Table 1.

Applicability and compatibility of Feasibility Theory with dominant theories of neuromuscular control.

Dimensionality Reduction	PCA, NMF, etc. describe the general shape and structure of the feasible activation space. The resulting basis functions serve as an approximation of the input-output relationship of the system (i.e., descriptive synergies).
Motor Primitives / Synergies / Modular Organization	If the basis functions mentioned above are of neural origin, they would be the means by which the nervous system inhabits the feasible activation space and executes valid solutions (i.e., prescriptive synergies).
Uncontrolled Manifold (UCM) Theory	The UCM Theory emphasizes that the temporal evolution of muscle activation patterns in the interior of the feasible activation space need not be as tightly controlled as those at its boundaries. This is because moving between interior points has no impact on the output as they constitute the null-space of the task (i.e., they are “goal-equivalent” as in Scholz and Schöner, 1999). In contrast, Feasibility Theory describes details of the structure of the feasible activation space.
Exploration-Exploitation	Heuristic and trial-and-error approaches can be used to find points within the Feasible Activation Space because it is a needle-in-a-haystack problem. By definition, there is a small likelihood of finding a point on a low-dimensional manifold embedded in a high-dimensional space (e.g., the volume of a line is zero). Thus, the families of valid solutions found are preferentially adopted (e.g., as motor habits De Rugy et al., 2012). Such a heavily iterative approach is compatible with reinforcement learning (Valero-Cuevas et al., 2009a), motor babbling (Touwen, 1976), the hundreds of thousands of steps children take when learning to walk (Adolph et al., 2012), or the mass practice a patient needs for effective rehabilitation (Lang et al., 2009).
Probabilistic Neuromuscular Control	If muscle activation patterns within the feasible activation space can be found (by any means), they can be combined to build probability density functions (i.e., Bayesian priors). A likely valid action for a particular situation can then be selected via Bayes' Theorem (e.g., Körding and Wolpert, 2004).
Optimization / Minimal Intervention Principle/ Optimal Control	Every point in the feasible activation space is, by definition, valid. However, if a cost function is used to evaluate each point in it, the feasible activation space is transformed into a fitness landscape. Optimization methods can then navigate this fitness landscape to find local and global minima (e.g., Crowninshield and Brand, 1981; Anderson and Pandy, 2001; Todorov and Jordan, 2002).