  Allostasis proposes that long-term viability of a living system is achieved
through anticipatory adjustments of its physiology and behaviour: emphasising
physiological and affective stress as an adaptive state of adaptation that
minimizes long-term prediction errors. More recently, the active inference
framework (AIF) has also sought to explain action and long-term adaptation
through the minimization of future errors (free energy), through the learning
of statistical contingencies of the world, offering a formalism for allostatic
regulation. We suggest that framing prediction errors through the lens of
biological hormonal dynamics proposed by allostasis offers a way to integrate
these two models together in a biologically-plausible manner. In this paper, we
describe our initial work in developing a model that grounds prediction errors
(surprisal) into the secretion of a physiological stress hormone (cortisol)
acting as an adaptive, allostatic mediator on a homeostatically-controlled
physiology. We evaluate this using a computational model in simulations using
an active inference agent endowed with an artificial physiology, regulated
through homeostatic and allostatic control in a stochastic environment. Our
results find that allostatic functions of cortisol (stress), secreted as a
function of prediction errors, provide adaptive advantages to the agent's
long-term physiological regulation. We argue that the coupling of
information-theoretic prediction errors to low-level, biological hormonal
dynamics of stress can provide a computationally efficient model to long-term
regulation for embodied intelligent systems.
