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A tale of two densities: active inference is enactive inference

机译：两个密度的故事：有效推论是制约的

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The aim of this article is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) - and its corollary, active inference - in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature, because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain - variously known as predictive processing, predictive coding or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference - what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control - the self-organising, belief-guided selection of action policies - and do not have the properties ascribed by structural representationalists.
机译：本文的目的是澄清如何最好地解释一些核心自由能原理（FEP）的中央构建体（FEP） - 以及其推论，积极推理 - 在理论神经科学和生物学中：即，生成模型和变形的作用密度在这个理论中发挥作用。我们认为这些构建体在文献中被系统地歪曲，因为FEP与活跃推断之间的混合，一方面，截然不同（尽管密切相关）贝叶斯配方，以大脑为中心 - 各种称为预测处理，预测编码或预测误差最小化框架。更具体地说，我们研究了这些模型的两个对比解释：结构表现主义解释和有性解释。我们认为，对生成和识别模式的结构代表性的解释并没有对这些构建在FEP下的积极推断中发挥作用的作用。我们提出了对积极推理的制作解释 - 可能被称为施加推理。在FEP下的积极推断中，生成和识别模型是最佳的推论和控制的最佳转换 - 自组织，信仰引导的行动政策选择 - 并且没有结构代表员的特性。

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