  Active inference is emerging as a possible unifying theory of perception and
action in cognitive and computational neuroscience. On this theory, perception
is a process of inferring the causes of sensory data by minimising the error
between actual sensations and those predicted by an inner \emph{generative}
(probabilistic) model. Action on the other hand is drawn as a process that
modifies the world such that the consequent sensory input meets expectations
encoded in the same internal model. These two processes, inferring properties
of the world and inferring actions needed to meet expectations, close the
sensory/motor loop and suggest a deep symmetry between action and perception.
In this work we present a simple agent-based model inspired by this new theory
that offers insights on some of its central ideas. Previous implementations of
active inference have typically examined a "perception-oriented" view of this
theory, assuming that agents are endowed with a detailed generative model of
their surrounding environment. In contrast, we present an "action-oriented"
solution showing how adaptive behaviour can emerge even when agents operate
with a simple model which bears little resemblance to their environment. We
examine how various parameters of this formulation allow phototaxis and present
an example of a different, "pathological" behaviour.
