  In psychology and neuroscience it is common to describe cognitive systems as
input/output devices where perceptual and motor functions are implemented in a
purely feedforward, open-loop fashion. On this view, perception and action are
often seen as encapsulated modules with limited interaction between them. While
embodied and enactive approaches to cognitive science have challenged the
idealisation of the brain as an input/output device, we argue that even the
more recent attempts to model systems using closed-loop architectures still
heavily rely on a strong separation between motor and perceptual functions.
Previously, we have suggested that the mainstream notion of modularity strongly
resonates with the separation principle of control theory. In this work we
present a minimal model of a sensorimotor loop implementing an architecture
based on the separation principle. We link this to popular formulations of
perception and action in the cognitive sciences, and show its limitations when,
for instance, external forces are not modelled by an agent. These forces can be
seen as variables that an agent cannot directly control, i.e., a perturbation
from the environment or an interference caused by other agents. As an
alternative approach inspired by embodied cognitive science, we then propose a
nonmodular architecture based on the active inference framework. We demonstrate
the robustness of this architecture to unknown external inputs and show that
the mechanism with which this is achieved in linear models is equivalent to
integral control.
