There has been a great deal of recent research examining this hypothesis, often based on using statistical analyses of EMGs during behavior. The basic approach in these experiments has been to 1) measure EMGs from a large number of muscles during a complex behavior (or more than one behavior), 2) use a computational analysis such as non-negative matrix factorization or independent components analysis to identify a set of synergies from the recorded EMGs, 3) evaluate whether the observed EMGs can be well described as the combination of these synergies, and 4) relate the identified muscle synergies to task relevant variables. Using such an approach, a wide range of motor behaviors have been suggested to be produced using muscle synergies [28
]. Other studies have used a more direct examination of muscle activations to identify and analyze muscle synergies, thereby avoiding the more indirect statistical analyses [38
Key to this approach is examining EMGs recorded under a rich enough range of behavioral conditions: the wider the range of behavioral conditions that can be explained by muscle synergies, the more support there is for such an explanation. In fact, one of the main critiques of experiments supporting the muscle synergy hypothesis is that they reflect task constraints rather than reflecting a neural control strategy [40
]. In this critique, the ability of muscle synergies to explain a behavior reflects the fact that there are only a few ways that a task can be successfully performed, once all the task constraints are fully accounted for. For instance, if one considers stability requirements in addition to explicit task variables, the apparent redundancy of muscle activation patterns is reduced [43
]. Similarly, if one considers additional potential demands placed on the CNS such as minimizing noise or other optimization criteria, then control of individual muscles could explain observed EMG patterns as well as muscle synergies [41
]. Finally, if one assumes smooth recruitment of muscles across smooth changes in task variables (e.g. across different directions of reaches or forces), one would expect that muscle activations would lie upon a low dimensional, albeit non-linear, manifold [42
]. Thus, it can be difficult to predict how truly redundant a task is or how surprising it would be to find a low dimensional solution to the task.
Recent experiments have attempted to address this critique by demonstrating that an impressive range of behaviors such as human reaching [44
] and posture [45
], primate grasping [46
], and frog locomotion [47
] and nocifensive reflexes [48
] can be explained as combinations of muscle synergies. In the study examining human postural maintenance [45
], it was shown that the long latency reflexes observed following phasic perturbations to the limb could be well explained by a few coordination patterns, or muscle synergies. Moreover, similar patterns were observed irrespective of whether working in a stiff or compliant environment. This was an unexpected result since previous work suggested that there should be an increased involvement of multiarticular muscles in the compliant environment. Another notable study examined the nocifensive reflexes in the spinalized frog [48
]. This work was especially compelling since it did not rely on computational analyses but on more direct observations of muscle activations evoked in response to phasic stimulation of muscle afferents. The study demonstrated that such stimulation caused collective modulation of the amplitude and timing of muscles within a single putative muscle synergy, while leaving the muscles in other synergies unaffected. Importantly, this modulation did not alter the relative timings of the muscles within that synergy, suggesting that synergies produced by spinal circuitry in the frog were synchronous as opposed to time-varying (see ). Although the use of perturbation analyses in these studies diminishes concerns about task constraints by increasing the richness of behavioral conditions, such concerns are not entirely alleviated; it is not clear a priori at what point a task is ‘complex enough’ so that the muscle synergy based explanation is surprising enough to be confirmed.
Indeed, two recent studies [40
] have provided evidence arguing against the existence of muscle synergies. Both of these studies are based on analyses of variability in motor patterns. One recent trend in studies in motor control has been a re-examination of noise and variability in task performance [49
]. Rather than treat this variability as reflecting ‘errors’ due to poor planning or control, these more recent studies consider this variability as reflecting efficient control, with the CNS only correcting for variability which prevents the accomplishment of task goals. Variability which does not affect the task can be allowed without penalty since attempts to correct such task irrelevant variability would be an unnecessary waste of effort. This hypothesis, referred to as the ‘uncontrolled manifold’ [51
] or ‘minimum intervention’ hypothesis [42
] and closely related to optimal feedback control [49
], in some ways stands in contrast to the muscle synergy hypothesis. In the uncontrolled manifold hypothesis, the problem for the CNS is not in reducing the degrees of freedom, but in identifying those degrees of freedom which are task relevant and those which are not. Having excess degrees of freedom implies that the CNS is more likely to be able to use degrees of freedom which align well with the task demands than if the degrees of freedom were restricted: i.e. redundancy allows for flexibility. Although in many cases, research on the uncontrolled manifold hypothesis invokes structures which are identical to muscle synergies [53
] (referred to as ‘muscle-modes’ or ‘m-modes’ in that work), these structures do not seem not essential to their main hypothesis that the CNS controls only task relevant perturbations. Note also that in the uncontrolled manifold work, the term ‘synergy’ is used to refer to the flexible control of execution variables to regulate task relevant variability, rather the grouping of muscle activations as described in [50
One recent study examined this minimum intervention hypothesis directly at the level of individual muscles and compared it to the muscle synergy hypothesis [42
]. This study examined the structure of the within trial variability of finger motor control in humans during a force regulation task. The elegance of this study is that the experimenters were able to record from nearly every muscle which contributed to index finger force, thereby characterizing an accurate mapping between muscle activation and task performance. In support of the minimum intervention hypothesis, they demonstrated that people allowed for more variability in task irrelevant dimensions than in task relevant dimensions, providing a clear demonstration of the minimum intervention principle at the level of physiological variables. Further, they demonstrated using either PCA or ICA that it was unlikely that the muscle coordination patterns could be well explained as muscle synergies. Although the authors allowed for a possible role for muscle synergies in planning vs. in execution of movements, their results suggest strongly that the CNS can control online individual degrees of freedom (i.e. muscles) as necessary in order to achieve task goals.
Another recent paper also examined the variability in human finger control during force production tasks [40
]. Using a clever analysis, they effectively demonstrated that the patterns of variability observed during this task were best explained as reflecting the control of individual muscles, rather than muscle synergies. This was shown both experimentally and in computational analyses. Both of these studies provide strong challenges to the muscle synergy hypothesis as an explanation for the neural control of these tasks.