  Active inference is an ambitious theory that treats perception, inference and
action selection of autonomous agents under the heading of a single principle.
It suggests biologically plausible explanations for many cognitive phenomena,
including consciousness. In active inference, action selection is driven by an
objective function that evaluates possible future actions with respect to
current, inferred beliefs about the world. Active inference at its core is
independent from extrinsic rewards, resulting in a high level of robustness
across e.g.\ different environments or agent morphologies. In the literature,
paradigms that share this independence have been summarised under the notion of
intrinsic motivations. In general and in contrast to active inference, these
models of motivation come without a commitment to particular inference and
action selection mechanisms. In this article, we study if the inference and
action selection machinery of active inference can also be used by alternatives
to the originally included intrinsic motivation. The perception-action loop
explicitly relates inference and action selection to the environment and agent
memory, and is consequently used as foundation for our analysis. We reconstruct
the active inference approach, locate the original formulation within, and show
how alternative intrinsic motivations can be used while keeping many of the
original features intact. Furthermore, we illustrate the connection to
universal reinforcement learning by means of our formalism. Active inference
research may profit from comparisons of the dynamics induced by alternative
intrinsic motivations. Research on intrinsic motivations may profit from an
additional way to implement intrinsically motivated agents that also share the
biological plausibility of active inference.
