Let me start by summarizing what has happened with motor control over the past 40 years. Most obviously, it has become a field of its own. In particular, there is the International Society of Motor Control, which has its official journal “Motor Control,” runs a series of biennial conferences “Progress in Motor Control,” awards every 2 years the Bernstein Prize for outstanding contributions to motor control, and helps organize the Annual Motor Control Summer School. All of these developments happened within the past 25 years. There are courses in motor control at both undergraduate and graduate levels offered by many programs in kinesiology, textbooks and reference books with “motor control” in the title, and national and international conferences on motor control.

Motor control has been linked to natural science starting, at least, with the seminal book by Kugler and Turvey (1987). These links separate motor control from purely behavioral studies and engineering (including control theory). Behavioral studies explore regularities in variables without trying to unite them into laws of nature. Engineering and control theory are well-developed fields with a purpose to design, build, and control inanimate (typically, man-made) objects. These approaches are well suited for building artificial objects (e.g., robots) that mimic certain features of biological movements without trying to understand laws of nature that allow biological objects to move. However, assuming that the central nervous system performs computational operations like those designed by engineers to control ballistic missiles and other artificial objects is not a serious scientific hypothesis.

Nikolai Bernstein, considered by many as the founder of motor control, described in his classical book “On the Construction of Movements” (Bernstein, 1947/2020) a multilevel hierarchical control scheme of human movements. In that scheme, he separated two closely intertwined aspects of motor control, control (generating time profiles of neural variables leading to a desired movement) and coordination (recruiting multiple elements to ensure movement stability). One of the levels in Bernstein’s scheme was termed the level of synergies. Bernstein emphasized two important roles of this level: (a) alleviating the famous problem of motor redundancy (see later) by uniting elements (joints, muscles, etc.) into groups and (b) ensuring dynamic stability of movements, which Bernstein viewed as paramount for any functional movement given the unpredictable external forces and mechanical coupling of body segments.

Computational tools have been developed to explore both aspects of synergies. Groupings of elements during natural movements have been quantified and explored using matrix factorization methods, such as principal component analysis, factor analysis, nonnegative matrix factorization, and similar tools (Ting, 2007; Tresch et al., 2006). These studies have led to several exciting findings in studies of multimuscle synergies during whole-body movements, such as standing and walking, as well as in studies of their development and deterioration with age and neurological disorders (Chvatal et al., 2013; Ivanenko et al., 2007; Ivanenko et al., 2013; Ivanenko et al., 2009; Santello & Lang, 2015). There was also a qualitative leap forward in studies of movement stability with the introduction of the uncontrolled manifold (UCM) hypothesis (Scholz & Schöner, 1999; Schöner, 1995) and its associated computational apparatus for analysis of stability of potentially important performance variables in multidimensional spaces of elemental variables (Latash et al., 2007). This breakthrough has led to numerous studies of stability of biological actions across tasks, species, and populations (Latash, 2008, 2019).

Arguably, the most important development in the field of motor control has occurred in its theoretical foundation, which allowed a shift in studies of biological movements from phenomenology to the formulation of laws of nature. The natural science (physical) approach to biological movements can be traced back to the mid-1960s when Anatol Feldman introduced the equilibrium-point (EP) hypothesis (Feldman, 1966, 1986). The EP hypothesis viewed the neural control of a muscle as the process of setting time-varying values of the threshold of the stretch reflex. This value (λ) played the role of a parameter in the dependence of active muscle force (FA) on muscle length (L): FA = ƒ(L – λ), when L > λ, where ƒ is a function. In other words, unlike traditional hypotheses, the EP hypothesis assumed that the brain did not specify any of the peripheral variables measured in typical behavioral experiments (such as forces, coordinates, muscle activations, etc.). Instead, it uses parametric control, that is, specifies parameters (values of λ) in the law of nature common across all skeletal muscles and many species. Forces, displacements, and muscle activations emerge as a result of interaction between the moving effector and environment and can have different magnitudes depending on the external force field. This basic assumption was very much in line with Bernstein’s idea that the brain could not specify mechanical variables during biological movements (Bernstein, 1947/2020). This assumption also separated the neural control of biological movements from movements in the inanimate world. Indeed, the only way to change movement of an inanimate object is to apply force to it. Consequently, the control of such objects has been based on precomputation and implementation of requisite force profiles by force generators (actuators). According to the EP hypothesis and its current developments, the central nervous system does not (and cannot, in principle!) specify forces but parameterizes relevant laws of nature such that forces and other peripheral variables emerge with an equally important role played by the time-varying external force field.

Until the 1990s, the EP hypothesis was limited to analysis of the neural control of individual muscles and simple joints. Then, the idea of parametric control was expanded to movements of arbitrary multimuscle systems up to the whole body (Feldman, 2015; Latash, 2019). This step required the introduction of the concept of a spatial referent coordinate (RC) as a multidimensional parameter that links the force vector acting on a moving effector and its coordinate. Note that for a single muscle, RC is equivalent to λ. Recently, the idea of control with RCs has been generalized to multieffector systems and merged with the UCM hypothesis (Latash, 2010; Martin et al., 2009). This approach has been developed not only for motor actions but also for perception and, potentially, for cognitive processes as well as for various movement disorders (Latash, 2019, 2021).

According to the idea of control with spatial RCs, the central nervous system sets a relatively low-dimensional RC at the task level (e.g., to move a fingertip to a point in space, one must specify only three coordinates). Furthermore, the low-dimensional task-level RC leads to the emergence of higher dimensional RCs at the levels of elements, such as joints and muscles. This is illustrated in Figure 1. Note that the mapping shown in this figure involves multiple few-to-many transformations, which are organized with the help of feedback loops (Martin et al., 2009) both within the central nervous system and from peripheral receptors to ensure that the task-relevant salient variables encoded by the task-level RC are stable against changes in the intrinsic body states and external force field. We will return to this topic later.

Figure 1
Figure 1

—The central nervous system sets a relatively low-dimensional RC at the task level. Furthermore, there is a sequence of few-to-many mappings leading to the emergence of higher-dimensional RCs at the levels of elements, such as joints and muscles. Back coupling, both within the central nervous system and from peripheral receptors, stabilizes action encoded in the task-level RC. RC = referent coordinate; DOF = degrees of freedom.

Citation: Kinesiology Review 10, 3; 10.1123/kr.2021-0011

Feldman (2019) made a potentially very important step by linking theoretical constructs from the theory of control with RCs to the large body of experimental material in the field of neuronal population coding. The brilliant series of studies pioneered by Apostolos Georgopoulos and coworkers (Georgopoulos et al., 1992; Georgopoulos & Carpenter, 2015; Georgopoulos et al., 1986) have led to an appreciation of the fact that neurons in brain areas, including cortical motor areas, encode movement-related variables only as populations. Traditionally, changes in neuronal population vectors have been viewed as direct precursors of mechanical variables, such as direction of arm movement, direction of the force vector produced by the effector, and so forth. This understanding contradicts Bernstein’s conclusion that the brain, in principle, cannot prescribe peripheral mechanical variables. Feldman (2019) has reconsidered this body of data and suggested that it reflected encoding not of movement mechanics but of time-varying parameters (such as λ and RC) that produced mechanics only indirectly after interaction with afferent signals reflecting the current external force field. So, signals along descending pathways from the brain have been reconsidered as encoding not coordinates, forces, or any variables computed from forces and coordinates but parameters of laws of nature. This view has been supported by several studies with stimulation of different descending pathways (Ilmane et al., 2013; Raptis et al., 2010; Zhang et al., 2018).

The Big Questions

Studies of motor behavior that have as their goal the understanding of the neural mechanisms of motor control can be classified into three stages: (a) Stage 1: Studies of outcome performance variables, their interrelations, and their relations to task variables; (b) Stage 2: Studies of what these results mean for the neural control of movement within a theory of motor control; and (c) Stage 3: Studies of the role of specific neural structures and loops in implementing the hypothetical control schemes and producing the observed behavior.

For considerable time, studies did not progress beyond Stage 1, and interpretations of the results were formulated using the same variables that were collected in the experiment. This led to several hypotheses about signals from the brain encoding patterns of those peripheral variables. Typical examples are theories of motor control based on precomputation and implementation of requisite forces or muscle activation patterns (Ghez et al., 1991; Gottlieb et al., 1989; Hinder & Milner, 2003). These theories have been complemented by hypotheses about the existence of internal models in the brain that consider predicted interactions among body parts and of the effectors with the environment and produce patterns of neural activity adequate to implement the desired mechanics (Kawato, 1999; Shadmehr & Wise, 2005; Wolpert et al., 1998). One more time, it is important to remember the warning by Bernstein that the brain cannot, in principle, prescribe peripheral outcome variables. It must do this indirectly by specifying parameters of respective laws of nature.

Currently, we are at Stage 2 when measured variables are interpreted within a specific motor control theory as changes in hidden (control) variables and parameters. As the reader has probably surmised, the author not only likes the idea of control with spatial RCs but thinks that there is currently no viable alternative to this theory. This conviction is based on literally hundreds of studies in animals and humans (including patient populations), starting from the seminal experiments by Feldman (1966) and Feldman and Orlovsky (1972), which have provided ample experimental support for this theory (Feldman & Levin, 1995; Feldman, 2015; Latash, 1993, 2019). Any theory is only as good as its ability to generate nontrivial experimentally testable predictions (i.e., predictions that cannot be made based on alternative theories). Generating such predictions and testing them in experiments may be viewed as attempts to disprove the theory or, at least, force its major modification. In recent years, several such predictions have been made and tested (Cuadra et al., 2020; Latash, 2021; Mullick et al., 2013; Zhang et al., 2018), and so far, the theory has not only survived but confirmed its predictive power.

Moving to Stage 3 is a truly big undertaking, namely mapping parameters used in the control of movements to neurophysiological processes within the body. There is promising progress, at least as far as the neurophysiological origin of λ in the single-muscle control scheme is concerned. Lambda has been associated with subthreshold depolarization of the corresponding alpha-motoneuronal pool (Feldman, 2015), which effectively changes the threshold of the stretch reflex. This development has emphasized the importance of the stretch reflex, which couples the two worlds, the world of mechanics and the world of neural processes, without computations. However, at any functional level, even for a single joint spanned by more than two muscles, there is no established knowledge or even prevailing hypothesis about the neural implementation of control with RCs.

The scheme of control with spatial RCs has been developing primarily in a bottom-up direction, starting from the control of a single muscle, then moving to the control of a simple joint controlled by an agonist–antagonist muscle pair (Feldman, 1980), and then to the control of a multijoint effector. A complementary top-down approach has been developed by the group of Gregor Schöner (Erlhagen & Schöner, 2002; Schöner & Thelen, 2006). The neural field theory developed by this group starts with the basic question of selecting a target from potentially numerous objects in the visual field. In other words, it starts with the big question of where and how motor tasks emerge. A conceptual motor control scheme (compatible with the idea of control with spatial RCs) has been presented and developed by this group (Martin et al., 2009). This scheme unites the idea of control with RCs with concepts associated with the UCM hypothesis. It considers the contribution of various brain structures to defining the timing properties of the planned action, to the generation of an appropriate time-varying RC trajectory, and to the contribution of back-coupling loops, both from peripheral receptors and using short-latency loops within the central nervous system (Latash et al., 2005), to stability of higher-level variables encoded at the task level. Such stabilization is assumed to be reflected in covaried involvement of control variables at the higher dimensional, hierarchically lower levels related to the control of the elements involved, such as limbs, digits, joints, muscles, and so forth.

The Hot Topics

This is the appropriate time to remind the reader that this is a subjective review of the state of motor control, and the author’s view on the truly hot topics may not be shared by many of his respected colleagues. In particular, the author does not think that trying to discover software in the brain that allows it to control functional movements is promising or even meaningful. There is no software in the brain of an animal because it is not a man-made, programmed device: we accept the assumption that it emerged in the process of evolution and was not created by a Supreme Programmer. Since no natural object can add numbers with any degree of accuracy, assuming that the brain solves equations of motion (or any other equations) makes little sense.

So, how can one advance the field of motor control as a subfield of natural science? A major obstacle inherent to Stage 2 is the lack of methods for reliable experimental investigation of control variables. Here we face a situation like the Heisenberg principle of indeterminacy but at a macroscale: application of an external perturbation to a moving effector leads, at very short time delays, to changes in parameters that are presumably specified by the central nervous system. There have been a few attempts to circumvent this lack of “lambda-meters.” Some of them are based on linear models of the moving effectors (Latash & Gottlieb, 1991) and likely underrepresented reflex-mediated damping (Gribble et al., 1998). Other methods are applicable only to relaxed muscles and require the application of multiple perturbations at different speeds (Calota et al., 2008). Recently, a method has been developed using the “inverse piano” device, which allows application of smooth positional perturbations to human fingers during pressing tasks (Martin et al., 2011). In subjects who had been trained “not to react” to such perturbations, the fingertip force and coordinate trajectories show very close to linear relations (Ambike et al., 2016), which allows estimation of the intercept (RC) and apparent stiffness of the effector (finger) and uses them as a reflection of two basic commands, the reciprocal and coactivation commands (Feldman, 1980). Still, this method rests on a number of assumptions and is applicable only to steady-state tasks.

Another hot topic is the role of sensory information in guiding purposeful movements. The effects of sensory information on movement are many and varied. They involve spinal reflexes, reflex-like reactions that emerge at delays longer than those of spinal reflexes and shorter than the shortest simple reaction time (typically, between 40 and 90 m/s), action–perception coupling studied within the field of ecological psychology (Gibson, 1979; Turvey, 2007; Warren, 2006), and intentional movement adjustments to changes in the environment. The current knowledge of the involved neural circuitry is fragmented, and these phenomena are rarely considered within the approach to motor control based on laws of nature. Typical studies explore the effects of sensory information on mechanical and electromyographic variables not on control variables. Interpreting such data in terms of control is not always easy because part of the afferent inflow acts via the stretch reflex loop and is expected to affect muscle activations and mechanical variables even in the absence of changes in control variables. Other afferent signals may lead to changes in λ for the involved motoneuronal pools. Deciphering these effects in terms of changes in control variables is not trivial.

Relations between optimality and stability of actions form another, potentially very large and important, set of questions. Notions from the field of optimal control, including optimal feedback control, have been applied to studies of movements for a considerable time (Diedrichsen et al., 2010; Prilutsky & Zatsiorsky, 2002; Seif-Naraghi & Winters, 1990). The concept of optimality has been used to address the famous problem of motor redundancy (Bernstein, 1967; Turvey, 1990): how to select a specific pattern of involvement of numerous elements based on a task formulated using a low-dimensional set of parameters. Typical examples include selection of joint configurations for a desired location of the endpoint of a limb, selection of digit forces for a desired resultant force acting on a handheld object, selecting combinations of muscle activations for a desired overall mechanical effect, and so forth. Looking for an optimal solution to such problems assumes that a specific single solution that minimizes a certain cost function over movement time is preferred. An alternative approach to the existence of multiple elements at every level of analysis of movements has been developed based on the principle of abundance (Gelfand & Latash, 1998; Latash, 2012). Within this principle, the excess of elements is viewed not as a computational problem but as a very important feature of the design of the motor apparatus that allows stabilization of multiple salient performance variables in a task-specific way. According to the principle of abundance, no single (optimal) solution is chosen, but a whole family of solutions equally acceptable for the task can emerge.

Figure 2 illustrates a simple task of producing a certain magnitude of total force (FTOT) by two fingers pressing on an external object. According to the principle of abundance, stability of total force is reflected in covariation across trials of the magnitudes of the two finger forces. The cloud of data points across trials is expected to be elongated along the subspace (UCM for this task) shown with the solid line in Figure 2, where total force remains unchanged. Imagine that this cloud is centered about a point corresponding to a certain optimal sharing of FTOT between the finger forces (e.g., 50:50 as in Figure 2). Large deviations from this point along the UCM (e.g., Point a in Figure 2) are signs of high stability of FTOT and, at the same time, signs of violations of the optimality criterion. Hence, there is an inherent trade-off between stability and optimality (Park et al., 2010). A recent study has shown that healthy individuals show person-specific signatures when dealing with this trade-off (de Freitas et al., 2019), which may be viewed as personal traits, so far barely explored.

Figure 2
Figure 2

—The task is to produce a certain magnitude of FTOT by two fingers pressing on an external object. Stability of total force is reflected in covarying across trials magnitudes of the two finger forces, leading to an intertrial cloud of data points elongated along the UCM for this task shown with the solid slanted line. The cloud is centered about a point corresponding to a certain (possibly optimal) sharing between the finger forces (e.g., 50:50). Large deviations from this point along the UCM (e.g., Point a) are signs of high stability for FTOT and, at the same time, signs of violations of the optimality criterion. UCM = uncontrollable manifold; FTOT = total force.

Citation: Kinesiology Review 10, 3; 10.1123/kr.2021-0011

An extension of this topic is exploration of the stability–optimality trade-off in spaces of control variables. Indeed, as illustrated in Figure 1, control with RCs implies a sequence of few-to-many (abundant!) transformations. Are there individual-specific optimal patterns of involvement of RCs at a lower level to satisfy constraints encoded by RCs at the highest task level? Do covaried adjustments of RCs at a lower level stabilize RCs at a higher level? So far, we are aware of only one study that has tried to address these issues in a limited set of tasks (Reschechtko & Latash, 2018).

Arguably, the hottest topic is the links between activity in various brain structures and RCs for natural movements. A number of recent studies have provided evidence that activity along certain descending pathways from the human brain encodes time changes in RCs for the involved effectors (Ilmane et al., 2013; Raptis et al., 2010; Zhang et al., 2018). Bernstein wrote in one of his papers (Bernstein, 1935) that neuronal populations in the brain, including those in the primary motor cortex, were likely to reflect not the motor output but perception by the person of the task with motor means. In other words, those neuronal populations reflected the subjective perception of the task by the person in terms of possibility for action or, using more contemporary terms, affordances (cf. Gibson, 1979). This is a very interesting idea, which may lead to a new area of study linking brain activity patterns to spatial RCs expressed not in absolute physical units (such as meters) but in units of possible action. A related topic is how motor tasks emerge in the body. There is a promising approach developed in the form of the dynamic neural field theory (Erlhagen & Schöner, 2002; Richter et al., 2017), but this approach is currently only tentatively tied to specific brain structures and circuits.

There have been only a few examples of applications of basic principles of motor control to movement disorders. This may be one of the reasons for the relatively slow progress in such fields as motor rehabilitation. One positive example is the new approach to motor impairments traditionally addressed as spasticity based on the idea of the neural control of muscles with spatial thresholds of the stretch reflex (Jobin & Levin, 2000; Levin & Feldman, 1994; Mullick et al., 2013). This view combines the typical consequences of neurological injuries—paresis and uncontrolled muscle contractions—into a single scheme where both groups of clinical signs reflect inability of the patients to shift the stretch reflex threshold over its whole normal range (which is larger than the biomechanical range of muscle length changes). Recent studies have demonstrated that this approach allows predictions of the motor consequences of spasticity and the tracking of motor improvements with practice (Subramanian et al., 2018; Turpin et al., 2017).

Another group of clinical studies has addressed the issue of stability of salient performance variables (e.g., trajectory of the fingertip during pointing, trajectory of the center of mass during whole-body actions, and resultant force applied to the handheld object), which is commonly impaired in neurological patients. These studies (reviewed in Latash, 2019; Latash & Huang, 2015) have shown that the control of action stability suffers at very early stages of Parkinson’s disease (and a few other disorders) and can even be detected at preclinical stages when neurological examinations fail to detect clinical signs of the disease. Indices of stability are sensitive to pharmacological treatment and deep brain stimulation (Falaki et al., 2017; Falaki et al., 2018; Park et al., 2014). This makes them potentially very useful biomarkers of the disease progression and effects of therapy as well as of the pathological processes in various brain structures.

Concluding Comments: Future Directions and Integration With Other Subdisciplines

The field of motor control grew out of motor behavior, neurophysiology, psychology, and biomechanics with an important role played by information accumulated in the field of movement disorders. It continues to depend on progress in those established fields of kinesiology. In particular, most studies in motor control are performed using behavioral motor tasks, tools from biomechanics (including identification of salient mechanical body parameters, inverse kinematics, inverse dynamics, etc.), and methods from neurophysiology, including TMS, EMG, MRI, and electroencephalography. It is only natural that motor control is well represented at conferences specializing in other fields, such as meetings of the American Society of Biomechanics, Society for Neuroscience, North American Society for the Psychology of Sport and Physical Activity, and so forth.

However, motor control is a field of its own, and it cannot rely on progress in other areas offering solutions for problems related to the neural control of movements. This field aspires to be part of natural science on par with physics of the inanimate world. This means that researchers must focus on formulating biology-specific laws of nature and/or understanding how they emerge from basic laws of nature, common for both living and inanimate objects, in the process of evolution, in ontogenesis, and with specialized practice. Currently, we are still at a very early, pre-Galilean stage of development of motor control, which may be both a problem and a source of excitement: it is truly exciting to work in an area where laws of nature are unknown and wait to be discovered, formulated, and explored.

One of the dangers of contemporary experimental studies of biological movements is performing measurements for measurements’ sake. The widespread use of computers has made data cheap, and it does not take much ingenuity to measure behavioral variables in one or more subpopulations and compare these variables across those subpopulations (e.g., across younger and older subjects or across athletes and nonathletes) or across states of the selected subpopulation (e.g., before and after a fatiguing exercise or before and after practice). The number of such papers has skyrocketed in recent years. Such data may be useful practically but not for distinguishing among competing theories or moving a particular theory ahead. It is time to focus more on theory-driven experiments. Even studies based on a wrong theory are much better than theory-free ones.

To summarize, motor control is a young and aspiring field. Over the past 40 years, it has become an established field of study. Its maturation requires a focus on theory-driven studies. It promises fruitful applications to applied fields, such as motor disorders and rehabilitation.

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The author (mll11@psu.edu) is with the Dept. of Kinesiology, The Pennsylvania State University, University Park, Pennsylvania, USA.

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    —The central nervous system sets a relatively low-dimensional RC at the task level. Furthermore, there is a sequence of few-to-many mappings leading to the emergence of higher-dimensional RCs at the levels of elements, such as joints and muscles. Back coupling, both within the central nervous system and from peripheral receptors, stabilizes action encoded in the task-level RC. RC = referent coordinate; DOF = degrees of freedom.

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    —The task is to produce a certain magnitude of FTOT by two fingers pressing on an external object. Stability of total force is reflected in covarying across trials magnitudes of the two finger forces, leading to an intertrial cloud of data points elongated along the UCM for this task shown with the solid slanted line. The cloud is centered about a point corresponding to a certain (possibly optimal) sharing between the finger forces (e.g., 50:50). Large deviations from this point along the UCM (e.g., Point a) are signs of high stability for FTOT and, at the same time, signs of violations of the optimality criterion. UCM = uncontrollable manifold; FTOT = total force.

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    • Export Citation
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    • Crossref
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    • Export Citation
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    • Crossref
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  • Hinder, M.R., & Milner, T.E. (2003). The case for an internal dynamics model versus equilibrium point control in human movement. Journal of Physiology, 549(3), 953963. https://doi.org/10.1113/jphysiol.2002.033845

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ilmane, N., Sangani, S., & Feldman, A.G. (2013). Corticospinal control strategies underlying voluntary and involuntary wrist movements. Behavioral and Brain Research, 236, 350358. https://doi.org/10.1016/j.bbr.2012.09.008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ivanenko, Y.P., Dominici, N., & Lacquaniti, F. (2007). Development of independent walking in toddlers. Exercise and Sport Science Reviews, 35(2), 6773. https://doi.org/10.1249/JES.0b013e31803eafa8

    • Crossref
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    • Export Citation
  • Ivanenko, Y.P., Dominici, N., Cappellini, G., Di Paolo, A., Giannini, C., Poppele, R.E., & Lacquaniti, F. (2013). Changes in the spinal segmental motor output for stepping during development from infant to adult. Journal of Neuroscience, 33(7), 30253036. PubMed ID: 23407959 https://doi.org/10.1523/JNEUROSCI.2722-12.2013

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ivanenko, Y.P., Poppele, R.E., & Lacquaniti, F. (2009). Distributed neural networks for controlling human locomotion: Lessons from normal and SCI subjects. Brain Research Bulletin, 78(1), 1321. PubMed ID: 19070781 https://doi.org/10.1016/j.brainresbull.2008.03.018

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jobin, A., & Levin, M.F. (2000). Regulation of stretch reflex threshold in elbow flexors in children with cerebral palsy: A new measure of spasticity. Developmental Medicine and Child Neurology, 42(8), 531540. PubMed ID: 10981931 https://doi.org/10.1017/S0012162200001018

    • Crossref
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    • Export Citation
  • Kawato, M. (1999). Internal models for motor control and trajectory planning. Current Opinions in Neurobiology, 9(6), 718727. https://doi.org/10.1016/S0959-4388(99)00028-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kugler, P.N., & Turvey, M.T. (1987). Information, natural law, and the self-assembly of rhythmic movement. Lawrence Erlbaum Associates Publ.

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