The concept of free energy has its origins in 19th century thermodynamics, but has recently found its way into the behavioral and neural sciences, where it has been promoted for its wide applicability and has even been suggested as a fundamental principle of understanding intelligent behavior and brain function. We argue that there are essentially two different notions of free energy in current models of intelligent agency, that can both be considered as applications of Bayesian inference to the problem of action selection: one that appears when trading off accuracy and uncertainty based on a general maximum entropy principle, and one that formulates action selection in terms of minimizing an error measure that quantifies deviations of beliefs and policies from given reference models. The first approach provides a normative rule for action selection in the face of model uncertainty or when information processing capabilities are limited. The second approach directly aims to formulate the action selection problem as an inference problem in the context of Bayesian brain theories, also known as Active Inference in the literature. We elucidate the main ideas and discuss critical technical and conceptual issues revolving around these two notions of free energy that both claim to apply at all levels of decision-making, from the high-level deliberation of reasoning down to the low-level information processing of perception.
翻译:自由能源的概念起源于19世纪的热力学,但最近又进入了行为和神经科学,因为其广泛适用性被提倡,甚至被建议为理解智能行为和大脑功能的一项基本原则。我们认为,目前在智能机构模式中,自由能源的概念基本上有两种不同的概念,这两种概念都可被视为是巴耶斯人对行动选择问题的推论的适用:一种是在基于普遍最大恒温原则交换准确性和不确定性时出现的,另一种是在尽量减少错误措施方面提出行动选择,这种错误措施可以量化信仰和政策与特定参考模式的偏差。第一种办法是在面临模式不确定性或信息处理能力有限的情况下为选择行动提供规范性规则。第二种办法直接旨在将行动选择问题作为巴耶斯人大脑理论的一个推论问题,也称为文献中的积极推论。我们阐述了主要概念,并讨论了围绕这两种自由能源概念进行的关键技术和概念展开讨论,这两种概念都主张在决策的各个层次上适用,从高层次分析推论到低层次推论。