# Surprise! Using Physiological Stress for Allostatic Regulation Under the Active Inference Framework [Pre-Print]

arXiv: https://arxiv.org/abs/2406.08471 · HTML: https://mlsanity.com/paper/2406.08471/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 31 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)

## Built on by (most cited first, 2 of 2)

- [Human-Robot Mutual Learning through Affective-Linguistic Interaction and Differential Outcomes Training [Pre-Print]](https://mlsanity.com/paper/2407.01280/) (2024)
- [[Social] Allostasis: Or, How I Learned To Stop Worrying and Love The Noise](https://mlsanity.com/paper/2508.12791/) (2025)

## Abstract (arXiv)

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.