Active inference unifies intentional and conflict-resolution imperatives of motor control
Fig 3
Mathematical description of generative model, i.e. the internal model the agent holds of the system and its dynamics, for the 1D model shown in Fig 1.
The agent represents its own state via the internal state vector that describes the system in generalized coordinates at the second order (Eq M.1). The forward model, gμ(μθ), describes how the agent forms an estimate of the expected sensory input based on the inferred system state, μs (Eq M.2). The model of the system dynamics allows the agent to predict the temporal evolution of its own state. In particular, the agent in our model entails a representation of reaching actions as instantiations of a desired state, , which acts as an attractor. The dynamics is then assumed to follow that of a damped oscillator (Eq M.3), with K representing the elastic constant controlling the attraction strength, ϕ the viscosity constant of the damping, and marm the mass of the agent forearm. Note that the model in Eq M.3 describes also states in which the agent does not intend to move; in this case the desired state is to be set to the current state, i.e.
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