In conventional schemes, the intrinsic frame of reference contains the causes (changes in muscle length), while the consequences (changes in limb position) are in extrinsic coordinates. Active inference turns this on its head and regards prior beliefs PI3K inhibitor that cause movement to exist in an extrinsic frame, while the consequences unfold in intrinsic coordinates. In what sense are these perspectives equivalent? Intuitively, one can either regard a limb as being pulled by a muscle or the muscle as being pushed by the limb. However, from the point of view of hidden states

(muscle length and limb position), the two scenarios are identical. In other words, the semantics of push versus pull are purely heuristic; the underlying trajectories (in both frames of reference) are simply solutions to the appropriate Euler-Lagrange equations of motion. In active inference, movements caused by changes

in muscle length are modeled as movements that cause changes in muscle length; cf. the Passive Motion Paradigm (Mussa Ivaldi et al., 1988). Intuitively, this makes sense in that we are aware of movements, not muscles. Can every movement specified by a cost function also be specified by a prior belief? An equivalence between cost functions and prior beliefs can be established by appealing to the complete class theorem (Brown, 1981 and Robert, 1992). This Veliparib states that any behavior is Bayes optimal for at least one prior belief and cost function. However, this pair is not necessarily unique, which means that one can exchange prior beliefs and cost functions to produce

the same motor behavior. This is exploited in active inference to provide a biologically plausible Parvulin solution to the motor control problem that can be regarded as a predictive coding with motor reflexes. This scheme can also be regarded as an instance of the equilibrium point hypothesis (Feldman and Levin, 1995), in which fixed points are replaced by trajectories that are specified by prior beliefs about motion. In active inference, these are actually empirical priors that are continuously updated during the perceptual inversion of hierarchical generative models. In this setting, the optimal trajectory is just the movement that has the greatest posterior probability, given the current context. See Figure 4. The duality between optimal control and estimation has been clearly articulated by Todorov (2008) and dates back to the inception of Kalman filtering. This equivalence was exploited by early proposals to replace cost with an auxiliary random variable conditioned on a desired observation. This means that minimizing cost is equivalent to maximizing the likelihood of desired observations (Cooper, 1988, Pearl, 1988 and Shachter, 1988). Subsequent work focused on efficient methods to solve the ensuing inference problem (Jensen et al., 1994 and Zhang, 1998).