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.

衡稳态理论与主动推理框架相结合，通过将预测误差与生物激素动力学相耦合，提出了一种计算高效的长期调节模型，能够为体外智能系统提供适应性调节和生理学调控。

惊喜！在主动推断框架下使用生理应激进行适应调节 [预印本]

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

The active inference framework (AIF) is a promising new computational
framework grounded in contemporary neuroscience that can produce human-like
behavior through reward-based learning. In this study, we test the ability for
the AIF to capture the role of anticipation in the visual guidance of action in
humans through the systematic investigation of a visual-motor task that has
been well-explored -- that of intercepting a target moving over a ground plane.
Previous research demonstrated that humans performing this task resorted to
anticipatory changes in speed intended to compensate for semi-predictable
changes in target speed later in the approach. To capture this behavior, our
proposed "neural" AIF agent uses artificial neural networks to select actions
on the basis of a very short term prediction of the information about the task
environment that these actions would reveal along with a long-term estimate of
the resulting cumulative expected free energy. Systematic variation revealed
that anticipatory behavior emerged only when required by limitations on the
agent's movement capabilities, and only when the agent was able to estimate
accumulated free energy over sufficiently long durations into the future. In
addition, we present a novel formulation of the prior function that maps a
multi-dimensional world-state to a uni-dimensional distribution of free-energy.
Together, these results demonstrate the use of AIF as a plausible model of
anticipatory visually guided behavior in humans.

本研究通过系统性探索视觉 - 运动任务的一种方法，测试了主动推理框架的能力，该框架基于当代的神经科学，并通过基于奖励的学习产生像人类一样的行为。我们提出的神经主动推理代理模型使用人工神经网络来选择动作，以便根据它们将揭示的任务环境信息的非常短期预测和预期自由能的长期估计来选择动作，在代理人的运动能力受限时才出现预期的行为，并且只有当代理人能够足够长时间地预测未来的累积自由能时，预期行为才会出现，通过这些结果证明了 AIF 作为人类预期视觉引导行为的可信模型。